Patent Application
20240337753
The present disclosure relates to agricultural machines and to sensing systems and sensing methods for agricultural machines.
Research and development is underway to study smart agriculture with the effective use of ICT (Information and Communication Technology) and IoT (Internet of Things) as the next generation of agriculture. Research and development is also underway to study automated and unmanned work vehicles, such as tractors, for use in agricultural fields. For example, work vehicles capable of traveling in an automatic steering mode with the use of a positioning system such as GNSS (Global Navigation Satellite System), which is capable of precise positioning, have been put into practical use.
In addition, research and development is also underway to study the technique of conducting a search of a region around a work vehicle with the use of obstacle sensors to detect obstacles around the work vehicle. For example, Japanese Laid-Open Patent Publication No. 2019-175059 discloses the technique of detecting obstacles around a self-drivable tractor with the use of LiDAR (Light Detection and Ranging) sensors.
Example embodiments of the present invention provide techniques for a search of an environment around an agricultural machine, which is improved or optimized for the area in which the agricultural machine is located.
A sensing system according to an example embodiment of the present disclosure includes a LiDAR sensor to sense an environment around the agricultural machine to output sensing data, and a processor configured or programmed to detect an object in a search region around the agricultural machine based on the sensing data, and to cause a size of the search region for the detection of the object to be different between a case where the agricultural machine is located in a field and a case where the agricultural machine is located in an out-of-field area that is outside the field.
A sensing method according to an example embodiment of the present disclosure includes sensing an environment around the agricultural machine using a LiDAR sensor to output sensing data, detecting an object in a search region around the agricultural machine based on the sensing data, and causing a size of the search region for the detection of the object to be different between a case where the agricultural machine is located in a field and a case where the agricultural machine is located in an out-of-field area that is outside the field.
General or specific aspects of the present disclosure may be implemented using a device, a system, a method, an integrated circuit, a computer program, a non-transitory computer-readable storage medium, or any combination thereof. The computer-readable storage medium may be inclusive of a volatile storage medium or a non-volatile storage medium. The device may include a plurality of devices. In the case where the device includes two or more devices, the two or more devices may be disposed within a single apparatus, or divided over two or more separate apparatuses.
According to example embodiments of the present disclosure, the size of the search region for detection of objects is set to be different between a case where the agricultural machine is located in the field and a case where the agricultural machine is located in the out-of-field area. Due to this feature, the size of the search region can be improved or optimized for the area in which the work vehicle is located. When the size of the search region is increased, detection of objects can be performed over a larger area around the agricultural machine. When the size of the search region is decreased, the computational load in the object detection process can be reduced.
The and above other elements, features, steps, characteristics and advantages of the present invention will become more apparent from the following detailed description of the example embodiments with reference to the attached drawings.
In the present disclosure, an “agricultural machine” refers to a machine for agricultural applications. The agricultural machine of the present disclosure can be a mobile agricultural machine that is capable of performing agricultural work while traveling. Examples of agricultural machines include tractors, harvesters, rice transplanters, vehicles for crop management, vegetable transplanters, mowers, seeders, spreaders, and mobile robots for agriculture. Not only may a work vehicle such as a tractor function as an “agricultural machine” alone by itself, but also a combination of a work vehicle and an implement that is attached to, or towed by, the work vehicle may function as an “agricultural machine”. For the ground surface inside a field, the agricultural machine performs agricultural work such as tilling, seeding, preventive pest control, manure spreading, planting of crops, or harvesting. Such agricultural work or tasks may be referred to as “groundwork”, or simply as “work” or “tasks”. Travel of a vehicle-type agricultural machine performed while the agricultural machine also performs agricultural work may be referred to as “tasked travel”.
“Self-driving” refers to controlling the movement of an agricultural machine by the action of a controller, rather than through manual operations of a driver. An agricultural machine that performs self-driving may be referred to as a “self-driving agricultural machine” or a “robotic agricultural machine”. During self-driving, not only the movement of the agricultural machine, but also the operation of agricultural work (e.g., the operation of the implement) may be controlled automatically. In the case where the agricultural machine is a vehicle-type machine, travel of the agricultural machine via self-driving will be referred to as “self-traveling”. The controller may be configured or programmed to control at least one of steering that is required in the movement of the agricultural machine, adjustment of the moving speed, or beginning and ending of a move. In the case of controlling a work vehicle having an implement attached thereto, the controller may be configured or programmed to control raising or lowering of the implement, beginning and ending of an operation of the implement, and so on. A move based on self-driving may include not only moving of an agricultural machine that moves along a predetermined path toward a destination, but also moving of an agricultural machine that follows a target of tracking. An agricultural machine that performs self-driving may also move partly based on the user's instructions. Moreover, an agricultural machine that performs self-driving may operate not only in a self-driving mode but also in a manual driving mode, where the agricultural machine moves through manual operations of the driver. When performed not manually but through the action of a controller, the steering of an agricultural machine will be referred to as “automatic steering”. A portion of, or the entirety of, the controller may reside outside the agricultural machine. Control signals, commands, data, etc. may be communicated between the agricultural machine and a controller residing outside the agricultural machine. An agricultural machine that performs self-driving may move autonomously while sensing the surrounding environment, without any person being involved in the controlling of the movement of the agricultural machine. An agricultural machine that is capable of autonomous movement is able to travel inside the field or outside the field (e.g., on roads) in an unmanned manner. During an autonomous move, operations of detecting and avoiding obstacles may be performed.
A “work plan” is data defining a plan of one or more tasks of agricultural work to be performed by an agricultural machine. The work plan may include, for example, information representing the order of the tasks of agricultural work to be performed by an agricultural machine or the field where each of the tasks of agricultural work is to be performed. The work plan may include information representing the time and the date when each of the tasks of agricultural work is to be performed. The work plan may be created by a processor communicating with the agricultural machine to manage the agricultural machine or a processor mounted on the agricultural machine. The processor can be configured or programmed to create a work plan based on, for example, information input by the user (agricultural business executive, agricultural worker, etc.) manipulating a terminal device. In this specification, the processor communicating with the agricultural machine to manage the agricultural machine will be referred to as a “management device”. The management device may manage agricultural work of a plurality agricultural machines. In this case, the management device may create a work plan including information on each task of agricultural work to be performed by each of the plurality of agricultural machines. The work plan may be downloaded to each of the agricultural machines and stored in a storage in each of the agricultural machines. In order to perform the scheduled agricultural work in accordance with the work plan, each agricultural machine can automatically move to a field and perform the agricultural work.
An “environment map” is data representing, with a predetermined coordinate system, the position or the region of an object existing in the environment where the agricultural machine moves. The environment map may be referred to simply as a “map” or “map data”. The coordinate system defining the environment map is, for example, a world coordinate system such as a geographic coordinate system fixed to the globe. Regarding the object existing in the environment, the environment map may include information other than the position (e.g., attribute information or other types of information). The “environment map” encompasses various type of maps such as a point cloud map and a lattice map. Data on a local map or a partial map that is generated or processed in a process of constructing the environment map is also referred to as a “map” or “map data”.
