The present invention relates in general to autonomous systems, and, more specifically, to an autonomous dust mitigation in mining and construction applications system.
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The creation of dust in mining and construction operations is a known hazard to both personnel and the environment. Personnel can inhale the dust or have the dust get into their eyes, and the dust itself could be carrying pathogens that can cause infections and disease. The dust can also spread across the environment, affecting plant and animal life in the area and settling into bodies of water where it can cause contamination. Such dust may also cause problems for the equipment on mining and construction sites by getting into the engines and clogging the filters and joints of the various machinery. Especially in instances where autonomous trucks are used in such applications, it is important to understand how the dust affects the trucks' particular autonomous system sensors. For example, dust comes in different particulate sizes, which affects the laser detection and ranging (LADAR) systems and cameras differently, depending on the frequencies, sizes of receptors, beam divergences, and distances. Two dust clouds that are indistinguishable from a human standpoint, can affect autonomous systems quite differently. The dust can also obscure pedestrians or cliff edges, and so mining and construction operations use a variety of methods to mitigate such dust.
One common method for dust suppression involves the use of trucks to spray water over the site that congeals the dust and prevents it from becoming airborne. Such a technique is considered cheap and effective, though carries with it the risk of overwatering a site and creating mud, which can entrap equipment and personnel, and causing toxic or otherwise hazardous runoff Moreover, some dust may or may not dissipate by wetting the surface, and other dust may require spraying or misting of the area.
Another method of dust mitigation involves applying mulch and vegetation to the area to protect exposed dirt from wind and water erosion. Although this method is environmentally friendly, it still requires that watering of the flora be performed to maintain the protection, and overwatering can result in erosion anyway. This technique can be very effective though carries with it a higher cost of implementation, since birds can eat up freshly applied seeds and many of the seeds may not implant anyway.
Yet other methods of dust mitigation involve plowing the sites with tills to create rough terrain, which reduces dust creation by almost 80%, or applying stones over the sites so that dust is not kicked up by equipment traversing loose soil. All of these methods carry with them their own benefits and detriments, though no dust mitigation solution has been provided that detects and responds to dust automatically and in real time.
There is significant time and effort involved in mitigating dust during mining and construction applications, though the widely accepted use of water for mitigation is a solution that has several problems. First, the application of too much water can render roads slippery and muddy and can slow down the performance of vehicles on the site. Second, the application of water in the wrong areas may just waste time and resources and may not address the dust problem. Even if a road creates dust when travelled, there may be areas of the road that need to be wet though others that need to remain dry for traction or other purposes. Adding water in some areas may create runoff, thereby wasting time or creating environmental problems. Finally, some areas of a site can create dust but are not being traversed by vehicles, and, therefore, may not need mitigation with water.
Thus, there is a need in the art for an autonomous dust mitigation in mining and construction applications system. Dust mitigation can benefit from having sensors measure the need and the effectiveness of the operation, and a more effective mitigation system includes sensing of the dust. It is to these ends that the present invention has been developed.
To minimize the limitations in the prior art, and to minimize other limitations that will be apparent upon reading and understanding the present specification, the present invention describes an autonomous dust mitigation in mining and construction applications system.
It is an objective of the present invention to provide an autonomous dust mitigation in mining and construction applications system that receives a plurality of detected inputs relating to dust on a work site.
It is another objective of the present invention to provide an autonomous dust mitigation in mining and construction applications system that interprets detected inputs relating to dust on a work site to generate a list of dust areas.
It is another objective of the present invention to provide an autonomous dust mitigation in mining and construction applications system that can assign priorities to detected dust areas.
It is another objective of the present invention to provide an autonomous dust mitigation in mining and construction applications system that assigns priorities to dust areas based on dust severity.
It is another objective of the present invention to provide an autonomous dust mitigation in mining and construction applications system that assigns priorities to dust areas based on area usage.
It is another objective of the present invention to provide an autonomous dust mitigation in mining and construction applications system that can dispatch dust mitigation equipment to high priority dust areas.
These and other advantages and features of the present invention are described herein with specificity so as to make the present invention understandable to one of ordinary skill in the art, both with respect to how to practice the present invention and how to make the present invention.
Elements in the figures have not necessarily been drawn to scale in order to enhance their clarity and improve understanding of these various elements and embodiments of the invention. Furthermore, elements that are known to be common and well understood to those in the industry are not depicted in order to provide a clear view of the various embodiments of the invention.
