Patent Grant
RE48527
1. Field of the Invention
The present invention relates to a control system for controlling the direction of travel of a vehicle, and in particular to a control system having an embedded spatial database using optical tracking for vehicle control and guidance. The control system of the present invention may also be used to control other aspects of a vehicle's motion, such as speed or acceleration. Furthermore, in the case of agricultural vehicles and the like, the present control system may be used to control yet other aspects of the vehicle's operation, such as the application of agricultural chemicals at desired locations (including at desired application rates), or the engagement and/or mode of operation of agricultural implements (e.g., plows, harvesters, etc.) at desired locations, etc.
For convenience, the invention will be described mainly with reference to agricultural vehicles and moving agricultural machinery. However, it will be clearly understood that the invention is not limited to agricultural applications and it may equally be applied to vehicles and other moving machinery in other areas.
2. Description of the Related Art
Automatic control of steering (“autosteering”) of vehicles is becoming more widespread, especially in agricultural and mining applications. Most commercially available automatic steering systems include a controller that has means for determining, among other things, the position and heading of a vehicle, a computer-based system for comparing the position and heading of the vehicle with a desired position and heading, and a steering control responsive to a control signal issued by the controller when the position and/or heading of the vehicle deviates from the desired position and/or heading.
A number of control systems have previously been devised for controlling the steering of agricultural vehicles. These systems are generally used on vehicles such as tractors (including tractors with towed tools or other implements), harvesters, headers and the like which operate in large fields. These vehicles generally move along predetermined trajectories (“paths”) throughout the field. In general, a wayline is entered into the control system and subsequent paths are calculated based on the wayline. If the vehicle deviates from the path as it moves, the controller causes the vehicle to steer back towards and onto the path as described below.
As the vehicle moves along the predetermined path trajectory, it uses various means such as signals produced by GPS (global positioning system) or INS (inertial navigation system) to identify if the vehicle deviates from the desired path trajectory. If the vehicle deviates, the extent of the deviation (i.e. the difference between the actual curvature of the vehicle's trajectory and the desired curvature, its actual compass heading compared with the desired compass heading, and the distance the vehicle is displaced laterally from the desired path) is expressed in the form of an error, and this error is fed back into the control system and used to steer the vehicle back onto the desired path.
A problem with previous vehicle control systems is that they are inherently “one-dimensional” or “linear” in nature. This means that, at a fundamental level, the controller operates by “knowing” the path that the vehicle is required to traverse, and “knowing” where the vehicle is located on that path (i.e. how far along the path the vehicle has moved) at a given time. However, the controller does not “know” where the vehicle is actually located in space. This is despite the fact that the controller may often progressively receive information containing the vehicle's spatial location, for example from the GPS/INS signals. In current controllers, the GPS/INS signals are used primarily to determine when the vehicle deviates from the path (i.e. to calculate the error) rather than for the primary purpose of determining the vehicle's actual position in space. Hence, at a fundamental level, the controller only “knows” the geometry of the path and how far the vehicle has moved along the path.
Therefore, with current controllers, if it is desired to know the actual spatial position of the vehicle, this must be calculated from the known geometry of the path and the known distance the vehicle has moved along that path. This calculation can be computationally expensive and difficult to implement in practice, particularly for curved, piecewise, broken or other complex path trajectories.
By way of example, it will be appreciated that one form of common path trajectory that agricultural vehicles are often required to traverse in fields is made up of a number of (usually parallel) path segments or “swaths” (these are sometimes also referred to as “rows”). Thus, the vehicle typically moves along one swath, harvesting or plowing as it goes, and it then turns around and moves back along an adjacent parallel swath, harvesting or plowing in the opposite direction. The adjacent swath will generally be spaced from the first swath sufficiently closely that no part of the field or crop is missed between the swaths, but also sufficiently apart so that there is not an unnecessary overlap region (i.e. a region between the swaths that gets plowed or harvested on both passes). In general, the distance between the mid-lines of each respective swath is determined with reference to the width of the vehicle (i.e. the width of the plow, harvester or possibly the tool being towed by the vehicle).
In cases where paths comprising a series of parallel swaths are used, the first swath will often be used as a reference swath or “wayline”. In general, the geometry of the wayline in space will be entered into the control system along with the vehicle or implement width, and this is used to calculate the required spacing (and hence trajectory) for each of the adjacent parallel swaths. However, with most existing control systems, the controller is only able to control the steering of the vehicle as it proceeds along each of the swaths. It is much harder to control the steering of the vehicle as it turns around between one swath and the next. Therefore, whilst the spatial geometry of the respective swaths may have been calculated, from the control system's point of view at any given time it only “knows” that it is on the nth swath (numbered from the wayline) and that it has been moving along that swath for a known amount of time with known speed (i.e. it knows that the vehicle is a certain distance along the nth swath). However, at a fundamental level, the control system does not inherently know where the vehicle is consequently located in space or the spatial relationship between each swath. A graphical representation of the difference between the vehicle's actual spatial location and what the control system “sees” is given in FIG. 113.
The “one-dimensional” or “linear” nature of existing control systems also causes other difficulties. One example is in relation to obstacle avoidance. In most agricultural applications, the positions of obstacles (e.g., fences, trees, immovable rocks, creeks, etc.) are known according to their “real-world” spatial location. The spatial location may be known according to global latitude and longitude coordinates (e.g., as provided by GPS), or alternatively the location may be known relative to a fixed point of known location (this is generally a point in or near the field used to define the origin of a coordinate system for the field). However, as current control systems only recognize where the vehicle is located along the path, not where the vehicle is actually located in space, the control system itself is therefore unable to recognize whether the location of the obstacle coincides with the trajectory of the path, and hence whether there may be a collision.
Consequently, with current control systems, it may be necessary for a number of separate modules to be provided, in addition to the primary control module, if automatic obstacle avoidance (i.e. obstacle avoidance without the need for intervention by the driver of the vehicle) is to be achieved. In these cases, one of the modules would be a collision detection module for calculating the geometry and trajectory of a section of the path a short distance ahead of the vehicle in terms of “real world” spatial coordinates and for determining whether any of the points along that section of path will coincide with the location of an obstacle. If the collision detection module identifies that the section of path is likely to pass through an obstacle (meaning that there would be a collision if the vehicle continued along that path), then a further module may be required to determine an alternative trajectory for (at least) the section of the path proximate the obstacle. Yet a further module may then be required to determine how best to steer the vehicle from the alternative trajectory back onto the original path after the vehicle has moved past the obstacle. This multi-modular control system structure is complicated and can lead to computational inefficiencies because the different modules may each perform many of the same geometric calculations for their own respective purposes, separately from one another, leading to “doubling up” and unnecessary computation. Also, with this modular control system structure, control of the vehicle generally passes from one module to another as described above, but determining when one module should take over from another creates significant difficulties in terms of both system implementation and maintenance.
Another problem associated with the “one-dimensional” nature of existing control systems is their inherent inflexibility and unadaptability. For example, in practice, if the vehicle deviates from the desired path for some reason, it may be preferable for subsequent paths (swaths) to also include a similarly shaped deviation so that the paths remain substantially parallel along their length (or tangentially parallel and consistently spaced in the case of curved sections of path). If the vehicle is, for example, a harvester or a plow, then keeping the paths parallel in this way may help to prevent portions of the field from being missed, or from being harvested/plowed multiple times (by passing over the same portion of field on multiple passes). Even with the modular control system structures described above, it is often difficult to determine the geometry of the deviated path portion in terms of “real world” coordinates, and even if this can be done, it is also difficult to adjust subsequent path geometries to correspond to the deviation from the predetermined path trajectory that was originally entered.
As a further example of the inherent inflexibility and unadaptability of current “one-dimensional” control systems, it is illustrative to consider the situation where an obstacle is located near the end of one swath such that it would be quicker and more efficient to simply move on to an adjacent swath located nearby rather than wasting time trying to go around the obstacle to finish the first swath before moving on to the adjacent swath. Current “one-dimensional” control systems are not able to recognize that it would be more efficient to move on. This is because the control system only knows where the vehicle is along its current path (e.g., close to the end of the swath), and if a modular control systems is used, that module may also recognize that it is approaching the obstacle. The control system does not know where the vehicle is actually located in space, and therefore it cannot recognize that the beginning of the next swath is actually located nearby—it simply does not know where the next swath is (or indeed where the current swath is in space). Therefore, current control systems cannot easily recognize when it would be better to change paths (at least without intervention from the vehicle's driver), as this example illustrates. Nor is the current “one-dimensional” structure inherently adapted to enable the control systems to automatically (i.e. autonomously without assistance from the driver) determine and guide the vehicle along an efficient trajectory between swaths.
As used herein, “attitude” generally refers to the heading or orientation (pitch with respect to the Y axis, roll with respect to the X axis, and yaw with respect to the Z axis) of the vehicle, or of an implement associated with the vehicle. Other vehicle/implement-related parameters of interest include groundspeed or velocity and position. Position can be defined absolutely in relation to a geo-reference system, or relatively in relation to a fixed position at a known location, such as a base station. A change in one or both of the position and orientation of the vehicle (which can include a towed component, such as an implement or a trailer) can be considered a change in the vehicle's “pose.” This includes changes (e.g., different order time derivatives) in attitude and/or position. Attitude and position are generally measured relatively with respect to a particular reference frame that is fixed relative to the area that the vehicle is operating in, or globally with respect to a geo-reference system.
U.S. Pat. No. 6,876,920, which is assigned to a common assignee herewith and incorporated herein by reference, describes a vehicle guidance apparatus for guiding a vehicle over a paddock or field along a number of paths, the paths being offset from each other by a predetermined distance. The vehicle guidance apparatus includes a GNSSGlobal navigation satellite system (GNSS) receiver for periodically receiving data regarding the vehicle's location, and an inertial relative location determining means for generating relative location data along a current path during time periods between receipt of vehicle position data from the GNSS receiver. The apparatus also includes data entry means to enable the entry by an operator of an initial path and a desired offset distance between the paths. Processing means are arranged to generate a continuous guidance signal indicative of errors in the attitude and position of the vehicle relative to one of the paths, the attitude and position being determined by combining corrected GNSS vehicle location data with the relative location data from the inertial relative location determining means.
In the system described in U.S. Pat. No. 6,876,920, the inertial sensor is used to provide a higher data rate than that obtainable from GNSS alone. Although the inertial navigation system (INS) part of the steering control system suffers from errors, in particular a yaw bias, the signals received from the GNSS system are used to correct these errors. Thus, the combination of a GNSS based system and a relatively inexpensive INS navigation system allow for quite accurate control of the position of the vehicle. Although this system allows for accurate vehicle positioning and sound control of the vehicle's steering, difficulties may be experienced if there are prolonged periods of GNSS outage. GNSS outages may occur due to unsuitable weather conditions, the vehicle operating in an area where GNSS signals cannot be accessed, or due to problems with the GNSS receiver. If a period of prolonged GNSS outage occurs, the steering system relies solely upon the INS. Unfortunately, a yaw bias in a relatively inexpensive inertial sensor used in the commercial embodiment of that steering control system can result in errors being introduced into the steering of the vehicle.
Optical computer mice are widely used to control the position of a cursor on a computer screen. Optical computer mice incorporate an optoelectronic sensor that takes successive pictures of the surface on which the mouse operates. Most optical computer mice use a light source to illuminate the surface that is being tracked (i.e. the surface over which the mouse is moving). Changes between one frame and the next are processed using the image processing ability of the chip that is embedded in the mouse. A digital correlation algorithm is used so that the movement of the mouse is translated into corresponding movement of the mouse cursor on the computer screen.
The optical movement sensors used in optical computer mice have high processing capabilities. A number of commercially available optical computer mice include optical mouse sensors that can process successive images of the surface over which the mouse is moving at speeds in excess of 1500 frames per second. The mouse has a small light emitting source that bounces light off the surface and onto a complementary metal oxide semiconductor (CMOS) sensor. The CMOS sensor sends each image to a digital signal processor (DSP) for analysis. The DSP is able to detect patterns in images and see how those patterns have moved since the previous image. Based on the change in patterns over a sequence of images, the digital signal processor determines how far the mouse has moved in X and Y directions, and sends these corresponding distances to the computer. The computer moves the cursor on the screen based upon the coordinates received from the mouse. This happens hundreds to thousands of times each second, making the cursor appear to move very smoothly.
The chips incorporated into optical computer mice often include photodetectors and an embedded integrated circuit that is used to analyse the digital signals received from the photodetectors. The photodetectors may include an array of photosensors, such as an array of charged couple devices (CCDs).
U.S. Pat. No. 5,786,804 (incorporated herein by reference), which is assigned to Hewlett-Packard Company, describes a method and system for tracking attitude of a device. The system includes fixing a two-dimensional (2D) array of photosensors to the device and using the array to form a reference frame and a sample frame of images. The fields of view of the sample and reference frames largely overlap, so that there are common image features from frame to frame. Several frames are correlated with the reference frame to detect differences in location of the common features. Based upon detection of correlations of features, an attitudinal signal indicative of pitch, yaw and/or roll is generated. The attitudinal signal is used to manipulate a screen cursor of a display system, such as a remote interactive video system.
It will be clearly appreciated that any reference herein to background material or a prior publication is not to be understood as an admission that any background material, prior publication or combination thereof forms part of the common general knowledge in the field, or is otherwise admissible prior art, whether in Australia or any other country.
In a first aspect, the present invention provides a control system for controlling movement of a vehicle characterized in that the control system includes an optical movement sensor which scans a surface over which the vehicle is moving and generates a signal indicative of relative movement along an axis of the vehicle and relative movement across an axis of the vehicle, said signal being provided to a controller.
In a second aspect, the present invention provides a control system for controlling movement of a vehicle comprising a controller having a computer memory for storing or generating a desired path of travel, the controller being adapted to receive position and/or heading signals from one or more sensors, the position and/or heading signals enabling the controller to determine a position and/or heading of the vehicle relative to a desired path of travel, the controller sending control signals to a steering control mechanism in response to the determined position and/or heading of the vehicle, wherein the position and/or heading signals from the one or more sensors include a signal generated by an optical movement sensor configured to scan a surface during travel of the vehicle, the optical movement sensor generating a signal indicative of relative movement along an axis of the vehicle and relative movement across an axis of the vehicle.
The surface that is scanned by the optical movement sensor is suitably a surface over which the vehicle is travelling. Suitably, the optical movement sensor scans a surface that is close to or under the vehicle during travel of the vehicle over the surface.