An “agricultural road” is a road used mainly for agriculture. An “agricultural road” is not limited to a road paved with asphalt, and encompasses unpaved roads covered with soil, gravel or the like. An “agricultural road” encompasses roads (including private roads) on which only vehicle-type agricultural machines (e.g., work vehicles such as tractors, etc.) are allowed to travel and roads on which general vehicles (automobiles, trucks, buses, etc.) are also allowed to travel. The work vehicles may automatically travel on a general road in addition to an agricultural road. The “general road” is a road maintained for traffic of general vehicles.
Hereinafter, example embodiments of the present disclosure will be described. Note, however, that unnecessarily detailed descriptions may be omitted. For example, detailed descriptions on what is well known in the art or redundant descriptions on what is substantially the same configuration may be omitted. This is to avoid lengthy description, and facilitate the understanding of those skilled in the art. The accompanying drawings and the following description, which are provided by the present inventors so that those skilled in the art can sufficiently understand the present disclosure, are not intended to limit the scope of the claims. In the following description, elements having identical or similar functions are denoted by identical reference numerals.
The following example embodiments are only exemplary, and the techniques according to the present disclosure are not limited to the following example embodiments. For example, numerical values, shapes, materials, steps, orders of steps, layout of a display screen, etc. which are indicated in the following example embodiments are only exemplary, and admit of various modifications so long as it makes technological sense. Any one implementation may be combined with another so long as it makes technological sense to do so.
Hereinafter, example embodiments in which techniques according to the present disclosure are applied to a work vehicle, such as a tractor, which is an example of agricultural machine, will be mainly described. The techniques and example embodiments according to the present disclosure are also applicable to other types of agricultural machines in addition to the work vehicle such as a tractor.
The work vehicle 100 according to the present example embodiment is a tractor. The work vehicle 100 can have an implement attached to its rear and/or its front. While performing agricultural work in accordance with the type of the implement, the work vehicle 100 is able to travel inside a field. The work vehicle 100 may travel inside the field or outside the field with no implement being attached thereto.
The work vehicle 100 has a self-driving function. In other words, the work vehicle 100 can travel by the action of a controller, rather than manually. The controller according to the present example embodiment is provided inside the work vehicle 100, and is configured or programmed to control both the speed and steering of the work vehicle 100. The work vehicle 100 can perform self-traveling outside the field (e.g., on roads) as well as inside the field.
The work vehicle 100 includes a device usable for positioning or localization, such as a GNSS receiver or an LiDAR sensor. Based on the position of the work vehicle 100 and information on a target path, the controller of the work vehicle 100 causes the work vehicle 100 to automatically travel. In addition to controlling the travel of the work vehicle 100, the controller is also configured or programmed to control the operation of the implement. As a result, while automatically traveling inside the field, the work vehicle 100 is able to perform agricultural work by using the implement. In addition, the work vehicle 100 is able to automatically travel along the target path on a road outside the field (e.g., an agricultural road or a general road). The work vehicle 100 travels in a self-traveling mode along roads outside the field with the effective use of data output from sensors, such as the cameras 120, the obstacle sensors 130 and the LiDAR sensor 140.
The management device 600 includes a computer to manage the agricultural work performed by the work vehicle 100. The management device 600 may be, for example, a server computer that performs centralized management on information regarding the field on the cloud and supports agriculture by use of the data on the cloud. The management device 600, for example, creates a work plan for the work vehicle 100 and causes the work vehicle 100 to execute agricultural work in accordance with the work plan. The management device 600 generates a target path in the field based on, for example, the information entered by a user using the terminal unit 400 or any other device. The management device 600 may generate and edit an environment map based on data collected by the work vehicle 100 or any other movable body by use of the sensor such as a LiDAR sensor. The management device 600 transmits data on the work plan, the target path and the environment map thus generated to the work vehicle 100. The work vehicle 100 automatically moves and performs agricultural work based on the data.
The terminal device 400 includes a computer that is used by a user who is at a remote place from the work vehicle 100. The terminal device 400 shown in
Hereinafter, a configuration and an operation of the system according to the present example embodiment will be described in more detail.
As shown in
The work vehicle 100 can include at least one sensor to sense the environment around the work vehicle 100 and a processor to process sensing data output from the at least one sensor. In the example shown in
The cameras 120 may be provided at the front/rear/right/left of the work vehicle 100, for example. The cameras 120 image the environment around the work vehicle 100 and generate image data. The images acquired by the cameras 120 can be output to a processor included in the work vehicle 100 and transmitted to the terminal device 400, which is responsible for remote monitoring. Also, the images may be used to monitor the work vehicle 100 during unmanned driving. The cameras 120 may also be used to generate images to allow the work vehicle 100, traveling on a road outside the field (an agricultural road or a general road), to recognize objects, obstacles, white lines, road signs, traffic signs or the like in the surroundings of the work vehicle 100.
The LiDAR sensor 140 in the example shown in
The plurality of obstacle sensors 130 shown in
The work vehicle 100 further includes a GNSS unit 110. The GNSS unit 110 includes a GNSS receiver. The GNSS receiver may include an antenna to receive a signal(s) from a GNSS satellite(s) and a processor to calculate the position of the work vehicle 100 based on the signal(s) received by the antenna. The GNSS unit 110 receives satellite signals transmitted from the plurality of GNSS satellites, and performs positioning based on the satellite signals. GNSS is the general term for satellite positioning systems such as GPS (Global Positioning System), QZSS (Quasi-Zenith Satellite System; e.g., MICHIBIKI), GLONASS, Galileo, and BeiDou. Although the GNSS unit 110 according to the present example embodiment is disposed above the cabin 105, it may be disposed at any other position.
The GNSS unit 110 may include an inertial measurement unit (IMU). Signals from the IMU can be used to complement position data. The IMU can measure a tilt or a small motion of the work vehicle 100. The data acquired by the IMU can be used to complement the position data based on the satellite signals, so as to improve the performance of positioning.
The controller of the work vehicle 100 may be configured or programmed to utilize, for positioning, the sensing data acquired by the sensors such as the cameras 120 and/or the LIDAR sensor 140, in addition to the positioning results provided by the GNSS unit 110. In the case where objects serving as characteristic points exist in the environment that is traveled by the work vehicle 100, as in the case of an agricultural road, a forest road, a general road or an orchard, the position and the orientation of the work vehicle 100 can be estimated with a high accuracy based on data that is acquired by the cameras 120 and/or the LiDAR sensor 140 and on an environment map that is previously stored in the storage. By correcting or complementing position data based on the satellite signals using the data acquired by the cameras 120 and/or the LiDAR sensor 140, it becomes possible to identify the position of the work vehicle 100 with a higher accuracy.
