DETERMINING THAT A TARGET IS A DESTINATION OF A PROJECTED PATH OF A MACHINE

Description

TECHNICAL FIELD

The present disclosure relates generally to a machine and, for example, to determining that a target is a destination of a projected path of the machine.

BACKGROUND

To perform a dumping operation, a machine, such as a wheel loader, can use an implement (e.g., a bucket or another implement) to load and to carry a material (e.g., asphalt, debris, dirt, snow, feed, gravel, logs, raw minerals, recycled material, rock, sand, woodchips, or similar material) and to dump the material into a dump target (e.g., another machine, such as a dump truck). The machine can include perception sensors to detect a position of the dump target, such as to enable an automated implement lift operation. For example, the machine can use the perception sensors to identify when the machine is sufficiently near to the dump target, and thereby automatically cause the implement to raise to a dumping height to enable an efficient dumping of material from the implement into the dump target as soon as the machine reaches the dump target.

However, the machine often operates at a work site that includes more than one dump target. In many cases, therefore, the perception sensors detect respective positions of multiple dump targets, but the machine is unable to discern which dump target is an intended destination of the machine. This results in the automated implement lift operation being performed any time the machine approaches close enough to a dump target, even when the dump target is not the intended destination of the machine. Consequently, the implement can be lifted to a high position at inappropriate times (e.g., when the machine is passing a first dump target to travel to a second dump target), which results in an elevated center of gravity of the machine (e.g., as the machine travels with the implement in the high position). This can reduce a stability and a control of the machine, which can lead to spillage of a material carried by the machine. Further, unnecessary lifting of implement, when carrying a heavy load of material, can increase wear and tear on the implement and on a linkage that connects the implement to the machine, which can impact a performance of, as well as reduce an operable life of, the implement and the linkage, and the machine.

The controller of the present disclosure solves one or more of the problems set forth above and/or other problems in the art.

SUMMARY

In some implementations, a machine comprises: an implement and a linkage; a perception sensor system that includes one or more perception sensors; a machine sensor system that includes one or more machine sensors; and a controller configured to: obtain perception information from the perception sensor system, wherein the perception information indicates respective positions of a plurality of targets; obtain machine information from the machine sensor system; determine, based on the perception information and the machine information, respective potential paths of the machine to the plurality of targets; determine, based on the machine information, a projected path of the machine; and determine, based on the respective potential paths of the machine and the projected path of the machine, that a particular target, of the plurality of targets, is a destination of the projected path.

In some implementations, a controller of a machine includes one or more memories; and one or more processors, coupled to the one or more memories, configured to: obtain perception information from a perception sensor system of the machine; obtain machine information from a machine sensor system of the machine; determine, based on the perception information and the machine information, respective potential paths of the machine to a plurality of targets; determine, based on the machine information, a projected path of the machine; and determine, based on the respective potential paths of the machine and the projected path of the machine, that a particular target, of the plurality of targets, is a destination of the projected path.

In some implementations, a method includes obtaining, by a controller of a machine, perception information and machine information; determining, by the controller, based on the perception information and the machine information, respective potential paths of the machine to a plurality of targets, respectively; and determining, by the controller, based on the respective potential paths of the machine and a projected path of the machine, that a particular target, of the plurality of targets, is a destination of the projected path.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of an example machine described herein.

FIG. 2 is a diagram of an example configuration of the front of the machine.

FIGS. 3A-3E are diagrams of an example implementation described herein.

FIGS. 4A-4B are diagrams of an example implementation described herein.

FIG. 5 is a diagram of example components of a device associated with determining that a target is a destination of a projected path of a machine.

DETAILED DESCRIPTION

This disclosure relates to a controller of a machine (e.g., that performs a dumping operation) and is applicable to any machine that is capable of loading and moving material (e.g., from a first location to a second, different location) and/or dumping the material (e.g., into a dump target). For example, the machine may be any machine that performs an operation associated with an industry such as, for example, mining, construction, farming, transportation, or any other industry. As some examples, the machine may be a vehicle, a wheel loader, a backhoe loader, a cold planer, a compactor, a feller buncher, a forest machine, a forwarder, a harvester, an excavator, an industrial loader, a knuckleboom loader, a material handler, a motor grader, a pipelayer, a road reclaimer, a skid steer loader, a tractor, a dozer, a tractor scraper, or other above ground equipment, underground equipment, aerial equipment, or marine equipment.

FIG. 1 is a diagram of an example machine 100 described herein. For example, the machine 100 may include a mobile machine, such as the wheel loader shown in FIG. 1, or any other type of mobile machine. Further, the machine 100 may be a manned machine or an unmanned machine, and/or may be fully autonomous, semi-autonomous, or remotely operated.

As shown, the machine 100 may have a frame 102 that supports an operator station 104, a power system 106, a drive system 108, an implement 110, a perception sensor system 112, and a controller 114. The operator station 104 may include operator controls 116 for operating the machine 100 via the power system 106. In some examples, the machine 100 may not include an operator station 104 and/or operator controls 116 (e.g., the machine 100 may be controlled via other means, such as a remote control system). The operator station 104 may be configured to define an interior cabin 118 within which the operator controls 116 are housed.