The following detailed description is merely exemplary in nature and is not intended to limit the described embodiments or the application and uses of the described embodiments. As used herein, the word “exemplary” or “illustrative” means “serving as an example, instance, or illustration.” Any implementation described herein as “exemplary” or “illustrative” is not necessarily to be construed as preferred or advantageous over other implementations. All of the implementations described below are exemplary implementations provided to enable persons skilled in the art to make or use the embodiments of the disclosure and are not intended to limit the scope of the disclosure, which is defined by the claims. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, brief summary, or the following detailed description. It is also to be understood that the specific devices and processes illustrated in the attached drawings, and described in the following specification, are simply exemplary embodiments of the inventive concepts defined in the appended claims. Hence, specific dimensions and other physical characteristics relating to the embodiments disclosed herein are not to be considered as limiting, unless the claims expressly state otherwise.
The present invention uses sensors installed on manned and unmanned vehicles to qualify how the particular dust cloud is affecting a vehicle, and to determine if such an effect interferes with the operation of the vehicle. For example, heavy dust may be present, but it may not deteriorate the performance of the vehicle. On the other hand, light dust may be present, but this dust may be occurring at frequencies that render the vehicle's autonomous systems blind.
The invention measures the levels of dust detected by the sensors, computes if the levels of dust can affect the performance of the system, and, in the case where the effect warrants the use of water to mitigate the issue, invokes a water dispensing vehicle or sprinklers in needed areas. Both the sensing vehicle and the water vehicle can be autonomous or manned.
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One method for detecting dust is the single return laser detection and ranging (LADAR), which can see dust as the first return signal sent back to the system. The LADAR hits the surface or edge of the dust cloud, and the dust cloud can be morphologically classified using size, volume, and various movement parameters.
Another method for detecting dust is by multi-return LADAR, which can partially penetrate through the dust, and, in some cases, can fully penetrate through the dust. As an example, the multiple returns of a single beam can be classified as dust when they return multiple ranges for the same beam. A solid object, in comparison, provides a single range for each beam.
Cameras, such as electro-optical sensor or infrared cameras, can also see some dust. There are cloud classification algorithms that can correctly classify the dust using the color, size, and texture of particles detected.
When dust is dense enough, LADARs tend to bloom. The diffracted energy sent into the dust is reflected back to the sensor, usually within the minimum range of the sensor, and tends to overwhelm, or saturate, the receivers, or cause crosstalk in systems with multiple receiver elements. This blooming can be used as a strong indicator that dust is present.
As the energy radiated by the LADAR bounces off a dust cloud very close to the receiver, the receivers are triggered at very short distances. These very short distances are usually faster than the internal time counters in the LADAR can measure, therefore triggering a “too close” error on the LADAR for each beam embedded in the cloud.
Redundancy of a variety of sensor combinations. Radio detection and ranging (RADAR) can see through most dust clouds, while and LADAR does not. Therefore, if a combination of these sensors covers the area where the dust cloud is located, the RADAR will show it as transparent and the LADAR will provide returns as if it were solid. These discrepancies between redundant sensors can also be used to detect dust. The redundancy is not only between LADAR and RADAR, but it can also be between two LADARs of different frequencies, a camera and RADAR combination, or any other appropriate combination.
As the dust is detected, the location of the dust can be determined and stored. The volume and other characteristics of the dust can also be stored such as, for example, density, particulate size, size of cloud, distance from the ground, and other relevant parameters. LADAR can also be used to determine the support surface, as well as the inclination of the ground. If the system detecting the dust is also an autonomous system, it can provide measurements that indicate how the dust, at different locations, is affecting the autonomous system, and may store this information.
A number of key measurements may affect the performance and safety of the system. As the dust gets denser, less energy will be able to penetrate and reflect from the ground and the distance at which the support surface can be determined is affected. Therefore, the area where the vehicles can determine the support surface for their wheels becomes smaller as the vehicle drives along. For example, a truck may be able to determine its support surface using a LADAR 50 meters in front of the driving area. In a dusty environment, the same system may only be allowed to determine the support surface at 20 meters in front of the vehicle. Depending on the application or the specific environment, 20 meters may or may not be enough. If the maximum speed in the area is less than 5 mph, the vehicle without a load may be able to brake in time if a ditch on the road is detected within the 20 meters. However, if the vehicle is fully loaded and driving at 60 mph, 20 meters may not be sufficient to stop, and, therefore, the vehicle will be unsafe to drive in the dust.