The optical movement sensor may comprise the operative part of an optical computer mouse. Therefore, in saying that the optical movement sensor “scans” the surface over which the vehicle moves, where the optical movement sensor comprises the operative part of an optical computer mouse, it will be understood that the optical movement sensor receives successive images of the surface over which the vehicle is moving. One part or other of the control system will then detect patterns in the images, and uses the change in the patterns between successive images to obtain information regarding the movement of the vehicle.
The optical movement sensor may comprise an illumination source and an illumination detector. The optical movement sensor may comprise an optical movement sensor integrated circuit.
As noted above, the optical movement sensor may comprise the operative part from an optical computer mouse. Alternatively, the optical movement sensor may be adapted from or derived from the operative part of an optical computer mouse. The optical movement sensor may use a light source to illuminate the surface that is being tracked (i.e. the surface over which the vehicle is moving).
Changes between one frame and the next may be processed by an image processing part of a chip embedded in the optical movement sensor and this may translate the movement across the surface of the optical movement sensor (which will generally be mounted to the vehicle) into movement along two axes. Alternatively, the image processing may be performed by processing means separate from the optical movement sensor. For example, the signals received by the optical movement sensor may be conveyed to a separate microprocessor with graphics processing capabilities for processing.
The optical movement sensor may include an optical movement sensing circuit that tracks movement in a fashion similar to the optical movement sensing circuits used to track movement in computer mice. The person skilled in the art will readily appreciate how such optical movement sensing circuits analyze data and provide signals indicative of movement of the sensor across the surface. For this reason, further discussion as to the actual algorithms used in the optical movement sensing circuits need not be provided. Suitably, the optical movement sensing circuit may comprise an optical movement sensing integrated circuit. Such optical movement sensing integrated circuits are readily available from a number of suppliers.
In some embodiments, the control system of the present invention may further comprise one or more inertial sensors for providing further signals regarding the vehicle's attitude and position (or changes thereto) to the controller. Accelerometers and rate gyroscopes are examples of inertial sensors that may be used. The inertial sensors may form part of or comprise an inertial navigation system (INS), a dynamic measurement unit (DMU), an inertial sensor assembly (ISA), or an attitude heading reference system (AHRS). These are well-known to persons skilled in the art and need not be described further. The inertial sensors may be used in conjunction with other navigation sensors, such as magnetometers, or vehicle based sensors such as steering angle sensors, or wheel speed encoders.
Inertial sensors, such as rate gyroscopes and accelerometers, can suffer from time varying errors that can propagate through to create errors in the vehicle's calculated attitude and/or position. These errors can be sufficiently acute that to prevent providing the controller with significantly inaccurate measures of the vehicle's attitude and/or position, it is preferable (and often necessary) for the control system to also receive signals regarding the vehicle's attitude and/or position (or changes thereto) from a source that is independent of the inertial sensors. These separate signals can be used to compensate for the errors in the inertial sensor signals using known signal processing techniques.
It is common to use GNSS signals (which provide information regarding the vehicle's location) to compensate for the errors in the inertial sensor signals. However, the present invention opens up the possibility of providing a control system that includes the optical movement sensor and one or an assembly of inertial sensors (and possibly including one or more other vehicle sensors as well). In other words, in some embodiments of the present invention, the signals provided by the optical movement sensor may be used to compensate for the errors in the inertial sensor signals instead of or in addition to the GNSS signals.
In embodiments such as those described in the previous paragraph, a single optical movement sensor may generally be sufficient to compensate for the errors in inertial sensors, such as accelerometers which measure rates of change in linear displacement. However, a single optical movement sensor may not be sufficient to compensate for errors in inertial sensors, such as gyroscopes which measure rates of change in angular displacement because the optical movement sensor will often be fixedly mounted to the vehicle such that the orientation of the optical movement sensor is fixed to, and changes with, the orientation of the vehicle.
The single optical movement sensor of the kind used in optical computer mice is able to detect and measure movement of the optical movement sensor along the X (roll) and Y (pitch) axes (in the present context this means the X (roll) and Y (pitch) axes of the vehicle because the optical movement sensor is fixed to the vehicle). However, this kind of optical movement sensor is not generally able to detect and measure rotation about the Z (yaw) axis. Consequently, if it is desired to compensate for the XYZ errors in inertial sensors such as gyroscopes using optical movement sensors that are fixedly mounted to the vehicle, two or more optical movement sensors will generally need to be provided and mounted at different locations on the vehicle.
Alternatively, a single optical movement sensor can be used to compensate for the errors in gyroscopes and the like which measure rates of change in rotational displacement if the optical movement sensor is not fixed with respect to the vehicle. Rather, the optical movement sensor could be mounted so that when the vehicle turned (i.e. rotated about its Z (yaw) axis), the orientation of the optical movement sensor would remain unchanged. In effect, even if the vehicle turns, the orientation of the optical movement sensor would remain unchanged, meaning that the optical movement sensor would effectively translate but not rotate with respect to the surface over which the vehicle is moving. A single optical movement sensor might thus be used to compensate for the errors in both accelerometers and gyroscopes, but some system or mechanism (e.g., gimbal-mounting) would need to be provided to maintain the constant orientation of the optical movement sensor.
The embodiments of the invention described above where the control system incorporates one or more inertial sensors, one or more optical movement sensors, and where the optical movement sensor(s) are used (instead of GNSS signals) to compensate for the errors in the inertial sensor(s) can generally be described as relative measurement control systems. This is because the optical movement sensor(s) and the inertial sensor(s) can only measure changes in vehicle attitude and/or position. They are unable to fix the geographic position and attitude of the vehicle in absolute “global” coordinates. References in this document to relative movement of the vehicle, or of an implement associated with the vehicle, or relative attitude/position/heading/pose information should be understood in this context.
However, the relative coordinate system established by relative measurement control systems such as those described above can relate to absolute geographic space if the vehicle can be moved sequentially to at least two, and preferably three or more, locations whose absolute geographic locations are known. This leads to the possibility of calibrating a control system having only optical, inertial, and possibly other vehicle sensors, in the total absence of GNSS. For example, during power up (initialization), the inertial navigation system positions of the vehicle could be arbitrarily set on a map whose origin and orientation is known. To relate this map to absolute geographic space, the vehicle could be located at the first known location, the internal coordinates noted, then moved to a second location and the new internal coordinates likewise noted. The line between the two points could be fitted from the internal map onto the real world map to arrive at the XY offset between the two map origins, the orientation difference between the two map origins, and the linear scaling difference between the two maps.
Thus, in one embodiment, the present invention may comprise a control system including one or more optical movement sensors and one or more inertial sensors. Suitably, the control system may include one or more optical movement sensors and an assembly of inertial sensors. In one embodiment, the control system of the present invention may further comprise an assembly of sensors including accelerometers and rate gyroscopes for providing further position and/or attitude signals to the controller. The assembly may comprise between one and three sensor sets orthogonally mounted, with each sensor set comprising not necessarily one of each, but no more than one of each of the above-mentioned sensors. Such inertial sensors are well known to persons skilled in the art and need not be described further.
In another embodiment, the present invention may comprise a control system including one or more optical movement sensors and one or more other sensors. The other sensors may comprise navigation sensors such as magnetometers, or vehicle sensors such as wheel speed encoders, and steering angle encoders. Control systems in accordance with this embodiment of the invention would also be described as relative measurement control systems, and the relative coordinate system established by such a system can relate to absolute geographic space in generally the same way as described above.
In yet another embodiment, the control system of the present invention, which incorporates one or more optical movement sensors, may be integrated with a GNSS system. In this system, the GNSS system provides absolute measurement in geographic space and the optical movement sensor provides relative movement data that can be used to control the vehicle during periods of outage of GNSS signals or during periods of normal operation when no GNSS signals are being received. Thus, in a further embodiment, the present invention provides a control system including one or more optical movement sensors and a GNSS system.
In a further still embodiment, the control system of the present invention may incorporate one or more optical movement sensors, a GNSS system and one or more inertial sensors, suitably an assembly of inertial sensors. In this embodiment, the optical movement sensor is configured to look at the ground near or under the vehicle. The output signal generated by the optical movement sensor comprises the relative movement along the axis of the vehicle and the relative movement across the axis of the vehicle. This information can be used as an additional source for compensating for the errors in the inertial sensors, giving a combined GNSS/INS/optical movement sensor system with the capability of operating over sustained periods of GNSS outage. Thus, in another embodiment, the present invention that may provide a control system including one or more optical movement sensors, a GNSS system and one or more inertial sensors, such as an assembly of inertial sensors.
GPS (global positioning system) is the name of the satellite-based navigation system originally developed by the United States Department of Defence. GNSS (including GPS and other satellite-based navigation systems) is now used in a wide range of applications. A number of systems also exist for increasing the accuracy of the location readings obtained using GNSS receivers. Some of these systems operate by taking supplementary readings from additional satellites and using these supplementary readings to “correct” the original GNSS location readings. These systems are commonly referred to as “Satellite Based Augmentation Systems” (SBAS) and some examples of SBASs are: the United States' “Wide Area Augmentation System” (WAAS); the European Space Agency's “European Geostationary Navigation Overlay Service” (EGNOS); and the Japanese “Multi-Functional Transportation Satellite” (MFTS).
A number of “Ground Based Augmentation Systems” (GBASs) also exist which help to increase the accuracy of GNSS location readings by taking additional readings from beacons located at known locations on the ground. It will be understood that, throughout this specification, all references to GNSS include GNSS when augmented by supplementary systems such as SBASs, GBASs and the like.
In embodiments of the present invention where the optical movement sensor is used in combination with one or more other sensors, the datastream from the optical movement sensor may be combined with a datastream from another sensor. This may be done using known signal processing techniques to obtain a stream of statistically optimal estimates of the vehicle's current position and/or attitude. Suitably, the signal processing techniques may utilize a statistically optimized filter or estimator. The optimal filter or estimator could usefully, but not necessarily, comprise a Kalman filter.
The optical sensor used in the control system in accordance with the present invention may comprise an optical movement sensing integrated circuit that receives raw data from a lens assembly mounted on a vehicle or on an implement towed by a vehicle. The lens assembly may be configured such that an image of the ground immediately below the lens assembly is formed on a photosensor plane of the optical movement sensing integrated chip by the lens assembly. Usefully, the lens may be a telecentric lens. Furthermore, the lens may be an object space telecentric lens. An object space telecentric lens is one that achieves dimensional and geometric invariance of images within a range of different distances from the lens and across the whole field of view. Telecentric lenses will be known to those skilled in the art and therefore need not be described any further.
The lens assembly may be chosen so that the extent of the image on the optical movement sensing integrated chip represents a physical extent in the object plane which is commensurate with both the anticipated maximum speed of the vehicle and the processing rate of the optical movement sensing integrated circuit. For example, if the maximum speed of the vehicle is 5 m per second and the desired overlap of successive images is 99%, an image representing 0.5 m in extent will require a processing speed of 1000 frames per second.
The optical movement sensor may include an illumination source of sufficient power such that the image of the ground beneath the vehicle is rendered with optimum contrast. This can be usefully, but not necessarily implemented as an array of high intensity light emitting diodes chosen to emit light at the wavelength of optimal intensity of the optical movement sensor.
Desirably, the optical movement sensor may be provided with a mechanism to keep the entrance pupil of the optical assembly free of dust. This could be usefully implemented by means of a high velocity air curtain passing the entrance pupil. Other mechanisms may be used, such as those that spray a cleaning fluid over the pupil. The cleaning fluid in those embodiments may comprise a cleaning liquid, such as water. Other means or mechanisms suitable for keeping the lens, or at least the entrance pupil of the optical assembly, free of dust will be known to those skilled in the art and may also be used with the present invention.
In another embodiment, the present invention provides a control system for controlling a position of an implement associated with a vehicle, characterised in that the control system includes an optical movement sensor which scans a surface over which the implement is moving and generates a signal indicative of relative movement along an axis of the implement and relative movement across an axis of the implement, said signal being provided to a controller.
In another aspect, the present invention provides a control system for maintaining a position and/or heading (attitude) of an implement close to a desired path of travel, the control system comprising a controller having a computer memory for storing or generating the desired path of travel, the controller being adapted to receive position and/or heading signals relating to a position and/or heading of the implement from one or more sensors, the position and/or heading signals enabling the controller to determine the position and/or heading of the implement relative to the desired path of travel, the controller sending control signals to a position and/or heading control mechanism in response to the determined position and/or heading, wherein the position and/or heading signals from the one or more sensors include a signal generated by an optical movement sensor configured to scan a surface over which the implement is travelling, the optical movement sensor generating a signal indicative of relative movement along an axis of the vehicle and relative movement across an axis of the vehicle.
Suitably, in this aspect, the optical movement sensor is mounted to the implement. The optical movement sensor may scan the surface close to the implement or underneath the implement as the implement traverses the surface.
In this aspect, the control algorithms and the position control mechanisms may be as described in U.S. Pat. No. 7,460,942, which is assigned to a common assignee herewith and incorporated herein by reference. In embodiments of this aspect of the invention, the position of the implement may be controlled by controlling the steering of the vehicle associated with the implement (this is especially useful if the implement is rigidly and fixedly connected to the vehicle), or by moving the position of the implement (or at least a working part of the implement) relative to the vehicle, which may be achieved by adjusting the lateral offset between the working part of the implement and the vehicle, or by using the working part of the implement to “steer” the implement.
In this aspect, the control system may further include one more of a GNSS system and inertial sensors and navigation sensors and vehicle based sensors. These various systems and sensors are described above with reference to other aspects of the invention.
It is a further objective of the present invention to provide a vehicle control system having an embedded spatial database that may at least partially ameliorate one or more of the above-mentioned difficulties, or which may provide a useful or commercial alternative to existing control systems.
Accordingly, in a first broad form the present invention resides in a vehicle control system having a controller and a spatial database adapted to provide spatial data to the controller at control speed.
In another broad form, the invention resides in a control system for controlling a vehicle within a region to be traversed, the control system comprising: a spatial database containing spatial data; a controller adapted to receive spatial data from the spatial database at control speed; the control system being adapted to receive spatial data from the controller and/or an external source; and the controller using the spatial data for controlling the vehicle.
In a further broad form, a control system is provided for steering a vehicle within a region to be traversed, the control system comprising: a spatial database containing spatial data; a controller adapted to receive spatial data from the spatial database at control speed; the controller being adapted to control the steering of the vehicle, the spatial database being adapted to receive updated spatial data from the controller and/or an external source; and the updated spatial data relating to the vehicle and/or an implement associated with and proximate the vehicle and/or at least a portion of the region proximate the vehicle.