The prime mover 102 may be a diesel engine, for example. Instead of a diesel engine, an electric motor may be used. The transmission 103 can change the propulsion and the moving speed of the work vehicle 100 through a speed changing mechanism. The transmission 103 can also switch between forward travel and backward travel of the work vehicle 100.
The steering device 106 includes a steering wheel, a steering shaft connected to the steering wheel, and a power steering device to assist in the steering by the steering wheel. The front wheels 104F are the steered wheels, such that changing their angle of turn (also referred to as “steering angle”) can cause a change in the traveling direction of the work vehicle 100. The steering angle of the front wheels 104F can be changed by manipulating the steering wheel. The power steering device includes a hydraulic device or an electric motor to supply an assisting force to change the steering angle of the front wheels 104F. When automatic steering is performed, under the control of the controller disposed in the work vehicle 100, the steering angle may be automatically adjusted by the power of the hydraulic device or the electric motor.
A linkage device 108 is provided at the rear of the vehicle body 101. The linkage device 108 includes, e.g., a three-point linkage (also referred to as a “three-point link” or a “three-point hitch”), a PTO (Power Take Off) shaft, a universal joint, and a communication cable. The linkage device 108 allows the implement 300 to be attached to, or detached from, the work vehicle 100. The linkage device 108 is able to raise or lower the three-point link with a hydraulic device, for example, thus changing the position and/or attitude of the implement 300. Moreover, motive power can be sent from the work vehicle 100 to the implement 300 via the universal joint. While towing the implement 300, the work vehicle 100 allows the implement 300 to perform a predetermined task. The linkage device may be provided at the front portion of the vehicle body 101. In that case, the implement 300 can be connected with the front portion of the work vehicle 100.
Although the implement 300 shown in
The work vehicle 100 shown in
In addition to the GNSS unit 110, the cameras 120, the obstacle sensors 130, the LiDAR sensor 140 and the operational terminal 200, the work vehicle 100 in the example of
The GNSS receiver 111 in the GNSS unit 110 receives satellite signals transmitted from the plurality of GNSS satellites and generates GNSS data based on the satellite signals. The GNSS data is generated in a predetermined format such as, for example, the NMEA-0183 format. The GNSS data may include, for example, the identification number, the angle of elevation, the azimuth angle, and a value representing the reception strength of each of the satellites from which the satellite signals are received.
The GNSS unit 110 shown in
Note that the positioning method is not limited to being performed by use of an RTK-GNSS; any arbitrary positioning method (e.g., an interferometric positioning method or a relative positioning method) that provides positional data with the necessary accuracy can be used. For example, positioning may be performed by utilizing a VRS (Virtual Reference Station) or a DGPS (Differential Global Positioning System). In the case where positional data with the necessary accuracy can be obtained without the use of the correction signal transmitted from the reference station 60, positional data may be generated without using the correction signal. In that case, the GNSS unit 110 does not need to include the RTK receiver 112.
Even in the case where the RTK-GNSS is used, at a site where the correction signal from the reference station 60 cannot be acquired (e.g., on a road far from the field), the position of the work vehicle 100 is estimated by another method with no use of the signal from the RTK receiver 112. For example, the position of the work vehicle 100 may be estimated by matching the data output from the LiDAR sensor 140 and/or the cameras 120 against a highly accurate environment map.
The GNSS unit 110 according to the present example embodiment further includes the IMU 115. The IMU 115 may include a 3-axis accelerometer and a 3-axis gyroscope. The IMU 115 may include a direction sensor such as a 3-axis geomagnetic sensor. The IMU 115 functions as a motion sensor which can output signals representing parameters such as acceleration, velocity, displacement, and attitude of the work vehicle 100. Based not only on the satellite signals and the correction signal but also on a signal that is output from the IMU 115, the processing circuit 116 can estimate the position and orientation of the work vehicle 100 with a higher accuracy. The signal that is output from the IMU 115 may be used for the correction or complementation of the position that is calculated based on the satellite signals and the correction signal. The IMU 115 outputs a signal more frequently than the GNSS receiver 111. Utilizing this signal that is output highly frequently, the processing circuit 116 allows the position and orientation of the work vehicle 100 to be measured more frequently (e.g., about 10 Hz or above). Instead of the IMU 115, a 3-axis accelerometer and a 3-axis gyroscope may be separately provided. The IMU 115 may be provided as a separate device from the GNSS unit 110.
The cameras 120 are imagers that image the environment around the work vehicle 100. Each of the cameras 120 includes an image sensor such as a CCD (Charge Coupled Device) or a CMOS (Complementary Metal Oxide Semiconductor), for example. In addition, each camera 120 may include an optical system including one or more lenses and a signal processing circuit. During travel of the work vehicle 100, the cameras 120 image the environment around the work vehicle 100, and generate image data (e.g., motion picture data). The cameras 120 are able to capture motion pictures at a frame rate of 3 frames/second (fps: frames per second) or greater, for example. The images generated by the cameras 120 may be used by a remote supervisor to check the environment around the work vehicle 100 with the terminal device 400, for example. The images generated by the cameras 120 may also be used for the purpose of positioning and/or detection of obstacles. As shown in
The obstacle sensors 130 detect objects existing around the work vehicle 100. Each of the obstacle sensors 130 may include a laser scanner or an ultrasonic sonar, for example. When an object exists at a position within a predetermined distance from one of the obstacle sensors 130, the obstacle sensor 130 outputs a signal indicating the presence of the obstacle. The plurality of obstacle sensors 130 may be provided at different positions on the work vehicle 100. For example, a plurality of laser scanners and a plurality of ultrasonic sonars may be disposed at different positions on the work vehicle 100. Providing such a great number of obstacle sensors 130 can reduce blind spots in monitoring obstacles around the work vehicle 100.
The steering wheel sensor 152 measures the angle of rotation of the steering wheel of the work vehicle 100. The angle-of-turn sensor 154 measures the angle of turn of the front wheels 104F, which are the steered wheels. Measurement values by the steering wheel sensor 152 and the angle-of-turn sensor 154 are used for steering control by the controller 180.
The axle sensor 156 measures the rotational speed, i.e., the number of revolutions per unit time, of a axle that is connected to the wheels 104. The axle sensor 156 may be a sensor including a magnetoresistive element (MR), a Hall generator, or an electromagnetic pickup, for example. The axle sensor 156 outputs a numerical value indicating the number of revolutions per minute (unit: rpm) of the axle, for example. The axle sensor 156 is used to measure the speed of the work vehicle 100.
The drive device 240 includes various types of devices required to cause the work vehicle 100 to travel and to drive the implement 300; for example, the prime mover 102, the transmission 103, the steering device 106, the linkage device 108 and the like described above. The prime mover 102 may include an internal combustion engine such as, for example, a diesel engine. The drive device 240 may include an electric motor for traction instead of, or in addition to, the internal combustion engine.