The distance at which dynamic obstacles are detected, classified, and predicted may also be affected by dust. As with the performance of safety systems, this measurement can be used to compare the reduced sensing and prediction characteristics of the vehicle in dust against the rules of the particular application.
The distance at which static obstacles are detected and classified may also be affected by dust. The distance at which these obstacles are detected and classified is negatively affected by dust. Measuring how much they are affected by the dust and how this affects the performance of the vehicle becomes important to assess safety within the application.
It is important to note that, to minimize water usage, it becomes important to track the effects of the dust to the parameters of the autonomous system as it relates to the particular application.
The invention can be decomposed in two parts: data collection and mitigation effects. During the data collection phase, one or more trucks collect information on the location of the dust, the characteristics of the dust, and the safety assessment. The safety assessment presented above can take into consideration many variables of the user and area, however, simple assessments exist where the user or system may decide to water areas that are above a certain threshold of dust in the many of the measurements taken, or even to use time as a factor in the decision to water an area.
The information collected is stored in a database. The database is then accessed by a dust mitigation planner algorithm. The dust mitigation planner is an optimization algorithm that generates trajectories and water mitigation needs around the area being groomed. The algorithm can take into consideration a variety of inputs, including the status of the water delivery trucks, as well as all the information collected in the database.
Optimization can be performed a variety of ways, including simple programming, dynamic programming, and genetic algorithms. The space of optimization can include the cost of the water, cost of reduced performance, chances of losing traction, as well as a large number of utility parameters in the optimization area. Once the optimization is performed, the trucks are sent to water the specified areas.
While the invention has been described m connection with what is presently considered to be the most practical and preferred embodiments, it is to be understood that the invention is not to be limited to the disclosed embodiments, but, on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
The autonomous dust mitigation system used in mining and construction applications comprises a plurality of sensors, a computing device, a dust area mapping system, a dust area prioritizing system, and a plurality of dust mitigation equipment.
The system that is described in the present invention has a plurality of sensors that detect a plurality of dusty area information and the plurality of dust area information includes size, speed, direction, height, relative height, position, and relative position. The plurality of sensors transmit the detect plurality of dust area information to said computing device and the dust area mapping system is installed on the computing device. Also, the dust area mapping system interprets the detected plurality of dust area information to determine a dust area map. Also, the dust area prioritizing system is installed on said computing device and determines an appropriate dust area based on plurality of dust area information and determined dust area map. The computing device instructs the plurality of dust mitigation equipment to perform a dust mitigation.
The system that is disclosed in the present invention has a plurality of sensors that comprise a LADAR device.
Laser Detection and Ranging (LADAR) refers to a surveying method that measures distance to a target by illuminating the target with laser light and measuring the reflected light with a sensor. Differences in laser return times and wavelengths can then be used to make digital 3-D representations of the target.
In another embodiment, the system has a plurality of sensors that comprise a stereo camera. A stereo camera is a type of camera with two or more lenses with a separate image sensor or film frame for each lens. This allows the camera to simulate human binocular vision, and therefore gives it the ability to capture three-dimensional images, a process known as stereo photography.
In yet another embodiment, the system has a plurality of sensors that comprise a ranging sensor. A ranging sensor requires no physical contact with the object being detected. This type of sensor allows a robot to see an obstacle without actually having to come into contact with it.
In the system described by the present invention, the dust area prioritizing system comprises a plurality of environment data. Also, the dust area prioritizing system determines an appropriate dust area priority based on the plurality of dust area information, determined dust area map, and plurality of environment data and the computing device instructs said plurality of dust mitigation equipment to perform a dust mitigation.
In this system that is described in the present invention, the planner coordinates with the overall site planner to deconflict the mitigation equipment and the other equipment such as haul trucks.
Areas to be treated include haul roads, access roads, stock piles, and open areas. Watering road sis the most common method used for haul road dust control. Surfactants or wetting agents can be added to the water to extend its life as a dust control agent. Slat solutions are commonly mixed with water and used for haul road dust control. Petroleuyjm resins are generally engineered products or byproducts of lubrication oil manufacturing. Polymers include acrylics and vineyls, which are chemical additives, mixed with water to form a diluted solution, then applied to the road surface topically. Adhesives are compounds, solutions, formulas, etc. that are mixed with the soil surface to form a new road surface.
Number | Date | Country | |
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62759963 | Nov 2018 | US |