In agricultural applications, the region to be traversed by the vehicle will generally be the field that is to be plowed, harvested, etc., and the invention will be described generally with reference to agricultural vehicles operating in fields. However, no limitation is meant in this regard, and the region to be traversed by the vehicle may take a range of other forms in different applications. For example, in automotive applications the region to be traversed by the vehicle might comprise roadways located in a particular geographical area. Alternatively, in mining applications the region could comprise the vehicle navigable regions of the mine. In underground mining, this could include the various levels of the mine located vertically above and below one another at different relative levels (depths). Furthermore, the control system of the present invention could be applied to vehicles that operate on airport tarmacs, in which case the region to be traversed by the vehicle might be the tarmac, or a portion thereof. From these examples, the person skilled in the art will appreciate the breadth of other applications that are possible.
The control system of the present invention includes a spatial database that contains spatial data. The spatial database may also be adapted to receive spatial data including updated spatial data, and to provide spatial data to other components of the control system. In general, data may be characterized as “spatial” if it has some relationship or association with “real world” geographical location, or if it is stored somehow with reference to geographical location. Some illustrative examples of the kinds of spatial data that may be stored within the database include (but are not limited to) coordinate points describing the location of an object (e.g., a rock or tree) in terms of the object's “real world” geographical location in a field, the coordinate points for a geographical location itself, information regarding a “state” of the vehicle (e.g., its speed, “pose” (position and orientation) or even fuel level) at a particular geographical location, a time when the vehicle was at a particular geographical location, or a command to the vehicle to change its trajectory or mode of equipment (e.g., plow) operation if or when it reaches a certain geographical location. These examples illustrate that any data or information that has an association with geographical location, or which is stored with reference to geographical location, can constitute “spatial data”. For the remainder of this specification, the terms “spatial data” and “spatial information” will be used interchangeably. References simply to “data” or “information” will generally also carry a similar meaning, and references simply to the “database” will be to the spatial database, unless the context requires otherwise. Typically, the spatial database is an electronic database stored in a memory device, such as, for example, a RAM, as discussed in more detail below.
Spatial data may be stored within the database according to any convenient coordinate system, including (but not limited to) cartesian (or projected) coordinates, polar coordinates, cylindrical coordinates, spherical coordinates, latitude/longitude/altitude etc. The coordinate system may also be “global” in the sense of the location references provided by GPS, or “local” coordinates such as those defined with respect to a local origin and reference orientation. The coordinates may or may not take into account the curvature caused by the Earth's overall spherical shape. Hence, there is no limitation as to the coordinate system that may be used with the present invention, although it is envisaged that Cartesian (x,y or x,y,z) coordinates or latitude/longitude/altitude will be used most frequently because of the way these inherently lend themselves to describing geographical location, and because of the ease with which these coordinate systems can be implemented digitally. Particularly representative embodiments may utilize the WGS84World Geodetic System 1984 datum which is consistent with the current GPS.
Those skilled in the art will know that GPS (global positioning system) is the name of the satellite based navigation system originally developed by the United States Department of Defense. GPS is now used in a wide range of applications. A number of systems also exist for increasing the accuracy of the location readings obtained using GPS receivers. Some of these systems operated by taking supplementary readings from additional satellites and using these supplementary readings to “correct” the original GPS location readings. These systems are commonly referred to as “Satellite Based Augmentation Systems” (SBAS) and some examples of SBASs are: the United States' “Wide Area Augmentation System” (WAAS); the European Space Agency's “European Geostationary Navigation Overlay Service” (EGNOS); and the Japanese' “Multi-Functional Transportation Satellite” (MFTS).
A number of “Ground Based Augmentation Systems” (GBASs) also exist which help to increase the accuracy of GPS location readings by taking additional readings from beacons located at known locations on the ground. It will be understood that, throughout this specification, all references to GPS include GPS when augmented by supplementary systems such as SBASs, GBASs and the like.
It is explained above that the controller (which controls the vehicle) receives spatial data from the spatial database. In this way, the data received by the controller from the database forms at least part of the control inputs that the controller operates on to control the vehicle (i.e. the spatial data forms at least part of the inputs that drive the controller). The fact that the controller operates directly on information that is inherently associated with “real world” geographic location represents a change in thinking compared with existing vehicle control systems. In particular, it means that the control system of the present invention “thinks” directly in terms of spatial location. Put another way, in the control system of the present invention, control parameters are defined in geographic space rather than the space of an abstract vector. Consequently, the controller of the present invention may be considered to be inherently “multi-dimensional” or “spatial” in nature, as opposed to “one-dimensional” or “linear” like the existing control systems described in the background section above.
It is envisaged that at least some (and probably most) of the components of the control system, including the controller, will typically be implemented using commercially available equipment and a generally conventional control architecture. For instance, the controller may be implemented using equipment that provides memory and a central processing unit to run the one or more algorithms required to control the vehicle. Likewise, the controller (and hence the control algorithm(s)) used in the present invention may take any form suitable for controlling the steering of a vehicle. Typically, closed loop or feedback type control will be used at least in relation to some signal streams (i.e. in relation to at least some of the vehicle variables being controlled by the controller). However, open loop control may also be used, as may feed-forward control structures wherein the spatial data received by the controller from the spatial database is fed forward to form part of the control outputs used to control the vehicle. Where feedback type control is used, the control structure may incorporate combinations of proportional, integral and differential control, or a series of such (possibly nested) control loops. However, no particular limitation is meant in this regard and the person skilled in the art will appreciate that any form of suitable control and/or controller may be used.
The control system may also incorporate conventional signal processing and transmitting equipment, for example, for suitably filtering incoming spatial data signals, and for transmitting control signals from the controller to the vehicle's steering system to steer the vehicle. The person skilled in the art will appreciate that any suitable electric, mechanical, pneumatic or hydraulic actuators, or combinations thereof, may be used with the present invention. The actuators may be linked with the vehicle's steering and drive systems to control the steering, acceleration, deceleration, etc. of the vehicle in response to control signals produced by the controller. Associated equipment such as amplifiers and power sources may also be provided as required to amplify the control signals, and to power the actuators. A wide range of power sources may be used including batteries, generators, pumps, etc., depending on the nature of the actuator(s) and the signals to be amplified.
Whilst the present control system may operate using a conventional form of controller and using at least some commercially available equipment, the spatial database used to store the spatial data and to provide the spatial data to the controller may be different to other forms of databases used in other areas. In other areas (including non-control related applications such as those where data storage is the principal objective), databases often contain the vast amounts of information (in this case “information” is not used in its “spatial” sense) and the information is generally stored in complex hierarchical structures. Conceptually, these databases may be considered to be “multi-levelled” in that an initial query may return only relatively superficial level information, but this may in turn allow the user to interrogate the database more deeply to obtain more specific, linked or related information. This complex structure means that these kinds of databases can take considerable time (many seconds, minutes or even longer) to generate the appropriate output in response to a query. Those skilled in the art will appreciate that databases such as these, which take a relatively long time to return information in response to a query, may not be suitable for use in control systems such as the present which require low latencies between variable inputs and control outputs to thereby enable real-time control to be provided.
The spatial database used in the present invention will suitably be adapted to provide the data to the controller at control speed. In this sense, “control speed” means that the database is able to provide the information at a rate of the same order as the speed at which the controller repeats successive cycles of the control algorithm (i.e. at a rate of the same order as the “clock speed” of the controller). Ideally, the database will be adapted to provide the data to the controller, and perhaps also receive data from the controller and/or external sources, at every successive cycle of the control algorithm (i.e. at the controller's clock speed). However, in some embodiments it may be sufficient for the database to be adapted to provide (and perhaps receive) data at less than, but close to, the controller's clock speed (for example, at every second or third successive cycle of the control algorithm), provided that the rate is fast enough to provide the controller with sufficiently up-to-date spatial information to achieve adequate vehicle control performance. In cases where the controller operates at different clock speeds for different data signal streams, the database may be adapted to provide data at a rate of the same order as one of those controller clock speeds. In any event, the database should provide data to the controller at a rate commensurate with the control loop bandwidth.
In practice, it is envisaged that the database may be adapted to provide data to the controller at a rate of between 1 Hz and 100 Hz. Given the speeds that vehicles such as agricultural vehicles typically move at (generally less than 60 km/hr or 37.3 miles/hr), rates between 1 Hz and 20 Hz will almost always be sufficient, and even rates between 3 Hz and 12 Hz may be sufficient for vehicles moving at significantly less than 60 km/hr. Nevertheless, those skilled in the art will recognize that the necessary or achievable rates may vary depending on the level of control precision and performance required in different applications, the speed at which the vehicle in question moves, and the capabilities of the available equipment used to implement the control system.
Those skilled in the art will appreciate that because the spatial database used in the present invention can provide spatial data to the controller at control speed, and therefore forms part of the system's overall configuration, the spatial database may be considered to be “embedded” within the control system, rather than external to it. This is particularly so in embodiments where feedback type control is used, and the spatial database forms part of the system's overall closed loop structure (i.e. in embodiments where the spatial database forms part of the loop).
In order for the database to be able to provide (and, if desired, also receive) data at the required rates, the form of the database should allow the required rapid database access and response times. Ideally, the database and all of the data that it contains will be loaded into the control system's memory (i.e. loaded into RAM). This way, the data will be directly accessible by the controller's CPU (central processing unit), rather than requiring a query to be sent to a remote disk or storage device containing the data, the response to which would then need to be loaded into RAM before being accessible by the CPU. However, it is possible that the database could be located on a separate disk or other storage device, particularly if the device is capable of retrieving data in response to a query with sufficient speed such as, for example, a disk device with RAM read/write cache.
It is envisaged that the amount of memory required to store the spatial data relating to a particular field to be traversed by the vehicle may be in the order of megabytes. By way of example (given for illustrative purposes only), consider a straight wayline that is 1 km long and which has 500 parallel swaths of corresponding length. If the database is designed to incorporate information pertaining to locations every 2 m along each of the 500 swaths, this corresponds to 501.times.500=250,500 locations. When the data is structured within the database in the manner described further below, this may correspond to approximately 4 MB of memory required to store the coordinates of each point. However, it is also envisaged that as the nature and complexity of the data required to be stored in the database increases, the required amount of memory may increase to hundreds of megabytes or gigabytes. Devices which provide this amount of memory are (or are at least becoming) commercially available.
The speed of the database may be assisted by the way in which the data is arranged (i.e. stored) within the database. A wide range of methods and algorithms are known for arranging data (i.e. for assigning appropriate “indices” and the corresponding memory allocations to individual items of data) within databases, and the particular method chosen depends on the nature of the data, and the way and speed with which the database is to respond to a query. For the complex hierarchical “multi-leveled” databases described above, the data should be arranged so as to enable the database to collate and deliver all relevant information relating to a complex query. However, as explained above, the requirement for those databases to be able to process complex queries leads to potentially long lag times which may be undesirable in the context of vehicle control applications. Therefore, the spatial database used in the present invention can store data in a “single-level” or “flat” structure according to the geographical location that particular items of data relate to.
Some algorithms which could be used to arrange the spatial data within the database include the algorithms commonly referred to by the names “Grid-indexing”, “Quadtree” or “R-tree”. However, in other embodiments of the invention data may be arranged within the database using a form of algorithm that will be referred to as a “spatial hash-key” algorithm. A spatial hash-key algorithm maps physical locations (based on their “real world” coordinates) into one-dimensional “hash-keys”. The “hash-key” for each location is a string of characters that can be stored in the database's hash table and retrieved in response to a query.
Properties of the spatial hash-key algorithm may include: points which are close to each other in the real world should have closely related hash keys (i.e. the algorithm should maintain “locality”), the algorithm should operate using whatever coordinate system the control system uses to represent the region, the algorithm should be adapted for digital implementation (hence, it should be adapted to operate using integer or floating-point numbers, preferably with 64-bit “double” precision or better) the algorithm should be fast to compute.
It is explained above that the control system of the present invention, and ideally the spatial database, may be adapted to receive updated data from the controller and/or an external source. The spatial database can be adapted to receive the updated information at control speed. Data received from the controller may include or may be used to generate, for example, estimates of the vehicle's predicted state (i.e. its speed, position, orientation, etc.) at an upcoming location based on its current instantaneous state at a particular location. The external sources may include GPS, INS, or any other inertial, visual or other system used for obtaining information relating to the state of the vehicle or other aspects of the region (such as obstacles close to the vehicle). Data received in this way may be (at least initially) recorded in its unprocessed or “raw” form in the database. This unprocessed data may be fed directly back into the controller, or the respective streams of incoming data (possibly relating to disparate variables) may be filtered using a Kalman filter or some other similar digital signal processing technique to obtain a statistically optimized estimate of the state of the vehicle and its proximate surroundings as it travels. This optimized estimate of the vehicle's state at a particular location may then be fed into the controller. The use of statistically optimized estimates and data may help to improve control performance.
According to a further broad form, the invention resides in a closed loop vehicle control system comprising: a spatial database; a controller adapted to receive spatial data from the spatial database at control speed, the controller controlling the steering of the vehicle; wherein updated spatial data is fed back into the control system.
In yet another broad form, the invention resides in a method for controlling a vehicle comprising: entering spatial data relating to a region to be traversed by the vehicle into a spatial database; providing spatial data from the spatial database to a controller at control speed to control the vehicle as the vehicle traverses the region; and entering updated spatial data into the spatial database as the vehicle traverses the region.
In yet a further broad form, the invention resides in a vehicle control system comprising: a spatial database; a controller adapted to receive spatial data from the spatial database; and the controller using the spatial data from the spatial database to control the steering of the vehicle.
It will be appreciated that all preferred features and aspects of the invention described with particular reference to one or other broad form of the invention, may also apply equally to all other forms of the invention, unless the context dictates otherwise.
Certain embodiments, aspects and features of the invention will now be described and explained by way of example and with reference to the drawings. However, it will be clearly appreciated that these descriptions and examples are provided to assist in understanding the invention only, and the invention is not limited to or by any of the embodiments, aspects or features described or exemplified.
1. Introduction and Environment
As required, detailed embodiments of the present invention are disclosed herein; however, it is to be understood that the disclosed embodiments are merely exemplary of the invention, which may be embodied in various forms. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present invention in virtually any appropriately detailed structure.