The buzzer 220 is an audio output device to present an alarm sound to alert the user of an abnormality. For example, the buzzer 220 may present an alarm sound when an obstacle is detected during self-driving. The buzzer 220 is controlled by the controller 180.
The processor 161 may be a microprocessor or a microcontroller. The processor 161 is configured or programmed to process sensing data output from sensors, such as the cameras 120, the obstacle sensors 130 and the LiDAR sensor 140. For example, the processor 161 is configured or programmed to detect objects around the work vehicle 100 based on data output from the cameras 120, the obstacle sensors 130 and the LiDAR sensor 140.
The storage 170 includes one or more storage mediums such as a flash memory or a magnetic disc. The storage 170 stores various data that is generated by the GNSS unit 110, the cameras 120, the obstacle sensors 130, the LiDAR sensor 140, the sensors 150, and the controller 180. The data that is stored by the storage 170 may include map data on the environment where the work vehicle 100 travels (environment map) and data on a target path for self-driving. The environment map includes information on a plurality of fields where the work vehicle 100 performs agricultural work and roads around the fields. The environment map and the target path may be generated by a processor in the management device 600. The controller 180 may be configured or programmed to perform a function of generating or editing an environment map and a target path. The controller 180 can be configured or programmed to edit the environment map and the target path, acquired from the management device 600, in accordance with the environment where the work vehicle 100 travels. The storage 170 also stores data on a work plan received by the communication device 190 from the management device 600.
The storage 170 also stores a computer program(s) to cause the processor 161 and each of the ECUs in the controller 180 to perform various operations described below. Such a computer program(s) may be provided to the work vehicle 100 via a storage medium (e.g., a semiconductor memory, an optical disc, etc.) or through telecommunication lines (e.g., the Internet). Such a computer program(s) may be marketed as commercial software.
The controller 180 includes the plurality of ECUs. The plurality of ECUs include, for example, the ECU 181 for speed control, the ECU 182 for steering control, the ECU 183 for implement control, the ECU 184 for self-driving control, and the ECU 185 for path generation.
The ECU 181 controls the prime mover 102, the transmission 103 and brakes included in the drive device 240, thus controlling the speed of the work vehicle 100.
The ECU 182 controls the hydraulic device or the electric motor included in the steering device 106 based on a measurement value of the steering wheel sensor 152, thus controlling the steering of the work vehicle 100.
In order to cause the implement 300 to perform a desired operation, the ECU 183 controls the operations of the three-point link, the PTO shaft and the like that are included in the linkage device 108. Also, the ECU 183 generates a signal to control the operation of the implement 300, and transmits this signal from the communication device 190 to the implement 300.
Based on data output from the GNSS unit 110, the cameras 120, the obstacle sensors 130, the LiDAR sensor 140, the sensors 150 and the processor 161, the ECU 184 performs computation and control for achieving self-driving. For example, the ECU 184 specifies the position of the work vehicle 100 based on the data output from at least one of the GNSS unit 110, the cameras 120 and the LiDAR sensor 140. Inside the field, the ECU 184 may determine the position of the work vehicle 100 based only on the data output from the GNSS unit 110. The ECU 184 may estimate or correct the position of the work vehicle 100 based on the data acquired by the cameras 120 and/or the LiDAR sensor 140. Use of the data acquired by the cameras 120 and/or the LiDAR sensor 140 allows the accuracy of the positioning to be further improved. Outside the field, the ECU 184 estimates the position of the work vehicle 100 by use of the data output from the LiDAR sensor 140 and/or the cameras 120. For example, the ECU 184 may estimate the position of the work vehicle 100 by matching the data output from the LiDAR sensor 140 and/or the cameras 120 against the environment map. During self-driving, the ECU 184 performs computation necessary for the work vehicle 100 to travel along a target path, based on the estimated position of the work vehicle 100. The ECU 184 sends the ECU 181 a command to change the speed, and sends the ECU 182 a command to change the steering angle. In response to the command to change the speed, the ECU 181 controls the prime mover 102, the transmission 103 or the brakes to change the speed of the work vehicle 100. In response to the command to change the steering angle, the ECU 182 controls the steering device 106 to change the steering angle.
The ECU 185 can determine a destination of the work vehicle 100 based on the work plan stored in the storage 170, and determine a target path from a beginning point to a target point of the movement of the work vehicle 100. The ECU 185 may perform the process of detecting objects around the work vehicle 100 based on the data output from the cameras 120, the obstacle sensors 130 and the LiDAR sensor 140.
Through the actions of these ECUs, the controller 180 realizes self-driving. During self-driving, the controller 180 controls the drive device 240 based on the measured or estimated position of the work vehicle 100 and on the target path. As a result, the controller 180 can cause the work vehicle 100 to travel along the target path.
The plurality of ECUs included in the controller 180 can communicate with each other in accordance with a vehicle bus standard such as, for example, a CAN (Controller Area Network). Instead of the CAN, faster communication methods such as Automotive Ethernet (registered trademark) may be used. Although the ECUs 181 to 185 are illustrated as individual blocks in
The communication device 190 is a device including a circuit communicating with the implement 300, the terminal device 400 and the management device 600. The communication device 190 includes circuitry to perform exchanges of signals complying with an ISOBUS standard such as ISOBUS-TIM, for example, between itself and the communication device 390 of the implement 300. This allows the implement 300 to perform a desired operation, or allows information to be acquired from the implement 300. The communication device 190 may further include an antenna and a communication circuit to exchange signals via the network 80 with communication devices of the terminal device 400 and the management device 600. The network 80 may include a 3G, 4G, 5G, or any other cellular mobile communications network and the Internet, for example. The communication device 190 may have a function of communicating with a mobile terminal that is used by a supervisor who is situated near the work vehicle 100. With such a mobile terminal, communication may be performed based on any arbitrary wireless communication standard, e.g., Wi-Fi (registered trademark), 3G, 4G, 5G or any other cellular mobile communication standard, or Bluetooth (registered trademark).
The operational terminal 200 is a terminal for the user to perform a manipulation related to the travel of the work vehicle 100 and the operation of the implement 300, and is also referred to as a virtual terminal (VT). The operational terminal 200 may include a display device such as a touch screen panel, and/or one or more buttons. The display device may be a display such as a liquid crystal display or an organic light-emitting diode (OLED) display, for example. By manipulating the operational terminal 200, the user can perform various manipulations, such as, for example, switching ON/OFF the self-driving mode, recording or editing an environment map, setting a target path, and switching ON/OFF the implement 300. At least a portion of these manipulations may also be realized by manipulating the operation switches 210. The operational terminal 200 may be configured so as to be detachable from the work vehicle 100. A user who is at a remote place from the work vehicle 100 may manipulate the detached operational terminal 200 to control the operation of the work vehicle 100. Instead of the operational terminal 200, the user may manipulate a computer on which necessary application software is installed, for example, the terminal device 400, to control the operation of the work vehicle 100.
The drive device 340 in the implement 300 shown in
Now, a configuration of the management device 600 and the terminal device 400 will be described with reference to
The management device 600 includes a storage 650, a processor 660, a ROM (Read Only Memory) 670, a RAM (Random Access Memory) 680, and a communication device 690. These component elements are communicably connected to each other via a bus. The management device 600 may function as a cloud server to manage the schedule of the agricultural work to be performed by the work vehicle 100 in a field and support agriculture by use of the data managed by the management device 600 itself. The user can input information necessary to create a work plan by use of the terminal device 400 and upload the information to the management device 600 via the network 80. The management device 600 can create a schedule of agricultural work, that is, a work plan based on the information. The management device 600 can further generate or edit an environment map. The environment map may be distributed from a computer external to the management device 600.
The communication device 690 is a communication module to communicate with the work vehicle 100 and the terminal device 400 via the network 80. The communication device 690 can perform wired communication in compliance with communication standards such as, for example, IEEE1394 (registered trademark) or Ethernet (registered trademark). The communication device 690 may perform wireless communication in compliance with Bluetooth (registered trademark) or Wi-Fi, or cellular mobile communication based on 3G, 4G, 5G or any other cellular mobile communication standard.
The processor 660 may be, for example, a semiconductor integrated circuit including a central processing unit (CPU). The processor 660 may be realized by a microprocessor or a microcontroller. Alternatively, the processor 660 may be realized by an FPGA (Field Programmable Gate Array), a GPU (Graphics Processing Unit), an ASIC (Application Specific Integrated Circuit) or an ASSP (Application Specific Standard Product) each including a CPU, or a combination of two or more selected from these circuits. The processor 660 is configured or programmed to consecutively execute a computer program, describing commands to execute at least one process, stored in the ROM 670 and thus realizes a desired process.
The ROM 670 is, for example, a writable memory (e.g., PROM), a rewritable memory (e.g., flash memory) or a memory which can only be read from but cannot be written to. The ROM 670 stores a program to control operations of the processor 660. The ROM 670 does not need to be a single storage medium, and may be an assembly of a plurality of storage mediums. A portion of the assembly of the plurality of storage memories may be a detachable memory.
The RAM 680 provides a work area in which the control program stored in the ROM 670 is once developed at the time of boot. The RAM 680 does not need to be a single storage medium, and may be an assembly of a plurality of storage mediums.
The storage 650 mainly functions as a storage for a database. The storage 650 may be, for example, a magnetic storage or a semiconductor storage. An example of the magnetic storage is a hard disc drive (HDD). An example of the semiconductor storage is a solid state drive (SSD). The storage 650 may be a device independent from the management device 600. For example, the storage 650 may be a storage connected to the management device 600 via the network 80, for example, a cloud storage.
The terminal device 400 includes an input device 420, a display device 430, a storage 450, a processor 460, a ROM 470, a RAM 480, and a communication device 490. These component elements are communicably connected to each other via a bus. The input device 420 is a device to convert an instruction from the user into data and input the data to a computer. The input device 420 may be, for example, a keyboard, a mouse or a touch panel. The display device 430 may be, for example, a liquid crystal display or an organic EL display. The processor 460, the ROM 470, the RAM 480, the storage 450 and the communication device 490 are substantially the same as the corresponding component elements described above regarding the example of the hardware configuration of the management device 600, and will not be described in repetition.
Now, an operation of the work vehicle 100, the terminal device 400 and the management device 600 will be described.
First, an example operation of self-traveling of the work vehicle 100 will be described. The work vehicle 100 according to the present example embodiment can automatically travel both inside and outside a field. Inside the field, the work vehicle 100 drives the implement 300 to perform predetermined agricultural work while traveling along a preset target path. When detecting an obstacle while traveling inside the field, the work vehicle 100 halts traveling and performs operations of presenting an alarm sound from the buzzer 220, transmitting an alert signal to the terminal device 400 and the like. Inside the field, the positioning of the work vehicle 100 is performed based mainly on data output from the GNSS unit 110. Meanwhile, outside the field, the work vehicle 100 automatically travels along a target path set for an agricultural road or a general road outside the field. While traveling outside the field, the work vehicle 100 travels with the effective use of data acquired by the cameras 120 and/or the LiDAR sensor 140. When an obstacle is detected outside the field, the work vehicle 100 avoids the obstacle or halts at the point. Outside the field, the position of the work vehicle 100 is estimated based on data output from the LiDAR sensor 140 and/or the cameras 120 in addition to positioning data output from the GNSS unit 110.
Hereinafter, an example of the operation performed when the work vehicle 100 performs self-traveling in the field is described.
Now, an example control by the controller 180 during self-driving inside the field will be described.
In the example shown in
Hereinafter, with reference to
As shown in
As shown in
As shown in
As shown in
For the steering control and speed control of the work vehicle 100, control techniques such as PID control or MPC (Model Predictive Control) may be applied. Applying these control techniques will achieve smoothness of the control of bringing the work vehicle 100 closer to the target path P.
Note that, when an obstacle is detected by sensors such as the cameras 120, the obstacle sensors 130 and the LiDAR sensor 140 during travel, the controller 180 halts the work vehicle 100. At this point, the controller 180 may cause the buzzer 220 to present an alarm sound or may transmit an alert signal to the terminal device 400. In the case where the obstacle is avoidable, the controller 180 may control the drive device 240 such that the obstacle is avoided.
The work vehicle 100 according to the present example embodiment can perform self-traveling outside a field as well as inside the field. Outside the field, the processor 161 and/or the controller 180 is able to detect objects around the work vehicle 100 (e.g., another vehicle, a pedestrian, etc.) based on data output from sensors such as the cameras 120, the obstacle sensors 130 and the LiDAR sensor 140. Using the cameras 120 and the LiDAR sensor 140 enables detection of an object located at a relatively distant position from the work vehicle 100. The controller 180 performs speed control and steering control such that the work vehicle 100 avoids the detected object, thereby realizing self-traveling on a road outside the field.
As described above, the work vehicle 100 according to the present example embodiment can automatically travel inside the field and outside the field in an unmanned manner.
Next, the process of setting search regions in accordance with the area in which an agricultural machine is located is described.
As previously described, the sensors, such as cameras 120, obstacle sensors 130 and LiDAR sensor 140, sense an environment around the work vehicle 100 to output sensing data. The processor 161 (
In examples which will be described below, in the process of detecting objects using the sensing data output from the LiDAR sensor 140, the size of the search region is changed in accordance with the area in which the work vehicle 100 is located.