Certain terminology will be used in the following description for convenience in reference only and will not be limiting. For example, up, down, front, back, right and left refer to the invention as oriented in the view being referred to. The words “inwardly” and “outwardly” refer to directions toward and away from, respectively, the geometric center of the embodiment being described and designated parts thereof. Global navigation satellite systems (GNSS) are broadly defined to include GPS (U.S.), Galileo (Europe, proposed), GLONASSGlobalnaya Navigazionnaya Sputnikovaya Sistema (GLONASS) (Russia), Beidou (China), Compass (China, proposed), IRNSSIndian Regional Navigation Satellite System (IRNSS) (India, proposed), QZSSQuasi-Zenith Satellite System (QZSS) (Japan, proposed) and other current and future positioning technology using signals from satellites, using single or multiple antennae, with or without augmentation from terrestrial sources. Inertial navigation systems (INS) include gyroscopic (gyro) sensors, accelerometers and similar technologies for providing output corresponding to the inertia of moving components in all axes, i.e. through six degrees of freedom (positive and negative directions along longitudinal X, transverse Y and vertical Z axes). Yaw, pitch and roll refer to moving component rotation about the Z, Y and X axes respectively. Said terminology will include the words specifically mentioned, derivatives thereof and words of similar meaning.
2. Optical Vehicle Control System 2
The tractor 10 is fitted with a steering control system. The steering control system includes a GNSS receiver 13 connected to antennas 20, a controller 14 and a steering valve block 15. The controller 14 suitably includes a computer memory that is capable of having an initial path of travel entered therein. The computer memory is also adapted to store or generate a desired path of travel. The controller 14 receives position and attitude signals from one or more sensors (to be described later) and the data received from the sensors are used by the controller 14 to determine or calculate the position and attitude of the tractor. The controller 14 then compares the position and attitude of the tractor with the desired position and attitude of the tractor. If the determined or calculated position and attitude of the tractor deviates from the desired position and attitude of the tractor, the controller 14 issues a steering correction signal that interacts with a steering control mechanism. In response to the steering correction signal, the steering control mechanism makes adjustments to the angle of steering of the tractor, to thereby assist in moving the tractor back towards the desired path of travel. The steering control mechanism may comprise one or more mechanical or electrical controllers or devices that can automatically adjust the steering angle of the vehicle. These devices may act upon the steering pump, the steering column or steering linkages.
In one embodiment of the present invention, the steering control algorithm may be similar to that described in our U.S. Pat. No. 6,876,920, which is incorporated herein by reference and discloses a steering control algorithm, which involves entering an initial path of travel (often referred to as a wayline). The computer in the controller 14 then determines or calculates the desired path of travel, for example, by determining the offset of the implement being towed by the tractor and generating a series of parallel paths spaced apart from each other by the offset of the implement. This ensures that an optimal working of the field is obtained. The vehicle then commences moving along the desired path of travel. One or more sensors provide position and attitude signals to the controller and the controller uses those position and attitude signals to determine or calculate the position and attitude of the vehicle. This position and attitude is then compared with the desired position and attitude of the vehicle. If the vehicle is spaced away from the desired path of travel, or is pointing away from the desired path, the controller generates a steering correction signal. The steering correction signal may be generated, for example, by using the difference between the determined position and attitude of the vehicle and the desired position and attitude of the vehicle to generate an error signal, with the magnitude of the error signal being dependent upon the difference between the determined position and attitude and the desired position and attitude of the vehicle. The error signal may take the form of a curvature demand signal that acts to steer the vehicle back onto the desired path of travel. Steering angle sensors in the steering control mechanism may monitor the angle of the steering wheels of the tractor and send the data back to the controller to thereby allow the controller to correct for understeering or oversteering.
In an alternative embodiment, the error signal may result in generation of a steering guidance arrow on a visual display unit to thereby enable the driver of the vehicle to properly steer the vehicle back onto the desired path of travel. This manual control indicator may also be provided in conjunction with the steering controls as described in paragraph above.
It will be appreciated that the invention is by no means limited to the particular algorithm described, and that a wide variety of other steering control algorithms may also be used.
In general terms, most, if not all, steering control algorithms operate by comparing a determined or calculated position and attitude of the vehicle with a desired position and attitude of the vehicle. The desired position and attitude of the vehicle is typically determined from the path of travel that is entered into, or stored in, or generated by, the controller. The determined or calculated position and attitude of the vehicle is, in most, if not all, cases determined by having input data from one or more sensors being used to determine or calculate the position and attitude of the vehicle. In U.S. Pat. No. 6,876,920, GNSS sensors, accelerometers, wheel angle sensors and gyroscopes are used as the sensors in preferred embodiments of that patent.
Returning now to
The actual details of the controller will be readily understood by persons skilled in the art and need not be described further.
The tractor 10 shown in
In the embodiment shown in
The optical tracking movement sensor 16 may comprise the operative part of an optical computer mouse. Optical computer mice incorporate an optoelectronics sensor that takes successive pictures of the surface on which the mouse operates. Most optical computer mice use a light source to illuminate the surface that is being tracked. Changes between one frame and the next are processed by an image processing part of a chip embedded in the mouse and this translates the movement of the mouse into movement on two axes using a digital correlation algorithm. The optical movement sensor 16 may include an illumination source for emitting light therefrom. The illumination source may comprise one or more LEDs. The optical movement sensor may also include an illumination detector for detecting light reflected from the ground or the surface over which the vehicle is travelling. Appropriate optical components, such as a lens (preferably a telecentric lens), may be utilized to properly focus the emitted or detected light. A cleaning system, such as a stream of air or other cleaning fluid, may be used to keep the optical path clean. The optical movement sensor 16 may comprise a charge coupled device (CCD) or a complementary metal oxide semiconductor (CMOS) sensor. The optical movement sensor 16 may also include an integrated chip that can rapidly determine the relative movement along an axis of the vehicle and the relative movement across an axis of the vehicle by analysing successive frames captured by the illumination detector. The optical movement sensor can complete hundreds to thousands of calculations per second.
The optical movement sensor 16 generates signals that are indicative of the relative movement of the vehicle along the vehicle's axis and the relative movement of the vehicle across the vehicle's axis. The signals are sent to the controller 14. The signals received by the controller 14 are used to progressively calculate or determine changes in the position and attitude of the vehicle. In the embodiment shown in
Only one optical movement sensor 16 is illustrated in
The alternative embodiment shown in
3. Alternative Embodiment Optical Control System 102
The embodiment shown in
The GNSS receiver on the tractor 110 receives GNSS signals from the constellation of GNSS satellites via GNSS antenna 120 mounted on the tractor 110. The signals are sent to controller 114. The signals received from GNSS receiver(s) 113 on tractor 110 are corrected by the error correction signal sent from the transmitter 138136. Thus, an accurate determination of position of the tractor can be obtained from the differential GNSS system.
The controller 114 also receives position signals from the optical movement sensor 116. As described above with reference to the embodiment in
4. Alternative Embodiment Optical Control System 202
The embodiment shown in
The sensor assembly 221 provides relative position and attitude information to the controller 214. Similarly, the optical movement sensor 216 also provides relative position and attitude information to controller 214. The controller uses both sets of information to obtain a more accurate determination of the position and attitude of the vehicle. This will be described in greater detail hereunder. Also, as described above with reference to the embodiments in
5. Alternative Embodiment Optical Control System 302
The embodiment shown in
In
The optical movement sensor(s) 16 of
In state space representations, the variables or parameters used to mathematically model the motion of the vehicle, or aspects of its operation, are referred to as “states” xi. In the present case, the states may include the vehicle's position (x,y), velocity
heading h, radius of curvature r, etc. Hence the states may include x1=x, x2=y, x3=h, x4=hr,
etc. However, it will be appreciated that the choice of states is never unique, and the meaning and implications of this will be well understood by those skilled in the art.
The values for the individual states at a given time are represented as the individual entries in an n×1 “state vector”:
X(t)=[x1(t)x2(t)x3(t)x4(t). . . xn(t)]T
where n is the number of states.
In general, the mathematical model used to model the vehicle's motion and aspects of its operation will comprise a series of differential equations. The number of equations will be the same as the number of states. In some cases, the differential equations will be linear in terms of the states, whereas in other situations the equations may be nonlinear in which case they must generally be “linearized” about a point in the “state space”. Linearization techniques that may be used to do this will be well known to those skilled in this area.
Next, by noting that any jth order linear differential equations can be re-written equivalently as a set of j first order linear differential equations, the linear (or linearized) equations that represent the model can be expressed using the following “state” equation:
where:
The quantities that are desired to be known about the vehicle (the real values for which are generally also measured from the vehicle itself, if possible) are the outputs y, from the model. Each of the outputs generated by the linear (or linearized) model comprises a linear combination of the states x, and inputs u, and so the outputs can be defined by the “output” or “measurement” equation:
Y(t)=CX(t)+DU(t)+Mv(t)
where
Next, it will be noted that both the state equation and the measurement equation defined above are continuous functions of time. However, continuous time functions do not often lend themselves to easy digital implementation (such as will generally be required in implementing the present invention) because digital control systems generally operate as recursively repeating algorithms. Therefore, for the purpose of implementing the equations digitally, the continuous time equations may be converted into the following recursive discrete time equations by making the substitutions set out below and noting that (according to the principle of superposition) the overall response of a linear system is the sum of the free (unforced) response of that system and the responses of that system due to forcing/driving inputs. The recursive discrete time equations are:
Xk+1=FXk+GUk+1+Lwk+1
Yk+1=ZYk+JUk+1+Nwk+1
where
However, as noted above, the quantity Ew(t) is not deterministic and so the integral defining Lwk+1 cannot be performed (even numerically). It is for this reason that it is preferable to use statistical filtering techniques. The optimal estimator shown in
In general, a Kalman filter operates as a “predictor-corrector” algorithm. Hence, the algorithm operates by first using the mathematical model to “predict” the value of each of the states at time step k+1 based on the known inputs at time step k+1 and the known value of the states from the previous time step k. It then “corrects” the predicted value using actual measurements taken from the vehicle at time step k+1 and the optimized statistical properties of the model. In summary, the Kalman filter comprises the following equations each of which is computed in the following order for each time step:
where
The operation of the discrete time Kalman filter which may be used in the optimal estimator of the present invention is schematically illustrated in
Returning now to
The error calculation module 62 uses the statistically optimal estimate of the position and attitude of the tractor obtained from the optimal estimator 60 and the desired position and attitude of the tractor determined from the required control path to calculate the error in position and attitude of the tractor. This may be calculated as an error in the x-coordinate, an error in the y-coordinate and an error in the heading of the position and attitude of the tractor. These error values are represented as “Ex”, “Ey” and “Eh” in
In cases where a GNSS outage occurs, the optical movement sensor continues to provide position and attitude data to the optimal estimator. In such circumstances, control of the vehicle can be effected by the information received from the optical movement sensor alone.
As a further benefit arising from the system shown in
6. Alternative Embodiment Optical Control System 402
The embodiments shown in
The optical movement chip 506 sends signals to the optimal estimator, as shown in
The present invention provides control systems that can be used to control the movement of the vehicle or an implement associated with the vehicle. The control system includes an optical movement sensor that may be the operative part of an optical computer mouse. These optical movement sensors are relatively inexpensive, provide a high processing rate and utilize proven technology. Due to the high processing rate of such optical movement sensors, the control system has a high clock speed and therefore a high frequency of updating of the determined or calculated position of the vehicle or implement. The optical movement sensor may be used by itself or it may be used in conjunction with a GNSS system, one or more inertial sensors, or one or more vehicle based sensors. The optical movement sensor can be used to augment the accuracy of inertial and/or other sensors. In particular, the optical movement sensor can be used to debias yaw drift that is often inherent in inertial sensors.
7. Alternative Embodiment Vehicle Control System 600
As described in the background section above, one of the problems with existing vehicle control systems is that they are inherently “one-dimensional” or “linear” in nature. The inherent “linear” nature of existing control systems is illustrated schematically in
Next,
The components of the control system in the particular embodiment shown in
The main control unit 603 receives GPS signals from the GPS antenna 604, and it uses these (typically in combination with feedback and/or other external spatial data signals) to generate a control signal for steering the vehicle. The control signal will typically be made up of a number of components or streams of data relating to the different parameters of the vehicle being controlled, for example the vehicle's “cross-track error”, “heading error”, “curvature error”, etc. These parameters will be described further below. The control signal is amplified using suitable signal amplifiers (not shown) to create a signal that is sufficiently strong to drive the actuators 605. The actuators 605 are interconnected with the vehicle's steering mechanism (not shown) such that the actuators operate to steer the vehicle as directed by the control signal.
In some embodiments, further actuators (not shown) may also be provided which are interconnected with the vehicle's accelerator and/or braking mechanisms, and the control signal may incorporate components or signal streams relating to the vehicle's forward progress (i.e. its forward speed, acceleration, deceleration, etc.). In these embodiments, the component(s) of the control signal relating to the vehicle's forward progress may also be amplified by amplifiers (not shown) sufficiently to cause the actuators which are interconnected with the accelerator/braking mechanism to control the vehicle's acceleration/deceleration in response to the control signal.
The vehicle 601 may also be optionally provided with one or more optical sensors 606, one or more inertial sensors (IS) 607 and a user terminal (UT) 608. One form of optical sensor 606 that may be used may operate by receiving images of the ground beneath the vehicle, preferably in rapid succession, and correlating the data pertaining to respective successive images to obtain information relating to the vehicle's motion. Other forms of optical sensor may also be used including LIDAR (Light Detection and Ranging) or sensors which operate using machine vision and/or image analysis. If present, the one or more inertial sensors 607 will typically include at least one gyroscope (e.g., a rate gyroscope), although the inertial sensors 607 could also comprise a number of sensors and components (such as accelerometers, tilt sensors and the like) which together form a sophisticated inertial navigation system (INS). The vehicle may be further provided with additional sensors (not shown) such as sensors which receive information regarding the location of the vehicle relative to a fixed point of known location in or near the field, magnetometers, ultrasonic range and direction finding and the like. The data generated by these additional sensors may be fed into the database and used by the control system to control the vehicle as described below.
In embodiments where the main control unit 603 comprises an industrial PC or the like, the user terminal 608 may comprise a full computer keyboard and separate screen to enable the user to utilize the full functionality of the computer. However, in embodiments where the main control unit is a purpose-built unit containing only hardware relating to the vehicle's control system, the terminal 608 may comprise, for example, a single combined unit having a display and such controls as may be necessary for the user to operate the vehicle's control system. Any kind of controls known by those skilled in this area to be suitable may be used on the main control unit, including keypads, joysticks, touch screens and the like.