The work vehicle 100 of the present example embodiment includes a sensing system 10 (
The LiDAR sensor 140 is capable of sequentially emitting pulses of a laser beam (hereinafter, abbreviated as “laser pulses”) while changing the direction of emission and measuring the distance to a point of reflection of each laser pulse based on the difference between the time of emission of each laser pulse and the time of reception of reflection of the laser pulse. The “point of reflection” can be an object in an environment around the work vehicle 100.
The LiDAR sensor 140 can measure the distance from the LiDAR sensor 140 to an object by an arbitrary method. Examples of the measurement method of the LiDAR sensor 140 include mechanical rotation, MEMS, and phased array type measurement methods. These measurement methods are different in the method of emitting laser pulses (the method of scanning). For example, the mechanical rotation type LiDAR sensor rotates a cylindrical head, which is for emitting laser pulses and detecting reflection of the laser pulses, to scan the surrounding environment in all directions, i.e., 360 degrees, around the axis of rotation. The MEMS type LiDAR sensor uses a MEMS mirror to oscillate the emission direction of laser pulses and scans the surrounding environment in a predetermined angular range centered on the oscillation axis. The phased array type LiDAR sensor controls the phase of light to oscillate the emission direction of the light and scans the surrounding environment in a predetermined angular range centered on the oscillation axis.
Similarly to the process of Step S121 (
The processor 161 uses the map data to determine the area corresponding to the geographic coordinates indicated by the position data (Step S202). The area corresponding to the geographic coordinates indicated by the position data corresponds to the area in which the work vehicle 100 is located. The processor 161 determines whether the area corresponding to the geographic coordinates indicated by the position data is inside the field 70 or outside the field 70 (Step S203).
If the area corresponding to the geographic coordinates indicated by the position data is an area outside the field 70, the processor 161 sets the first search region 710 as the search region (Step S205). If the area corresponding to the geographic coordinates indicated by the position data is an area inside the field 70, the processor 161 sets the second search region 720 as the search region (Step S204).
When the agricultural machine 100 is located in the area enclosed by the boundaries 70a, 70b, 70c, 70d of the field 70, the processor 161 sets the second search region 720 as the search region. When the agricultural machine 100 is located on land where there is a road 76 or building 77, the processor 161 sets the first search region 710 as the search region. When the agricultural machine 100 moves from the field 70 to the out-of-field area or from the out-of-field area to the field 70, the processor 161 switches the search region between the first search region 710 and the second search region 720.
In either case of
The size of the second search region 720 is smaller than that of the first search region 710. In the example shown in
The change in size of the search region can be realized by, for example, changing some of the three-dimensional point cloud data output by the LiDAR sensor 140 which is to be used in search for objects.
The three-dimensional point cloud data output by the LiDAR sensor 140 includes the information about the position of a plurality of points and the information such as the reception intensity of a photodetector (attribute information). The information about the position of a plurality of points is, for example, the information about the emission direction of laser pulses corresponding to the points and the distance between the LiDAR sensor 140 and the points. For example, the information about the position of a plurality of points is the information about the coordinates of the points in a sensor coordinate system or a local coordinate system. The local coordinate system is a coordinate system that moves together with the work vehicle 100. The coordinates of each point can be calculated from the emission direction of laser pulses corresponding to the points and the distance between the LiDAR sensor 140 and the points.
Some of a plurality of points indicated by the three-dimensional point cloud data which are used in search for objects are selected in consideration of, for example, the distance between the LiDAR sensor 140 and the points. By changing the distance which is the criterion for that selection, the size of the search region can be changed.
By subjecting only points whose distance from the LiDAR sensor 140 is equal to or shorter than the first distance L1 to search for objects, the first search region 710 whose maximum distance from the LiDAR sensor 140 is the first distance L1 can be set. By subjecting only points whose distance from the LiDAR sensor 140 is equal to or shorter than the second distance L2 to search for objects, the second search region 720 whose maximum distance from the LiDAR sensor 140 is the second distance L2 can be set.
The search region may be set based on the coordinates of each of the points. By selecting points located inside a desired shape in a coordinate system as points which are to be used in search for objects, the search region of the desired shape can be set.
The size of the search region may be changed by changing the range scanned by the LiDAR sensor 140. For example, the maximum distance from the LiDAR sensor 140 in the search region may be changed by changing the power of laser pulses emitted from the LiDAR sensor 140.
The processor 161 detects objects around the work vehicle 100 using the output data of the LiDAR sensor 140 corresponding to the set search region (Step S206). The processor 161 repeats the operations of Step S201 to Step S206 till it is ordered to end the operations (Step S207).
If an obstacle is detected in the process of detecting objects around the work vehicle 100, the work vehicle 100 can perform an action to avoid the obstacle or halt traveling.
For example, if an object such as a human, animal, or vehicle located on a preset target path is detected, the processor 161 determines that there is an obstacle (Step S301). For example, if an object which is not included in a pre-generated “environment map” and which is taller than a predetermined height is detected on the target path, the processor 161 determines that there is an obstacle.
If an obstacle is detected, the ECU 185 determines whether or not it is possible to generate such a detour route that the work vehicle 100 can avoid the obstacle (Step S302). For example, if there is a sufficient space on the roads 76 to make a detour, the ECU 185 determines that it is possible to generate a detour route. In the field 70, for example, if it is possible to generate a such detour route that the work vehicle 100 will not affect agricultural work and crops, the ECU 185 determines that it is possible to generate a detour route. For example, if it is agricultural work for which generation of a detour route is prohibited in advance, or if it is determined that making a detour will cause the work vehicle 100 to come into contact with crops, the ECU 185 determines that it is not possible to generate a detour route. For example, if it is possible to generate such a detour route that the work vehicle 100 will not enter an area of the field 70 in which the work has been done, the ECU 185 determines that it is possible to generate a detour route.
If the ECU 185 determines that it is possible to generate a detour route, the ECU 185 generates a detour route, and the controller 180 controls the work vehicle 100 so as to travel along the detour route (Step S303). After the work vehicle 100 has traveled through the detour route, the controller 180 controls the work vehicle 100 so as to return to the target path, before returning to the process of Step S207 shown in
If the ECU 185 determines that it is not possible to generate a detour route, the controller 180 controls the work vehicle 100 so as to halt traveling (Step S304). Concurrently, some operations are performed, such as emission of warning sound from the buzzer 220 and transmission of a warning signal to the terminal unit 400.
If an object detected as an obstacle has moved or a worker has removed the obstacle and, as a result, it is determined that the obstacle has been dismissed, the controller 180 controls the work vehicle 100 so as to resume traveling (Step S305 and Step S306), before returning to the process of Step S207 shown in
In the present example embodiment, the size of the search region for detection of objects is set to be different between a case where the work vehicle 100 is located in the field 70 and a case where the work vehicle 100 is located in the out-of-field area. Thus, the size of the search region can be improved or optimized for the area in which the work vehicle 100 is located.
For example, when the work vehicle 100 is located on a road 76 outside the field (agricultural road or general road), the search region is set to be relatively large, so that detection of objects can be performed over a large area around the work vehicle 100.
When the work vehicle 100 is located in the field 70, the search region is set to be relatively small, so that the computational load in the object detection process can be reduced. In general, the travel speed of the work vehicle 100 in the field 70 can be lower than the travel speed of the work vehicle 100 on the road 76 outside the field. Therefore, in the field 70, sometimes it may not be necessary to set the search region to be so large as compared with a case where the work vehicle 100 is traveling on the road 76. In such a case, the search region may be set to be relatively small, so that the computational load in the object detection process can be reduced.
Even if the travel speed of the work vehicle 100 traveling in the field 70 is equal to the travel speed of the work vehicle 100 traveling outside the field 70, the size of the search region may be set to be different between a case where the work vehicle 100 is located in the field 70 and a case where the work vehicle 100 is located outside the field 70.
In the above-described example embodiments, the position data generated by the GNSS unit 110 is used in detecting the area in which the work vehicle 100 is located, although it is not limited to this example. For example, the area in which the work vehicle 100 is located may be estimated by matching between the data output from the LiDAR sensor 140 and/or the cameras 120 and the environment map.
As described above, the LiDAR sensor 140 can scan a portion of the surrounding environment within a predetermined angular range centered on the oscillation axis. Some of a plurality of points indicated by the three-dimensional point cloud data output by the LiDAR sensor 140 are selected in consideration of the emission angle of corresponding laser pulses. By changing the range of the angle which is the criterion for that selection, the size of the search region can be changed.
By subjecting only points for which the emission angle of laser pulses is within the first angular range θ1 to search for objects, the first search region 710 extending from the LiDAR sensor 140 with the first angular range θ1 can be set. By subjecting only points for which the emission angle of laser pulses is within the second angular range θ2 to search for objects, the second search region 720 extending from the LiDAR sensor 140 with the second angular range θ2 can be set.
The search region may be set based on the coordinates of each of the points. By selecting points located inside a desired shape in a coordinate system as points which are to be used in search for objects, the search region of the desired shape can be set.
The size of the search region may also be changed by changing the range scanned by the LiDAR sensor 140. For example, the angular range of the search region may be changed by changing the angular range in which the LiDAR sensor 140 emits laser pulses. Alternatively, for example, the angular range of the search region may be changed by changing the angular range in which the emission direction of laser pulses is oscillated.
When the work vehicle 100 is located in the field 70, the size of the second search region 720 may be changed in accordance with the travel speed of the work vehicle 100. For example, the processor 161 causes the size of the second search region 720 to be greater in a case where the work vehicle 100 is traveling at the first velocity V1 than in a case where the work vehicle 100 is traveling at the second velocity V2 that is lower than the first velocity V1. When the travel speed of the work vehicle 100 is high, the size of the second search region 720 is increased so that detection of objects can be performed over a larger area around the work vehicle 100. When the travel speed of the work vehicle 100 is low, the size of the second search region 720 is decreased so that the computational load in the object detection process can be reduced.
When the work vehicle 100 is located in the field 70, the size of the second search region 720 may be changed in accordance with whether or not the work vehicle 100 is performing agricultural work. For example, the processor 161 causes the size of the second search region 720 to be greater in a case where the work vehicle 100 is performing agricultural work than in a case where the work vehicle 100 is not performing agricultural work. While the work vehicle 100 is performing agricultural work, the implement 300 is in operation, and it is desirable that the presence of human beings or animals around the work vehicle 100 and the implement 300 be detectable in an early phase. While the work vehicle 100 is performing agricultural work, the size of the second search region 720 is increased so that the presence of human beings or animals around the work vehicle 100 can be detectable in an early phase.
In the above-described example embodiments, the LiDAR sensor 140 mainly scans a portion of the surrounding environment extending on the front side of the work vehicle 100, although the LiDAR sensor 140 may scan a portion of the surrounding environment extending on the rear side of the work vehicle 100.
If the area corresponding to the geographic coordinates indicated by the position data generated by the GNSS unit 110 is an area outside the field 70, the processor 161 sets a third search region 730 as the search region on the rear side of the work vehicle 100. If the area corresponding to the geographic coordinates indicated by the position data is an area inside the field 70, the processor 161 sets a fourth search region 740 as the search region on the rear side of the work vehicle 100. Similarly to the relationship between the first search region 710 and the second search region 720, the size of the fourth search region 740 may be smaller than that of the third search region 730.
The work vehicle 100 may include a LiDAR sensor for scanning a portion of the surrounding environment extending on a lateral side of the work vehicle 100. In such a case, the size of the search region may be set to be different between a case where the work vehicle 100 is located in the field 70 and a case where the work vehicle 100 is located in an out-of-field area that is outside the field 70.
The sensing system 10 of the present example embodiment can be mounted on an agricultural machine lacking such functions, as an add-on. Such a system may be manufactured and marketed independently from the agricultural machine. A computer program for use in such a system may also be manufactured and marketed independently from the agricultural machine. The computer program may be provided in a form stored in a non-transitory computer-readable storage medium, for example. The computer program may also be provided through downloading via telecommunication lines (e.g., the Internet).
Some or all of the processes that are to be performed by the processor 161 in the sensing system 10 may be performed by another device. Such another device may be at least one of the processor 660 of the management device 600, the processor 460 of the terminal unit 400, and the operational terminal 200. In this case, such another device and the processor 161 function as a processor of the sensing system 10, or such another device functions as a processor of the sensing system 10. For example, when some of the processes that are to be performed by the processor 161 is performed by the processor 660 of the management device 600, the processor 161 and the processor 660 function as a processor of the sensing system 10.
Some or all of the processes that are to be performed by the processor 161 may be performed by the controller 180. In this case, the controller 180 and the processor 161 function as a processor of the sensing system 10, or the controller 180 functions as a processor of the sensing system 10.
As described above, the present disclosure includes an agricultural machine and a sensing system and sensing method for an agricultural machine, which will be described in the following paragraphs.
A sensing system 10 according to an example embodiment of the present disclosure is a sensing system 10 for a mobile agricultural machine 100, including a LiDAR sensor 140 provided in the agricultural machine 100 to sense an environment around the agricultural machine 100 to output sensing data, and a processor 161 configured or programmed to detect an object in a search region 710, 720 around the agricultural machine 100 based on the sensing data, wherein the processor 161 is configured or programmed to cause a size of the search region 710, 720 for the detection of the object to be different between a case where the agricultural machine 100 is located in a field 70 and a case where the agricultural machine 100 is located in an out-of-field area 76, 77 that is outside the field 70.