In
In order to control the steering of the vehicle, there are three parameters that should be controlled. These are the “cross-track error”, the “heading error” and the “curvature error”. The physical meaning of these parameters can be understood with reference to
Heading Error=H−h
Those skilled in the art will recognize that both h and H are inherently directional quantities.
Finally, the “curvature error” is the difference between the actual instantaneous radius of curvature r of the vehicle's motion and the desired instantaneous radius of curvature R. The curvature error is given by:
Curvature Error=1/R−1/r
It will also be clearly appreciated that there may be many other vehicle variables or parameters which also need to be controlled if, for example, acceleration/deceleration or the vehicle's mode of equipment operation are also to be controlled.
Referring next to
In the overall operation of the control system, the desired path trajectory for the vehicle is first entered into the control system by the user via the user terminal 608. The task path generator then interprets this user-defined path definition and converts it into a series of points of sufficient spatial density to adequately represent the desired path to the requisite level of precision. The task path generator typically also defines the vehicle's desired trajectory along the user-defined path, for example, by generating a desired vehicle position, a desired heading H and a desired instantaneous radius of curvature R for each point on the path. This information is then loaded into the spatial database. The way in which this and other spatial information is stored within the database in representative embodiments, and in particular the way in which pieces of data are given memory allocations according to their spatial location, is described further below.
As the vehicle moves along the user-defined path, it will invariably experience various perturbations in its position and orientation due to, for example, bumps, potholes, subsidence beneath the vehicle's wheels, vehicle wheel-spin, over/under-steer, etc. Those skilled in this area will recognize that a huge range of other similar factors can also influence the instantaneous position and orientation of the vehicle as it moves. One of the purposes of the present control system is to automatically correct for these perturbations in position and orientation to maintain the vehicle on the desired path (or as close to it as possible).
As the vehicle moves, the control system progressively receives updated information regarding spatial location from the external spatial data sources. The external spatial data sources will typically include GPS. However, a range of other spatial data sources may also be used in addition to, or in substitute for GPS. For example, the inertial navigation systems (INS), visual navigation systems, etc. described above may also be used as external data sources in the present control system.
Those skilled in the art will recognize that the spatial data collected by the external spatial data sources actually pertains to the specific location of the external spatial data receivers, not necessarily the vehicle/implement reference location itself (which is what is controlled by the control system). In
In addition to this, changes in the vehicle's attitude will also influence the spatial position readings received by the different receivers. For example, if one of the vehicle's wheels passes over, or is pushed sideways by a bump, this may cause the vehicle to rotate about at least one (and possibly two or three) of the axes shown in
In order to compensate for the difference in position between the vehicle's reference point and the location of the spatial data receiver(s), and also to account for changes in the vehicle's orientation, a vehicle attitude compensation module is provided. This is shown in
Those skilled in the art will recognize that the one or more external spatial data sources will progressively receive updated data readings in rapid succession (e.g., in “real time” or as close as possible to it). These readings are then converted by the vehicle attitude compensation module and fed into the spatial database. The readings may also be filtered as described above. Therefore, whilst each reading from each spatial data source is received, converted (ideally filtered) and entered into the spatial database individually, nevertheless the rapid successive way in which these readings (possibly from multiple “parallel” data sources) are received, converted and entered effectively creates a “stream” of incoming spatial data pertaining to the vehicle's continuously changing instantaneous location and orientation. In order to provide sufficient bandwidth, successive readings from each external spatial data source should be received and converted with a frequency of the same order as the clock speed (or at least one of the clock speeds) of the controller, typically 3 Hz-12 Hz or higher.
Referring again to
The position error generator then uses this information to calculate an instantaneous “error term” for the vehicle. The “error term” incorporates the vehicle's instantaneous cross-track error, heading error and curvature error (as described above). The error term is then fed into the controller. The controller is shown in greater detail in
From
In
The external obstacle detection input may comprise any form of vision based, sound based or other obstacle detection means, and the obstacle detection data may be converted by the vehicle attitude compensation module (just like the other sources of external data discussed above) and then fed into the spatial database. Where the control system incorporates obstacle detection, it is then necessary for the task path generator to be able to receive updated information from the spatial database. This is so that if an obstacle is detected on the desired path, an alternative path that avoids the obstacle can be calculated by the task path generator and re-entered into the database. The ability of the task path generator to also receive data from the spatial database is indicated by the additional arrow from the spatial database to the task path generator in
FIGS. 4-616-18 graphically represent the operation of the control system. However, it is also useful to consider the way in which the vehicle's parameters and dynamics are represented for the purposes of implementing the control system. Those skilled in the art will recognize that a range of methods may be used for this purpose. However, it is considered that one method is to represent the parameters and dynamics in “state space” form.
In state space representations, the variables or parameters used to mathematically model the motion of the vehicle, or aspects of its operation, are referred to as “states” xi. In the present case, the states may include the vehicle's position (x,y), velocity
heading h, radius of curvature r etc. Hence the states may include xi=x,
Etc. However, it will be appreciated that the choice of states is never unique, and the meaning and implications of this will be well understood by those skilled in the art.
The values for the individual states at a given time are represented as the individual entries in an n×1 “state vector”:
X(t)=[x1(t)x2(t)x3(t)x4(t). . . xn(t)]T
where n is the number of states.
In general, the mathematical model used to model the vehicle's motion and aspects of its operation will comprise a series of differential equations. The number of equations will be the same as the number of states. In some cases, the differential equations will be linear in terms of the states, whereas in other situations the equations may be nonlinear in which case they must generally be “linearised” about a point in the “state space”. Linearisation techniques that may be used to do this will be well known to those skilled in this area.
Next, by noting that any jth order linear differential equations can be re-written equivalently as a set j first order linear differential equations, the linear (or linearized) equations that represent the model can be expressed using the following “state” equation:
Where:
The process noise represents errors in the model and vehicle dynamics which exist in the actual vehicle but which are not accounted for in the model. As Ew(t) represents an unknown quantity, its contents are not known. However, for reasons that will be understood by those skilled in this area, in order to allow statistically optimized signal processing and state estimation Ew(t) is generally assumed to be Gaussian, white, have zero mean and to act directly on the state derivatives. It is also assumed that the process noise element associated with each individual state is uncorrelated with the process noise element of the other states.
The quantities that are desired to be known about the vehicle (the real values for which are generally also measured from the vehicle itself, if possible) are the outputs y1 from the model. Each of the outputs generated by the linear (or linearized) model comprises a linear combination of the states xi and inputs ui, and so the outputs can be defined by the “output” or “measurement” equation:
Y(t)=CX(t)+DU(t)Mv(t)
Next, it will be noted that both the state equation and the measurement equation defined above are continuous functions of time. However, continuous time functions do not often lend themselves to easy digital implementation (such as will generally be required in implementing the present invention) because digital control systems generally operate as recursively repeating algorithms. Therefore, for the purpose of implementing the equations digitally, the continuous time equations may be converted into the following recursive discrete time equations by making the substitutions set out below and noting that (according to the principle of superposition) the overall response of a linear system is the sum of the free (unforced) response of that system and the responses of that system due to forcing/driving inputs. The recursive discrete time equations are:
Xk+1=FXk+GUk+1+Lwk+1
Yk+1=ZXk+JUk+1+Nvk+1
where k+1 is the time step occurring immediately after time step k, Z=C, J=D and Nv is the discrete time analog of the continuous time measurement noise Mv(t). F is a transition matrix which governs the free response of the system. F is given by:
F=eAδ
GU.sub.k+1 is the forced response of the system, i.e. the system's response due to the driving inputs. It is defined by the convolution integral as follows:
Similarly, the quantity Lw.sub.k+1 is the (forced) response of the system due to the random “error” inputs that make up the process noise. Hence, conceptually this quantity may be defined as:
However, as noted above, the quantity Ew(t) is not deterministic and so the integral defining Lw.sub.k+1 cannot be performed (even numerically). It is for this reason that it is preferable to use statistical filtering techniques such as a “Kalman Filter” to statistically optimize the states estimated by the mathematical model.
In general, a “Kalman Filter” operates as a “predictor-corrector” algorithm. Hence, the algorithm operates by first using the mathematical model to “predict” the value of each of the states at time step k+1 based on the known inputs at time step k+1 and the known value of the states from the previous time step k. It then “corrects” the predicted value using actual measurements taken from the vehicle at time step k+1 and the optimized statistical properties of the model. In summary, the Kalman Filter comprises the following equations each of which is computed in the following order for each time step:
where the notation k+1|k means the value of the quantity in question at time step k+1 given information from time step k. Similarly, k+1|k+1 means the value of the quantity at time step k+1 given updated information from time step k+1. [0135]P is the co-variance in the difference between the estimated and actual value of X. [0136]Q is the co-variance in the process noise. [0137]K is the “Kalman gain” which is a matrix of computed coefficients used to optimally “correct” the initial state estimate. [0138]R is the co-variance in the measurement noise. [0139] is a vector containing measurement values taken from the actual vehicle.
The operation of the discrete time state space equations outlined above, including the Kalman gain and the overall feedback closed loop control structure, are represented graphically in
In relation to the spatial database, it is mentioned above that a wide range of methods are known for arranging data within databases. One commonly used technique is to provide a “hash table”. The hash table typically operates as a form of index allowing the computer (in this case the control system CPU) to “look up” a particular piece of data in the database (i.e. to look up the location of that piece of data in memory). In the context of the present invention, pieces of data pertaining to particular locations along the vehicle's path are assigned different hash keys based on the spatial location to which they relate. The hash table then lists a corresponding memory location for each hash key. Thus, the CPU is able to “look up” data pertaining to a particular location by looking up the hash key for that location in the hash table which then gives the corresponding location for the particular piece of data in memory. In order to increase the speed with which these queries can be carried out, the hash keys for different pieces of spatial data can be assigned in such a way that “locality” is maintained. In other words, points which are close to each other in the real world should be given closely related indices in the hash table (i.e. closely related hash keys).
The spatial hash algorithm used to generate hash keys for different spatial locations in representative embodiments of the present invention may be most easily explained by way of a series of examples. To begin, it is useful to consider the hypothetical vehicle path trajectory shown in
As outlined above, in the present invention all data is stored within the spatial database with reference to spatial location. Therefore, it is necessary to assign indices or “hash keys” to each piece of data based on the spatial location to which each said piece of data relates. However, it will be recalled that the hash table must operate by listing the hash key for each particular spatial location together with the corresponding memory location for data pertaining to that spatial location. Therefore, the hash table is inherently one-dimensional, and yet it must be used to link hash keys to corresponding memory allocations for data that inherently pertains to two-dimensional space.
One simple way of overcoming this problem would be to simply assign hash keys to each spatial location based only on, say, the Y coordinate at each location. The hash keys generated in this way for each point on the vehicle path in
The prefix “0x” indicates that the numbers in question are expressed in hexadecimal format. This is a conventional notation.
Those skilled in the art will recognize that the above method for generating hash keys is far from optimal because there are five distinct spatial locations assigned to each different hash key. Furthermore, in many instances, this method assigns the same hash key to spatial locations which are physically remote from each other. For instance, the point (0,1) is distant from the point (4,1), and yet both locations are assigned the same hash key. An identically ineffective result would be obtained by generating a hash key based on only the X coordinate.
An alternative method would be to generate hash keys by concatenating the X and Y coordinates for each location. The hash keys generated using this method for each point on the vehicle path in
In order to understand how the numbers listed in Table 2 above were arrived at, it is necessary to recognize that in the digital implementation of the present control system, all coordinates will be represented in binary. For the purposes of the present example which relates to the simplified integer based coordinate system in
Hence, to illustrate the operation of the spatial hash key algorithm used to generate the numbers in Table 2, consider the point (3,3). Those skilled in the art will understand that the decimal number 3 may be written as 11 in binary notation. Therefore, the location (3,3) may be rewritten in 8-bit binary array notation as (00000011,00000011). Concatenating these binary coordinates then gives the single 16-bit binary hash key 0000001100000011 which can equivalently be written as the hexadecimal number 0x303 or the decimal number 771. The process of converting between decimal, binary and hexadecimal representations should be well known to those skilled in the art and need not be explained.
It will be noted from Table 2 above that concatenating the X and Y coordinates leads to unique hash keys (in this example) for each spatial location. However, the hash keys generated in this way are still somewhat sub-optimal because points which are located close to each other are often assigned vastly differing hash keys. For example, consider the points (0,0) and (1,0). These are adjacent point in the “real world”. However, the hash keys assigned to these points using this method (written in decimal notation) are 0 and 256 respectively. In contrast, the point (0,4) is much further away from (0,0) and yet it is assigned the much closer hash key 4. Therefore, this algorithm does not maintain “locality”, and an alternative algorithm would be preferable.
Yet a further method for generating hash keys is to use a technique which shall hereinafter be referred to as “bitwise interleaving”. As for the previous example, the first step in this technique is to represent the (X,Y) coordinates in binary form. Hence, using the 8-bit binary array representation discussed above, the point (X,Y) may be re-written in 8-bit binary array notation as (X1X2X3X4X5X6X7×8, Y1Y2Y3Y4Y5Y6Y7Y8). Next, rather than concatenating the X and Y coordinates to arrive at a single 16-bit binary hash key, the successive bits from the X and Y binary coordinates are alternatingly “interleaved” to give the following 16-bit binary hash key X1Y1X2Y2X3Y3X4Y4×5Y5X6Y6X7YX8Y8. The hash keys generated using this method for each point on the vehicle path in
To further illustrate the operation of the spatial hash algorithm used to generate the numbers in Table 3, consider the point (3,4). As noted above, the decimal number 3 may be written as 11 in binary notation. Similarly, decimal number 4 is written as 100 in binary. Therefore, the location (3,4) may be rewritten in 8-bit binary array notation as (00000011,00000100). Bitwise interleaving these binary coordinates then gives the single 16-bit binary hash key 0000000000011010, which can equivalently be written as the hexadecimal number 0x1a or the decimal number 26.
From Table 3 it will be seen that generating hash keys by “bitwise interleaving” the X and Y coordinates leads to unique hash keys (in this example) for each spatial location. Also, the hash keys generated in this way satisfy the requirement that points which are close together in the real world are assigned closely related hash keys. For example, consider again the points (0,0) and (1,0). The hash keys now assigned to these points by “bitwise interleaving” (when written in decimal notation) are 0 and 2 respectively. Furthermore, the point (0,1) which is also nearby is also assigned the closely related hash key 1. Conversely, points which are separated by a considerable distance in the real world are given considerably differing hash keys, for example, the hash key for (4,3) is 37.