The size of the search region 710, 720 for the detection of the object is set to be different between a case where the agricultural machine 100 is located in the field 70 and a case where the agricultural machine 100 is located in the out-of-field area 76, 77. Due to this feature, the size of the search region 710, 720 can be improved or optimized for the area in which the agricultural machine 100 is located.
When the size of the search region 710, 720 is increased, detection of objects can be performed over a larger area around the agricultural machine 100. When the size of the search region 710, 720 is decreased, the computational load in the object detection process can be reduced.
In one example embodiment, the processor 161 may be configured or programmed to cause the size of the search region 720 to be smaller in the case where the agricultural machine 100 is located in the field 70 than in the case where the agricultural machine 100 is located in the out-of-field area 76, 77.
In the case where the agricultural machine 100 is located in the field 70, the search region 720 is set to be relatively small, so that the computational load in the object detection process can be reduced.
In one example embodiment, in the case where the agricultural machine 100 is located in the out-of-field area 76, 77, the processor 161 may be configured or programmed to set a region extending from the LiDAR sensor 140 with a first angular range θ1 as the search region 710, and in the case where the agricultural machine 100 is located in the field 70, the processor 161 may be configured or programmed to set a region extending from the LiDAR sensor 140 with a second angular range θ2 as the search region 720, the second angular range θ2 being smaller than the first angular range θ1.
Due to this feature, the search region 720 can be set to be relatively small while the agricultural machine 100 is located in the field 70, so that the computational load in the object detection process can be reduced.
In one example embodiment, in the case where the agricultural machine 100 is located in the out-of-field area 76, 77, the processor 161 may be configured or programmed to set a region whose maximum distance from the LiDAR sensor 140 is a first distance L1 as the search region 710, and in the case where the agricultural machine 100 is located in the field 70, the processor 161 may be configured or programmed to set a region whose maximum distance from the LiDAR sensor 140 is a second distance L2 as the search region 720, the second distance L2 being shorter than the first distance L1.
Due to this feature, the search region 720 can be set to be relatively small while the agricultural machine 100 is located in the field 70, so that the computational load in the object detection process can be reduced.
In one example embodiment, the sensing system 10 may further include a position sensor 110 provided in the agricultural machine 100 to detect a position of the agricultural machine 100 to output position data, and a storage 170 to store map data concerning an area in which the agricultural machine 100 travels, wherein the processor 161 may be configured or programmed to determine whether the agricultural machine 100 is located in the field 70 or the out-of-field area 76, 77 based on the position data and the map data.
Using the position sensor 110 enables determination of whether the agricultural machine 100 is located in the field 70 or the out-of-field area 76, 77.
In one example embodiment, the out-of-field area 76, 77 may be any of a road outside the field, a barn, or a gas station.
By setting the search region 710 to be relatively large, a search improved or optimized for the road outside the field, the barn, or the gas station can be conducted.
In one example embodiment, in the case where the agricultural machine 100 is located in the field 70, the processor 161 may change the size of the search region 720 in accordance with a travel speed of the agricultural machine 100.
Due to this feature, a search can be conducted over an area optimum for the travel speed.
In one example embodiment, the processor 161 may be configured or programmed to cause the size of the search region 720 to be greater in a case where the agricultural machine 100 travels at a first velocity V1 than in a case where the agricultural machine 100 travels at a second velocity V2 that is lower than the first velocity V1.
When the travel speed of the agricultural machine 100 is high, the size of the second search region 720 is increased so that detection of objects can be performed over a larger area around the agricultural machine 100.
In one example embodiment, in the case where the agricultural machine 100 is located in the field 70, the processor 161 may change the size of the search region in accordance with whether or not the agricultural machine 100 is performing agricultural work.
Due to this feature, a search can be conducted over an area optimum for the travel speed.
In one example embodiment, the processor 161 may be configured or programmed to cause the size of the search region 720 to be greater in a case where the agricultural machine 100 is performing agricultural work than in a case where the agricultural machine 100 is not performing agricultural work.
The size of the search region 720 is set to be greater while the agricultural machine 100 is performing agricultural work, so that the presence of human beings or animals around the work vehicle 100 can be detectable in an early phase.
In one example embodiment, an agricultural machine 100 may include the above-described sensing system 10.
Thus, the size of the search region 710, 720 for the detection of the object can be set to be different between a case where the agricultural machine 100 is located in the field 70 and a case where the agricultural machine 100 is located in the out-of-field area 76, 77. Due to this feature, the size of the search region 710, 720 can be improved or optimized for the area in which the agricultural machine 100 is located.
In one example embodiment, the agricultural machine 100 may further include a travel device 240 to enable the agricultural machine 100 to travel, and a controller 180 configured or programmed to control an operation of the travel device 240 such that the agricultural machine 100 performs self-driving.
The size of the search region 710, 720 for the detection of the object can be set to be different between a case where the self-driving agricultural machine 100 is located in the field 70 and a case where the self-driving agricultural machine 100 is located in the out-of-field area 76, 77.
A sensing method according to an example embodiment of the present disclosure is a sensing method for a mobile agricultural machine 100, including sensing an environment around the agricultural machine 100 using a LiDAR sensor 140 to output sensing data, detecting an object in a search region 710, 720 around the agricultural machine 100 based on the sensing data, and causing a size of the search region 710, 720 for the detection of the object to be different between a case where the agricultural machine 100 is located in a field 70 and a case where the agricultural machine 100 is located in an out-of-field area 76, 77 that is outside the field.
The size of the search region 710, 720 for the detection of the object is set to be different between a case where the agricultural machine 100 is located in a field 70 and a case where the agricultural machine 100 is located in an out-of-field area 76, 77. Due to this feature, the size of the search region 710, 720 can be improved or optimized for the area in which the agricultural machine 100 is located.
When the size of the search region 710, 720 is increased, detection of objects can be performed over a larger area around the agricultural machine 100. When the size of the search region 710, 720 is decreased, the computational load in the object detection process can be reduced.
The techniques and example embodiments according to the present disclosure are particularly useful in the fields of agricultural machines, such as tractors, harvesters, rice transplanters, vehicles for crop management, vegetable transplanters, mowers, seeders, spreaders, or agricultural robots, for example.
While example embodiments of the present invention have been described above, it is to be understood that variations and modifications will be apparent to those skilled in the art without departing from the scope and spirit of the present invention. The scope of the present invention, therefore, is to be determined solely by the following claims.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2021-212578 | Dec 2021 | JP | national |
This application claims the benefit of priority to Japanese Patent Application No. 2021-212578 filed on Dec. 27, 2021 and is a Continuation Application of PCT Application No. PCT/JP2022/046458 filed on Dec. 16, 2022. The entire contents of each application are hereby incorporated herein by reference.
| Number | Date | Country | |
|---|---|---|---|
| Parent | PCT/JP2022/046458 | Dec 2022 | WO |
| Child | 18749185 | US |