From the example described with reference to Table 3, it can be seen that generating hash keys by “bitwise interleaving” the binary X and Y coordinates preserves “locality”. This example therefore conceptually illustrates the operation of the bitwise interleaving spatial hash algorithm that may be used with representative embodiments of the present invention. However, the above example is based on the simplified integer based coordinate system shown in
The fact that GPS and other similar systems which describe spatial location typically do so using IEEE double-precision floating-point numbers (not simple integers). For instance, GPS supplies coordinates in the form of (X,Y) coordinates where X corresponds to longitude, and Y corresponds to latitude. Both X and Y are given in units of decimal degrees.
the fact that certain spatial locations have negative coordinate values when described using GPS and other similar coordinate systems. For example, using the WGS84 datum used by current GPS, the coordinates (153.00341,−27.47988) correspond to a location in Queensland, Australia (the negative latitude value indicates southern hemisphere).
Complexities inherent in representing numbers in accordance with the IEEE double-precision floating-point numbers standard.
A double-precision floating-point number represented in accordance with the IEEE 754 standard comprises a string of 64 binary characters (64 bits) as shown in
Hence, actual exponent value=written exponent value-exponent bias.
The exponent bias is 0x3ff=1023. Consequently, the maximum true exponent value that can be represented (written in decimal notation) is 1023, and the minimum true exponent value that can be represented is −1022.
Finally, the remaining 52 bits form the mantissa. However, as all non-zero numbers must necessarily have a leading “1” when written in binary notation, an implicit “1” followed by a binary point is assumed to exist at the front of the mantissa. In other words, the leading “1” and the binary point which must necessarily exist for all non-zero binary numbers is simply omitted from the actual written mantissa in the IEEE 64-bit standard format. This is so that an additional bit may be used to represent the number with greater precision. However, when interpreting numbers which are represented in accordance with the IEEE standard, it is important to remember that this leading “1” and the binary point implicitly exist even though they are not written.
Bearing in mind these issues, it is possible to understand the actual spatial hash algorithm used in representative implementations of the present control system. A “worked” example illustrating the operation of the spatial hash algorithm to generate a hash key based on the coordinate (153.0000°, −27.0000° is given in the form of a flow diagram in
From
After normalising the coordinates, the next step is to convert the respective coordinates from their representations in decimal degrees into binary IEEE double-precision floating-point number format. This is shown as step 3) in
Next, the binary representations of the two coordinates are split into their respective exponent (11 bits) and mantissa (52 bits) portions. This is step 4) in
After de-biasing the exponents, the resulting exponents are then adjusted by a selected offset. The size of the offset is selected depending on the desired “granularity” of the resulting fix-point number. In the particular example shown in step 6) of
After adjusting the exponent, the next step is to “resurrect” the leading “1” and the binary point which implicitly exist in the mantissa but which are left off when the mantissa is actually written (see above). Hence, the leading “1” and the binary point are simply prepended to the mantissa of each of the coordinates. This is step 7) in
The mantissa for each coordinate is then right-shifted by the number of bits in the corresponding exponent. The exponents for each coordinate are then prepended to their corresponding mantissas forming a single character string for each coordinate. There is then an optional step of discarding the high-order byte for each of the two bit fields. This may be done simply to save memory if required, but is not necessary. Finally, the resultant bit fields for each coordinate are bitwise interleaved to obtain a single hash key corresponding to the original coordinates. In the example shown in
Those skilled in the art will recognize that various other alterations and modifications may be made to the particular embodiments, aspects and features of the invention described without departing from the spirit and scope of the invention may be made to the particular embodiments, aspects and features of the invention described without departing from the spirit and scope of the invention.
This application is a reissue application of application Ser. No. 13/573,682, filed Oct. 3, 2012, now U.S. Pat. No. 8,768,558, issued Jul. 1, 2014, which is a continuation-in-part of and claims the benefit of U.S. patent application Ser. No. 12/504,779, filed Jul. 17, 2009, now U.S. Pat. No. 8,311,696, and is a continuation-in-part of and claims the benefit of U.S. patent application Ser. No. 12/947,620, filed Nov. 16, 2010, now abandoned, which is continuation of and claims the benefit of U.S. patent application Ser. No. 11/620,388, filed Jan. 5, 2007, entitled “Vehicle Control System,” now U.S. Pat. No. 7,835,832, issued Nov. 16, 2010, which are all incorporated herein by reference.
| Number | Name | Date | Kind |
|---|---|---|---|
| 3585537 | Rennick | Jun 1971 | A |
| 3596228 | Reed, Jr. | Jul 1971 | A |
| 3727710 | Sanders | Apr 1973 | A |
| 3815272 | Marleau | Jun 1974 | A |
| 3899028 | Morris | Aug 1975 | A |
| 3987456 | Gelin | Oct 1976 | A |
| 4132272 | Holloway | Jan 1979 | A |
| 4170776 | MacDoran | Oct 1979 | A |
| 4180133 | Collogan | Dec 1979 | A |
| 4398162 | Nagai | Aug 1983 | A |
| 4453614 | Allen | Jun 1984 | A |
| 4529990 | Brunner | Jul 1985 | A |
| 4637474 | Leonard | Jan 1987 | A |
| 4667203 | Counselman, III | May 1987 | A |
| 4689556 | Cedrone | Aug 1987 | A |
| 4694264 | Owens | Sep 1987 | A |
| 4694639 | Chen | Sep 1987 | A |
| 4710775 | Coe | Dec 1987 | A |
| 4714435 | Stipanuk | Dec 1987 | A |
| 4739448 | Rowe | Apr 1988 | A |
| 4751512 | Longaker | Jun 1988 | A |
| 4769700 | Pryor | Sep 1988 | A |
| 4777601 | Boegli | Oct 1988 | A |
| 4785463 | Janc | Nov 1988 | A |
| 4802545 | Nystuen | Feb 1989 | A |
| 4812991 | Hatch | Mar 1989 | A |
| 4858132 | Holmquist | Aug 1989 | A |
| 4864320 | Munson | Sep 1989 | A |
| 4894662 | Counselman | Jan 1990 | A |
| 4916577 | Dawkins | Apr 1990 | A |
| 4918607 | Wible | Apr 1990 | A |
| 4963889 | Hatch | Oct 1990 | A |
| 5031704 | Fleischer | Jul 1991 | A |
| 5100229 | Lundberg | Mar 1992 | A |
| 5134407 | Lorenz | Jul 1992 | A |
| 5144130 | Meyer et al. | Sep 1992 | A |
| 5148179 | Allison | Sep 1992 | A |
| 5152347 | Miller | Oct 1992 | A |
| 5155490 | Spradley | Oct 1992 | A |
| 5155493 | Thursby | Oct 1992 | A |
| 5156219 | Schmidt | Oct 1992 | A |
| 5165109 | Han | Nov 1992 | A |
| 5173715 | Rodal | Dec 1992 | A |
| 5177489 | Hatch | Jan 1993 | A |
| 5185610 | Ward | Feb 1993 | A |
| 5191351 | Hofer | Mar 1993 | A |
| 5194851 | Kraning et al. | Mar 1993 | A |
| 5202829 | Geier | Apr 1993 | A |
| 5207239 | Schwitalia | May 1993 | A |
| 5229941 | Hattori | Jul 1993 | A |
| 5239669 | Mason | Aug 1993 | A |
| 5247440 | Capurka | Sep 1993 | A |
| 5255756 | Follmer | Oct 1993 | A |
| 5268695 | Dentinger | Dec 1993 | A |
| 5293170 | Lorenz | Mar 1994 | A |
| 5294970 | Dornbusch | Mar 1994 | A |
| 5296861 | Knight | Mar 1994 | A |
| 5311149 | Wagner | May 1994 | A |
| 5323322 | Mueller | Jun 1994 | A |
| 5334987 | Teach | Aug 1994 | A |
| 5343209 | Sennott | Aug 1994 | A |
| 5345245 | Ishikawa | Sep 1994 | A |
| 5359332 | Allison | Oct 1994 | A |
| 5361212 | Class | Nov 1994 | A |
| 5365447 | Dennis | Nov 1994 | A |
| 5369589 | Steiner | Nov 1994 | A |
| 5375059 | Kyrtsos | Dec 1994 | A |
| 5390124 | Kyrtsos | Feb 1995 | A |
| 5390125 | Sennott | Feb 1995 | A |
| 5390207 | Fenton | Feb 1995 | A |
| 5416712 | Geier | May 1995 | A |
| 5442363 | Remondi | Aug 1995 | A |
| 5444453 | Lalezari | Aug 1995 | A |
| 5451964 | Babu | Sep 1995 | A |
| 5467282 | Dennis | Nov 1995 | A |
| 5471217 | Hatch | Nov 1995 | A |
| 5476147 | Fixemer | Dec 1995 | A |
| 5477228 | Tiwari | Dec 1995 | A |
| 5477458 | Loomis | Dec 1995 | A |
| 5490073 | Kyrtsos | Feb 1996 | A |
| 5491636 | Robertson | Feb 1996 | A |
| 5495257 | Loomis | Feb 1996 | A |
| 5504482 | Schreder | Apr 1996 | A |
| 5511623 | Frasier | Apr 1996 | A |
| 5519620 | Talbot | May 1996 | A |
| 5521610 | Rodal | May 1996 | A |
| 5523761 | Gildea | Jun 1996 | A |
| 5534875 | Diefes | Jul 1996 | A |
| 5543804 | Buchler | Aug 1996 | A |
| 5546093 | Gudat | Aug 1996 | A |
| 5548293 | Cohen | Aug 1996 | A |
| 5561432 | Knight | Oct 1996 | A |
| 5563786 | Torii | Oct 1996 | A |
| 5568152 | Janky | Oct 1996 | A |
| 5568162 | Samsel | Oct 1996 | A |
| 5583513 | Cohen | Dec 1996 | A |
| 5589835 | Gildea | Dec 1996 | A |
| 5592382 | Colley | Jan 1997 | A |
| 5596328 | Stangeland | Jan 1997 | A |
| 5600670 | Turney | Feb 1997 | A |
| 5604506 | Rodal | Feb 1997 | A |
| 5608393 | Hartman | Mar 1997 | A |
| 5610522 | Locatelli | Mar 1997 | A |
| 5610616 | Vallot | Mar 1997 | A |
| 5610845 | Slabinski | Mar 1997 | A |
| 5612883 | Shaffer | Mar 1997 | A |
| 5615116 | Gudat | Mar 1997 | A |
| 5617100 | Akiyoshi | Apr 1997 | A |
| 5617317 | Ignagni | Apr 1997 | A |
| 5621646 | Enge | Apr 1997 | A |
| 5638077 | Martin | Jun 1997 | A |
| 5644139 | Allen et al. | Jul 1997 | A |
| 5663879 | Trovato | Sep 1997 | A |
| 5664632 | Frasier | Sep 1997 | A |
| 5673491 | Brenna | Oct 1997 | A |
| 5680140 | Loomis | Oct 1997 | A |
| 5684476 | Anderson | Nov 1997 | A |
| 5684696 | Rao | Nov 1997 | A |
| 5706015 | Chen | Jan 1998 | A |
| 5717593 | Gvili | Feb 1998 | A |
| 5725230 | Walkup | Mar 1998 | A |
| 5731786 | Abraham | Mar 1998 | A |
| 5739785 | Allison | Apr 1998 | A |
| 5757316 | Buchler | May 1998 | A |
| 5765123 | Nimura | Jun 1998 | A |
| 5777578 | Chang | Jul 1998 | A |
| 5810095 | Orbach | Sep 1998 | A |
| 5814961 | Imahashi | Sep 1998 | A |
| 5828336 | Yunck | Oct 1998 | A |
| 5838562 | Gudat | Nov 1998 | A |
| 5848485 | Anderson et al. | Dec 1998 | A |
| 5854987 | Sekine | Dec 1998 | A |
| 5862501 | Talbot | Jan 1999 | A |
| 5864315 | Welles, II | Jan 1999 | A |
| 5864318 | Cosenza | Jan 1999 | A |
| 5875408 | Bendett | Feb 1999 | A |
| 5877725 | Kalafus | Mar 1999 | A |
| 5890091 | Talbot | Mar 1999 | A |
| 5899957 | Loomis | May 1999 | A |
| 5906645 | Kagawa | May 1999 | A |
| 5912798 | Chu | Jun 1999 | A |
| 5914685 | Kozlov | Jun 1999 | A |
| 5917448 | Mickelson | Jun 1999 | A |
| 5918558 | Susag | Jul 1999 | A |
| 5919242 | Greatline | Jul 1999 | A |
| 5923270 | Sampo | Jul 1999 | A |
| 5925080 | Shimbara | Jul 1999 | A |
| 5926079 | Heine | Jul 1999 | A |
| 5927603 | McNabb | Jul 1999 | A |
| 5928309 | Korver | Jul 1999 | A |
| 5929721 | Munn | Jul 1999 | A |
| 5933110 | Tang | Aug 1999 | A |
| 5935183 | Sahm | Aug 1999 | A |
| 5936573 | Smith | Aug 1999 | A |
| 5940026 | Popeck | Aug 1999 | A |
| 5941317 | Mansur | Aug 1999 | A |
| 5943008 | Van Dusseldorp | Aug 1999 | A |
| 5944770 | Enge | Aug 1999 | A |
| 5945917 | Harry | Aug 1999 | A |
| 5949371 | Nichols | Sep 1999 | A |
| 5955973 | Anderson | Sep 1999 | A |
| 5956250 | Gudat | Sep 1999 | A |
| 5969670 | Kalafus | Oct 1999 | A |
| 5987383 | Keller | Nov 1999 | A |
| 6014101 | Loomis | Jan 2000 | A |
| 6014608 | Seo | Jan 2000 | A |
| 6018313 | Engelmayer | Jan 2000 | A |
| 6023239 | Kovach | Feb 2000 | A |
| 6052647 | Parkinson | Apr 2000 | A |
| 6055477 | McBurney | Apr 2000 | A |
| 6057800 | Yang | May 2000 | A |
| 6061390 | Meehan | May 2000 | A |
| 6061632 | Dreier | May 2000 | A |
| 6062317 | Gharsalli | May 2000 | A |
| 6069583 | Silvestrin | May 2000 | A |
| 6070673 | Wendte | Jun 2000 | A |
| 6076612 | Carr | Jun 2000 | A |
| 6081171 | Ella | Jun 2000 | A |
| 6100842 | Dreier | Aug 2000 | A |
| 6104978 | Harrison | Aug 2000 | A |
| 6122595 | Varley | Sep 2000 | A |
| 6128574 | Diekhans | Oct 2000 | A |
| 6138062 | Usami | Oct 2000 | A |
| 6144335 | Rogers | Nov 2000 | A |
| 6191730 | Nelson, Jr. | Feb 2001 | B1 |
| 6191733 | Dizchavez | Feb 2001 | B1 |
| 6198430 | Hwang | Mar 2001 | B1 |
| 6198992 | Winslow | Mar 2001 | B1 |
| 6199000 | Keller | Mar 2001 | B1 |
| 6205401 | Pickhard | Mar 2001 | B1 |
| 6212453 | Kawagoe | Apr 2001 | B1 |
| 6215828 | Signell | Apr 2001 | B1 |
| 6229479 | Kozlov | May 2001 | B1 |
| 6230097 | Dance | May 2001 | B1 |
| 6233511 | Berger | May 2001 | B1 |
| 6236916 | Staub | May 2001 | B1 |
| 6236924 | Motz | May 2001 | B1 |
| 6253160 | Hanseder | Jun 2001 | B1 |
| 6256583 | Sutton | Jul 2001 | B1 |
| 6259398 | Riley | Jul 2001 | B1 |
| 6266595 | Greatline | Jul 2001 | B1 |
| 6275705 | Drane | Aug 2001 | B1 |
| 6285320 | Olster | Sep 2001 | B1 |
| 6292132 | Wilson | Sep 2001 | B1 |
| 6307505 | Green | Oct 2001 | B1 |
| 6313788 | Wilson | Nov 2001 | B1 |
| 6314348 | Winslow | Nov 2001 | B1 |
| 6325684 | Knight | Dec 2001 | B1 |
| 6336066 | Pellenc | Jan 2002 | B1 |
| 6345231 | Quincke | Feb 2002 | B2 |
| 6356602 | Rodal | Mar 2002 | B1 |
| 6377889 | Soest | Apr 2002 | B1 |
| 6380888 | Kucik | Apr 2002 | B1 |
| 6389345 | Phelps | May 2002 | B2 |
| 6392589 | Rogers | May 2002 | B1 |
| 6397147 | Whitehead | May 2002 | B1 |
| 6415229 | Diekhans | Jul 2002 | B1 |
| 6418031 | Archambeault | Jul 2002 | B1 |
| 6421003 | Riley | Jul 2002 | B1 |
| 6424915 | Fukuda | Jul 2002 | B1 |
| 6431576 | Viaud | Aug 2002 | B1 |
| 6434462 | Bevly | Aug 2002 | B1 |
| 6445983 | Dickson | Sep 2002 | B1 |
| 6445990 | Manring | Sep 2002 | B1 |
| 6449558 | Small | Sep 2002 | B1 |
| 6463091 | Zhodzicshsky | Oct 2002 | B1 |
| 6463374 | Keller | Oct 2002 | B1 |
| 6466871 | Reisman | Oct 2002 | B1 |
| 6469663 | Whitehead | Oct 2002 | B1 |
| 6484097 | Fuchs | Nov 2002 | B2 |
| 6498570 | Ross | Dec 2002 | B2 |
| 6501422 | Nichols | Dec 2002 | B1 |
| 6515619 | McKay, Jr. | Feb 2003 | B1 |
| 6516271 | Upadhyaya | Feb 2003 | B2 |
| 6539303 | McClure | Mar 2003 | B2 |
| 6542077 | Joao | Apr 2003 | B2 |
| 6549835 | Deguchi | Apr 2003 | B2 |
| 6553299 | Keller | Apr 2003 | B1 |
| 6553300 | Ma | Apr 2003 | B2 |
| 6553311 | Ahearn | Apr 2003 | B2 |
| 6570534 | Cohen | May 2003 | B2 |
| 6577952 | Geier | Jun 2003 | B2 |
| 6587761 | Kumar | Jul 2003 | B2 |
| 6606542 | Hauwiller | Aug 2003 | B2 |
| 6611228 | Toda | Aug 2003 | B2 |
| 6611754 | Klein | Aug 2003 | B2 |
| 6611755 | Coffee | Aug 2003 | B1 |
| 6622091 | Perlmutter | Sep 2003 | B2 |
| 6631394 | Ronkka | Oct 2003 | B1 |
| 6631916 | Miller | Oct 2003 | B1 |
| 6643576 | O'Connor | Nov 2003 | B1 |
| 6646603 | Dooley | Nov 2003 | B2 |
| 6657875 | Zheng | Dec 2003 | B1 |
| 6671587 | Hrovat | Dec 2003 | B2 |
| 6686878 | Lange | Feb 2004 | B1 |
| 6688403 | Bernhardt | Feb 2004 | B2 |
| 6703973 | Nichols | Mar 2004 | B1 |
| 6711501 | McClure | Mar 2004 | B2 |
| 6721638 | Zeitler | Apr 2004 | B2 |
| 6732024 | Wilhelm Rekow | May 2004 | B2 |
| 6744404 | Whitehead | Jun 2004 | B1 |
| 6754584 | Pinto | Jun 2004 | B2 |
| 6774843 | Takahashi | Aug 2004 | B2 |
| 6789014 | Rekow et al. | Sep 2004 | B1 |
| 6792380 | Toda | Sep 2004 | B2 |
| 6819269 | Flick | Nov 2004 | B2 |
| 6819780 | Benson | Nov 2004 | B2 |
| 6822314 | Beasom | Nov 2004 | B2 |
| 6865465 | McClure | Mar 2005 | B2 |
| 6865484 | Miyasaka | Mar 2005 | B2 |
| 6876920 | Mailer | Apr 2005 | B1 |
| 6879283 | Bird | Apr 2005 | B1 |
| 6900992 | Kelly | May 2005 | B2 |
| 6922635 | Rorabaugh | Jul 2005 | B2 |
| 6931233 | Tso | Aug 2005 | B1 |
| 6961018 | Heppe | Nov 2005 | B2 |
| 6967538 | Woo | Nov 2005 | B2 |
| 6990399 | Hrazdera | Jan 2006 | B2 |
| 7006032 | King | Feb 2006 | B2 |
| 7026982 | Toda | Apr 2006 | B2 |
| 7027918 | Zimmerman | Apr 2006 | B2 |
| 7031725 | Rorabaugh | Apr 2006 | B2 |
| 7089099 | Shostak | Aug 2006 | B2 |
| 7142956 | Heiniger | Nov 2006 | B2 |
| 7155335 | Rennels | Dec 2006 | B2 |
| 7162348 | McClure | Jan 2007 | B2 |
| 7191061 | McKay | Mar 2007 | B2 |
| 7221314 | Brabec | May 2007 | B2 |
| 7231290 | Steichen | Jun 2007 | B2 |
| 7248211 | Hatch | Jul 2007 | B2 |
| 7271766 | Zimmerman | Sep 2007 | B2 |
| 7277784 | Weiss | Oct 2007 | B2 |
| 7277792 | Overschie | Oct 2007 | B2 |
| 7292186 | Miller | Nov 2007 | B2 |
| 7324915 | Altman | Jan 2008 | B2 |
| 7358896 | Gradincic | Apr 2008 | B2 |
| 7373231 | McClure | May 2008 | B2 |
| 7388539 | Whitehead et al. | Jun 2008 | B2 |
| 7395769 | Jensen | Jul 2008 | B2 |
| 7400956 | Feller et al. | Jul 2008 | B1 |
| 7428259 | Wang | Sep 2008 | B2 |
| 7437230 | McClure et al. | Oct 2008 | B2 |
| 7451030 | Eglington | Nov 2008 | B2 |
| 7460942 | Mailer | Dec 2008 | B2 |
| 7479900 | Horstemeyer | Jan 2009 | B2 |
| 7505848 | Flann | Mar 2009 | B2 |
| 7522099 | Zhodzicshsky | Apr 2009 | B2 |
| 7522100 | Yang | Apr 2009 | B2 |
| 7571029 | Dai | Aug 2009 | B2 |
| 7610123 | Han et al. | Oct 2009 | B2 |
| 7623952 | Unruh et al. | Nov 2009 | B2 |
| 7689354 | Heiniger | Mar 2010 | B2 |
| 7835832 | Macdonald et al. | Nov 2010 | B2 |
| 7854108 | Koselka et al. | Dec 2010 | B2 |
| 7885745 | McClure et al. | Feb 2011 | B2 |
| 8098324 | Ueno et al. | Jan 2012 | B2 |
| 8106817 | Bhattacharya et al. | Jan 2012 | B2 |
| 8190337 | McClure | May 2012 | B2 |
| 8311696 | Reeve | Nov 2012 | B2 |
| 8437901 | Anderson | May 2013 | B2 |
| 8649930 | Reeve et al. | Feb 2014 | B2 |
| 8768558 | Reeve | Jul 2014 | B2 |
| 20010004601 | Drane | Jun 2001 | A1 |
| 20010025221 | Klein | Sep 2001 | A1 |
| 20020004691 | Kinashi et al. | Jan 2002 | A1 |
| 20020067849 | Klassen | Jun 2002 | A1 |
| 20020072850 | McClure et al. | Jun 2002 | A1 |
| 20020138187 | Qiu | Sep 2002 | A1 |
| 20020161522 | Cohen | Oct 2002 | A1 |
| 20020165645 | Kageyama | Nov 2002 | A1 |
| 20020165669 | Pinto et al. | Nov 2002 | A1 |
| 20020171427 | Wiegert | Nov 2002 | A1 |
| 20030014171 | Ma | Jan 2003 | A1 |
| 20030093210 | Kondo | May 2003 | A1 |
| 20030187560 | Keller | Oct 2003 | A1 |
| 20030187577 | McClure | Oct 2003 | A1 |
| 20030208319 | Ell et al. | Nov 2003 | A1 |
| 20040006426 | Armstrong | Jan 2004 | A1 |
| 20040039514 | Steichen | Feb 2004 | A1 |
| 20040186644 | McClure et al. | Sep 2004 | A1 |
| 20040212533 | Whitehead | Oct 2004 | A1 |
| 20040221790 | Sinclair et al. | Nov 2004 | A1 |
| 20050055147 | Hrazdera | Mar 2005 | A1 |
| 20050080559 | Ishibashi | Apr 2005 | A1 |
| 20050110676 | Heppe et al. | May 2005 | A1 |
| 20050150160 | Norgaard et al. | Jul 2005 | A1 |
| 20050196162 | Mootz et al. | Sep 2005 | A1 |
| 20050225955 | Grebenkemper | Oct 2005 | A1 |
| 20050259240 | Goren | Nov 2005 | A1 |
| 20050265494 | Goodlings | Dec 2005 | A1 |
| 20060031664 | Wilson | Feb 2006 | A1 |
| 20060095172 | Abramovitch et al. | May 2006 | A1 |
| 20060103573 | Geier | May 2006 | A1 |
| 20060167600 | Nelson | Jul 2006 | A1 |
| 20060206246 | Walker | Sep 2006 | A1 |
| 20060213167 | Koselka et al. | Sep 2006 | A1 |
| 20060215739 | Williamson | Sep 2006 | A1 |
| 20060290779 | Reverte et al. | Dec 2006 | A1 |
| 20070055412 | Bernhard | Mar 2007 | A1 |
| 20070069924 | Goren | Mar 2007 | A1 |
| 20070078570 | Dai | Apr 2007 | A1 |
| 20070088447 | Stothert | Apr 2007 | A1 |
| 20070112700 | Den Haan | May 2007 | A1 |
| 20070121708 | Simpson | May 2007 | A1 |
| 20070193798 | Allard et al. | Aug 2007 | A1 |
| 20070194984 | Waid | Aug 2007 | A1 |
| 20070198185 | McClure et al. | Aug 2007 | A1 |
| 20070205940 | Yang | Sep 2007 | A1 |
| 20070247361 | Shoarinejad | Oct 2007 | A1 |
| 20070285308 | Bauregger | Dec 2007 | A1 |
| 20080039991 | May et al. | Feb 2008 | A1 |
| 20080059068 | Strelow et al. | Mar 2008 | A1 |
| 20080129586 | Martin | Jun 2008 | A1 |
| 20080167770 | Macdonald et al. | Jul 2008 | A1 |
| 20080204312 | Euler | Aug 2008 | A1 |
| 20080269988 | Feller et al. | Oct 2008 | A1 |
| 20080284643 | Scherzinger et al. | Nov 2008 | A1 |
| 20080288205 | Teoh et al. | Nov 2008 | A1 |
| 20090093959 | Scherzinger et al. | Apr 2009 | A1 |
| 20090160951 | Anderson et al. | Jun 2009 | A1 |
| 20090164067 | Whitehead et al. | Jun 2009 | A1 |
| 20090171583 | DiEsposti | Jul 2009 | A1 |
| 20090174587 | DeLellio | Jul 2009 | A1 |
| 20090174597 | DiLellio | Jul 2009 | A1 |
| 20090174622 | Kanou | Jul 2009 | A1 |
| 20090175593 | Hayakawa | Jul 2009 | A1 |
| 20090177395 | Stelpstra | Jul 2009 | A1 |
| 20090177399 | Park | Jul 2009 | A1 |
| 20090204281 | McClure et al. | Aug 2009 | A1 |
| 20090251366 | McClure et al. | Oct 2009 | A1 |
| 20090259397 | Stanton | Oct 2009 | A1 |
| 20090259707 | Martin | Oct 2009 | A1 |
| 20090262014 | DiEsposti | Oct 2009 | A1 |
| 20090262018 | Vasilyev | Oct 2009 | A1 |
| 20090262974 | Lithopoulos | Oct 2009 | A1 |
| 20090265054 | Basnayake | Oct 2009 | A1 |
| 20090265101 | Jow | Oct 2009 | A1 |
| 20090265104 | Shroff | Oct 2009 | A1 |
| 20090273372 | Brenner | Nov 2009 | A1 |
| 20090273513 | Huang | Nov 2009 | A1 |
| 20090274079 | Bhatia | Nov 2009 | A1 |
| 20090274113 | Katz | Nov 2009 | A1 |
| 20090276155 | Jeerage | Nov 2009 | A1 |
| 20090295633 | Pinto | Dec 2009 | A1 |
| 20090295634 | Yu | Dec 2009 | A1 |
| 20090299550 | Baker | Dec 2009 | A1 |
| 20090322597 | Medina Herrero | Dec 2009 | A1 |
| 20090322598 | Fly | Dec 2009 | A1 |
| 20090322600 | Whitehead | Dec 2009 | A1 |
| 20090322601 | Ladd | Dec 2009 | A1 |
| 20090322606 | Gronemeyer | Dec 2009 | A1 |
| 20090326809 | Colley | Dec 2009 | A1 |
| 20100013703 | Tekawy | Jan 2010 | A1 |
| 20100026569 | Amidi | Feb 2010 | A1 |
| 20100030470 | Wang | Feb 2010 | A1 |
| 20100039316 | Gronemeyer | Feb 2010 | A1 |
| 20100039318 | Kmiecik | Feb 2010 | A1 |
| 20100039320 | Boyer | Feb 2010 | A1 |
| 20100039321 | Abraham | Feb 2010 | A1 |
| 20100060518 | Bar-Sever | Mar 2010 | A1 |
| 20100063649 | Wu | Mar 2010 | A1 |
| 20100084147 | Aral | Apr 2010 | A1 |
| 20100085249 | Ferguson | Apr 2010 | A1 |
| 20100085253 | Ferguson | Apr 2010 | A1 |
| 20100103033 | Roh | Apr 2010 | A1 |
| 20100103034 | Tobe | Apr 2010 | A1 |
| 20100103038 | Yeh | Apr 2010 | A1 |
| 20100103040 | Broadbent | Apr 2010 | A1 |
| 20100106414 | Whitehead | Apr 2010 | A1 |
| 20100106445 | Kondoh | Apr 2010 | A1 |
| 20100109944 | Whitehead | May 2010 | A1 |
| 20100109945 | Roh | May 2010 | A1 |
| 20100109947 | Rintanen | May 2010 | A1 |
| 20100109948 | Razoumov | May 2010 | A1 |
| 20100109950 | Roh | May 2010 | A1 |
| 20100111372 | Zheng | May 2010 | A1 |
| 20100114483 | Heo | May 2010 | A1 |
| 20100117894 | Velde | May 2010 | A1 |
| 20100117899 | Papadimitratos | May 2010 | A1 |
| 20100117900 | van Diggelen | May 2010 | A1 |
| 20100121577 | Zhang | May 2010 | A1 |
| 20100124210 | Lo | May 2010 | A1 |
| 20100124212 | Lo | May 2010 | A1 |
| 20100134354 | Lennen | Jun 2010 | A1 |
| 20100149025 | Meyers | Jun 2010 | A1 |
| 20100149030 | Verma | Jun 2010 | A1 |
| 20100149033 | Abraham | Jun 2010 | A1 |
| 20100149034 | Chen | Jun 2010 | A1 |
| 20100149037 | Cho | Jun 2010 | A1 |
| 20100150284 | Fielder | Jun 2010 | A1 |
| 20100152949 | Nunan | Jun 2010 | A1 |
| 20100156709 | Zhang | Jun 2010 | A1 |
| 20100156712 | Pisz | Jun 2010 | A1 |
| 20100156718 | Chen | Jun 2010 | A1 |
| 20100159943 | Salmon | Jun 2010 | A1 |
| 20100161179 | McClure | Jun 2010 | A1 |
| 20100161211 | Chang | Jun 2010 | A1 |
| 20100161568 | Xiao | Jun 2010 | A1 |
| 20100171660 | Shyr | Jul 2010 | A1 |
| 20100171757 | Melamed | Jul 2010 | A1 |
| 20100185364 | McClure | Jul 2010 | A1 |
| 20100185366 | Heiniger | Jul 2010 | A1 |
| 20100185389 | Woodard | Jul 2010 | A1 |
| 20100188285 | Collins | Jul 2010 | A1 |
| 20100188286 | Bickerstaff | Jul 2010 | A1 |
| 20100189163 | Burgi | Jul 2010 | A1 |
| 20100201829 | Skoskiewicz et al. | Aug 2010 | A1 |
| 20100207811 | Lackey | Aug 2010 | A1 |
| 20100210206 | Young | Aug 2010 | A1 |
| 20100211248 | Craig | Aug 2010 | A1 |
| 20100211315 | Toda | Aug 2010 | A1 |
| 20100211316 | DaSilva | Aug 2010 | A1 |
| 20100220004 | Malkos | Sep 2010 | A1 |
| 20100220008 | Conover | Sep 2010 | A1 |
| 20100222076 | Poon | Sep 2010 | A1 |
| 20100225537 | Abraham | Sep 2010 | A1 |
| 20100228408 | Ford | Sep 2010 | A1 |
| 20100228480 | Lithgow | Sep 2010 | A1 |
| 20100230198 | Frank et al. | Sep 2010 | A1 |
| 20100231443 | Whitehead | Sep 2010 | A1 |
| 20100231446 | Marshall | Sep 2010 | A1 |
| 20100232351 | Chansarkar | Sep 2010 | A1 |
| 20100235093 | Chang | Sep 2010 | A1 |
| 20100238976 | Young | Sep 2010 | A1 |
| 20100241347 | King | Sep 2010 | A1 |
| 20100241353 | Park | Sep 2010 | A1 |
| 20100241441 | Page | Sep 2010 | A1 |
| 20100241864 | Kelley | Sep 2010 | A1 |
| 20100312475 | Cheng | Dec 2010 | A1 |
| 20110015817 | Reeve | Jan 2011 | A1 |
| 20110118938 | Macdonald | May 2011 | A1 |
| 20120182421 | Asanov | Jul 2012 | A1 |
| 20120251123 | Pederson | Oct 2012 | A1 |
| 20120271540 | Miksa et al. | Oct 2012 | A1 |
| 20120300070 | Ohtomo et al. | Nov 2012 | A1 |
| 20130004086 | Carlbom et al. | Jan 2013 | A1 |
| 20130041549 | Reeve et al. | Feb 2013 | A1 |
| 20130046461 | Balloga | Feb 2013 | A1 |
| 20130066542 | Chung | Mar 2013 | A1 |
| 20130107034 | Di Bernardo et al. | May 2013 | A1 |
| 20130121678 | Xin | May 2013 | A1 |
| 20130141565 | Ling | Jun 2013 | A1 |
| 20130147661 | Kangas et al. | Jun 2013 | A1 |
| 20130156271 | Cimino | Jun 2013 | A1 |
| 20130166103 | Ko | Jun 2013 | A1 |
| 20130211715 | Bae et al. | Aug 2013 | A1 |
| Number | Date | Country |
|---|---|---|
| 105531706 | Apr 2016 | CN |
| 07244150 | Sep 1995 | JP |
| 1998036288 | Aug 1998 | WO |
| 200024239 | May 2000 | WO |
| 2008119386 | Dec 2005 | WO |
| 2003019430 | Mar 2006 | WO |
| 2008080193 | Jul 2008 | WO |
| 2009066183 | May 2009 | WO |
| 2009082745 | Jul 2009 | WO |
| 2009126587 | Oct 2009 | WO |
| 2009148638 | Dec 2009 | WO |
| 2010005945 | Jan 2010 | WO |
| 2010104782 | Sep 2010 | WO |
| 2011014431 | Feb 2011 | WO |
| Entry |
|---|
| Park, Chansik et al., “Integer Ambiguity Resolution for GPS Based Attitude Determination System”; SICE Jul. 29-31, 1998; Chiba; pp. 1115-1120. |
| Ward, Phillip W., “Performance Comparisons Between FFL, PLL, and a Novel FLL-Assisted-PLL Carrier Tracking Loop under RF Interference Conditions”; 11th Int. Tech Meeting of the Satellite Division of the US Inst. Of Navigation, Nashville, TN; Sep. 15-18, 1998; pp. 783-795. |
| Bevly, David M., “Comparison of INS v. Carrier-Phase DGPS for Attitude Determination in the Control of Off-Road Vehicles”; Ion 55th Annual Meeting; Jun. 28-30, 1999; Cambridge, Massachusetts; pp. 497-504. |
| “ISO”, 11783 Part 7 Draft Amendment 1 Annex, Paragraphs B.6 and B.7.ISO 11783-7 2004 DAM1, ISO; Mar. 8, 2004. |
| “ARINC Engineering Services, Interface Specification IS-GPS-200, Revision D”, Online [retrieved on May 18, 2010]. Retrieved from the Internet: <http://www.navcen.uscg.gov/gps/geninfo/IS-GPS-2000.pdf> (Dec. 7, 2004) p. 168, para [0001]. |
| “Eurocontrol, Pegasus Technical Notes on SBAS”, report [online] Dec. 7, 2004 [retrieved on May 18, 2010], Retrieved from the Internet: <http://www.icao.int.icao/en/ro/nacc/meetings/2004/gnss/documentation/Pegasus/tn.pdg> (Dec. 7, 2004), p. 89, paras [0001]-[0004]. |
| International Search Report and Written Opinion for PCT/US2004/015678 dated Jun. 21, 2005. |
| Schaer, et al., “Determination and Use of GPS Differential Code Bias Values”; Presentation [online]. Retrieved May 18, 2010. Retrieved from the Internet: <http://nng.esoc.esa.de/ws206/REPR2.pdf> (May 8, 2006). |
| Rho, Hyundho et al., “Dual-Frequency GPS Precise Point Positioning with WADGPS Corrections” [retrieved on May 18, 2010] Retrieved from the Internet: <http://gauss.gge.unb.ca/papers.pdf/iongnss2005.rho.wadgps.pdf> (Jul. 12, 2006). |
| International Search Report for PCT/AU2008/000002 dated Feb. 28, 2008. |
| Takac, Frank, et al., “Smark RTK: A Novel Method of Processing Standardized RTCM Network RTK Information for High Precision Positioning”; Proceedings of ENC GNSS 2008; Toulouse, France; Apr. 22, 2008. |
| International Search Report and Written Opinion for PCT/US08/81727 dated Dec. 23, 2008. |
| International Search Report and Written Opinion for PCT/US08/88070 dated Feb. 9, 2009. |
| International Search Report for PCT/US09/33567 dated Feb. 9, 2009. |
| International Search Report for PCT/US09/33693 dated Mar. 30, 2009. |
| International Search Report for PCT/US09/039686 dated May 26, 2009. |
| International Search Report and Written Opinion for PCT/IB2008/003796 dated Jul. 15, 2009. |
| International Search Report for PCT/US09/49776 dated Aug. 11, 2009. |
| International Search Report for PCT/US09/34376 dated Nov. 2, 2009. |
| International Search Report for PCT/US09/60668 dated Dec. 9, 2009. |
| International Search Report and Written Opinion for PCT/US09/63594 dated Jan. 11, 2010. |
| International Search Report for PCT/US09/067693 dated Jan. 26, 2010. |
| International Search Report and Written Opinion for PCT/US10/21334 dated Mar. 12, 2010. |
| International Search Report and Written Opinion for PCT/US10/26509 dated Apr. 20, 2010; 7 pages. |
| International Search Report for PCT/US10/26509 dated Apr. 20, 2010. |
| International Search Report and Written Opinion for PCT/US2010/043094 dated Sep. 17, 2010. |
| Notification of Transmittal of International Preliminary Report on Patentability for PCT/US09/039686 dated Oct. 21, 2010. |
| Notification Concerning Transmittal of International Report on Patentability for PCT/US2009/049776 dated Jan. 20, 2011. |
| Notification of Publication of International Application for WO 2011/014431 dated Feb. 3, 2011. |
| Parkinson, Bradford W., et al., “Global Positioning System: Theory and Applications, vol. II”; Bradford W. Parkinson and James J. Spiker, Jr., eds., Global Positioning System: Theory and Applications, vol. II, 1995, AIAA, Reston, VA, USA, pp. 3-50 (1995), 3-50. |
| Kaplan, E.D., “Understanding GPS: Principles and Applications”; Artech House, MA, 1996. |
| Han, Shaowel, et al., “Single-Epoch Ambiguity Resolution for Real-Time GPS Attitude Determination with the Aid of one-Dimensional Optical Fiber Gyro”, GPA Solutions, vol. 3, No. 1; pp. 5-12 (1999); John Wiley & Sons, Inc. |
| Irsigler, M., et al., “PPL Tracking Performance in the Presence of Oscillator Phase Noise”; GPS Solutions, vol. 5, No. 4, pp. 45-57 (2002). |
| Xu, Jiangning et al., “An Ehw Architecture for Real-Time GPS Attitude Determination Based on Parallel Genetic Algorithm”, The Computer Society Proceedings of the 2002 NASA/DOD Conference on Evolvable Hardware (EH'02) (2002). |
| “Orthman Manufacturing Co., www.orthman.com/htm;guidance.htm”; 2004; regarding the “Tracer Quick-Hitch”. |
| Keicher, R. et al., “Automatic Guidance for Agricultural Vehicles in Europe”; Computers and Electronics in Agriculture, vol. 25; Jan. 2000; pp. 169-194. |
| Ling, Dai, et al., “Real-Time Attitude Determination from Microsatellite by Lamda Method Combined with Kalman Filtering”; A Collection for the 22nd AIAA International Communications Satellite Systems Conference and Exhibit Technical Papers vol. 1, Monterey, California American Institute of Aeronautics and Astronautics, Inc. (May 2004), pp. 136-143. |
| Last, J.D., et al., “Effect of skywave interference on coverage of radio beacon DGPS stations”; IEEE Proc.-Radar, Sonar Navig., vol. 144, No. 3; Jul. 1997; pp. 163-168. |
| Noh, Kwang-Mo, “Self-tuning Controller for Farm Tractor Guidance”; Digital Repository @ Iowa State University, Retrospective Theses and Dissertations. Paper 9874; (1990); 192 pages. |
| Schwabe Williamson & Wyatt Listing of Related Cases dated Jan. 31, 2017; 1 page. |
| Noh, Kwang-Mo, Self-tuning controller for farm tractor guidance, Iowa State University Retrospective Theses and Dissertations, Paper 9874, (1990). |
| Van Zuydam,. R.P., Centimeter-Precision Guidance of Agricultural Implements in the Open Field by Means of Real Tim Kinematic DGPS, ASA-CSSA-SSSA, pp. 1023-1034 (1999). |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 11620388 | Jan 2007 | US |
| Child | 12947620 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 12504779 | Jul 2009 | US |
| Child | 13573682 | US | |
| Parent | 12947620 | Nov 2010 | US |
| Child | 12504779 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 13573682 | Oct 2012 | US |
| Child | 15196824 | US |