The power system 106 is configured to supply power to the machine 100. The power system 106 may be operably arranged to receive control signals from the operator controls 116 in the operator station 104 and/or from the controller 114. Additionally, or alternatively, the power system 106 may be operably arranged with the drive system 108 and/or the implement 110 to selectively operate the drive system 108 and/or the implement 110 according to the control signals. The power system 106 may provide operating power for the propulsion of the drive system 108 and/or the operation of the implement 110. The power system 106 may include an engine, a motor, an electric drive, a fuel cell, and/or another type of power system.

The drive system 108 may be operably arranged with the power system 106 to selectively propel the machine 100 via the control signals. The drive system 108 can include a plurality of ground-engaging members, such as wheels 120, as shown, which can be movably connected to the frame 102 through axles, drive shafts, and/or other components. The drive system 108 may be provided in the form of a track-drive system, a wheel-drive system, or any other type of drive system configured to propel the machine 100.

The implement 110 may be operably arranged with the power system 106 such that the implement 110 is selectively movable through control signals transmitted to the power system 106 from the operator controls 116 and/or the controller 114. As shown in FIG. 1, the implement 110 may be coupled to the machine 100 via a linkage 122, such as at a front 124 of the machine 100. The implement 110 may also be referred to as an attachment, a work tool, a work implement, and/or a tool, among other examples. FIG. 1 depicts implement 110 as a bucket as an example. Other embodiments can include any other suitable implement 110 for a variety of tasks, including, for example, dozing, brushing, compacting, grading, lifting, loading, plowing, and/or ripping, among other examples. Example implements 110 include a stump grinder, a trencher, a broom, a brush cutter, a cold planer, a mower, a mulcher, a processor, a pulverizer, a rake, a saw, a snow product, a snow blower, a tiller, a winch, an auger, a blade, a breaker/hammer, a compactor, a cutter, a forked lifting device, a grader bit and end bit, a grapple, a blade, and/or a ripper, among other examples. As described elsewhere herein, the implement 110 may include one or more components or parts that are electrically powered.

The perception sensor system 112 includes one or more perception sensors 126, which may be coupled to the machine 100, such as at the front 124 of the machine 100. The one or more perception sensors 126 may include a sonar sensor, a camera, a light detection and raging (LIDAR) sensor, and/or a radio detection and ranging (RADAR) sensor, or another type of sensor to perceive an environment of the machine 100. That is, one or more perception sensors 126 may include at least one sensor that is configured to capture perception data that can be used (e.g., by the controller 114) to determine at least one of a position (e.g., that indicates a distance and an azimuth angle, along with other examples) of a target (e.g., a dump target), such as relative to the machine 100, or a height of the target.

The controller 114 may include an electronic control module (ECM) or other computing device. The controller 114 may be configured to cause perception data captured by sets of one or more sensors of the perception sensor system 112 to be used in association with an automatic control operation associated with at least one of the machine 100 or the implement 110 and the linkage 122, and/or cause one or more other actions to be performed, as further described herein.

A rear portion 128 of the machine 100 may include an engine and a transmission. The engine may be any type of engine suitable for performing work using the machine 100, such as an internal combustion engine, a diesel engine, a gasoline engine, a gaseous fuel-powered engine, and/or the like. In other examples, rather than an engine, the machine 100 may include another power system, such as a motor (e.g., an electric motor), a battery powered system, a fuel cell, or another type of power system. The transmission may transfer power from the engine to the drive system 108 and/or the implement 110.

The machine may include a machine sensor system 130 that includes one or more machine sensors 132, which may be housed within the machine 100. The one or more machine sensors 132 may include a location sensor (e.g., a global positioning system (GPS) sensor, or a local positioning system sensor) configured to determine a location (e.g., a physical location) and/or a heading of the machine 100, a position sensor (e.g., a rotation sensor, or another sensor) configured to detect a position of the implement 110 and the linkage 122, a speed sensor configured to determine a speed of the machine 100 (e.g., when travelling over a surface), a steering angle sensor configured to determine a steering angle of the machine 100, and/or one or more other sensors.

As indicated above, FIG. 1 is provided as an example. Other examples may differ from what is described in connection with FIG. 1.

FIG. 2 is a diagram of an example configuration 200 of the front 124 of the machine 100 (e.g., when the implement 110 and the linkage 122 are extended to a “high” position, as further described herein). The perception sensor system 112 may include one or more perception sensors 126 that are positioned at different locations (e.g., on the machine 100, at the front 124 of the machine 100). For example, as shown in FIG. 2, one or more perception sensors 126 may be positioned at a first height associated with (e.g., aligned with, or nearly aligned with) a top of the operator station 104 and one or more perception sensors 126 may be positioned at a second height associated with (e.g., aligned with, or nearly aligned with) the wheels 120.

As indicated above, FIG. 2 is provided as an example. Other examples may differ from what is described in connection with FIG. 2.

FIGS. 3A-3E are diagrams of an example implementation 300 described herein. FIGS. 3A-3B show side views of the machine 100 when the implement 110 and the linkage 122 are in different positions and fields of view of the one or more perception sensors 126 of the perception sensor system 112. FIGS. 3C-3E show top-down views of the machine 100 and fields of view of the one or more perception sensors 126 of the perception sensor system 112 with respect to determining that a target is a destination of a projected path of the machine 100, as further described herein.

As shown in FIG. 3A, the implement 110 and the linkage 122 may be in a “low” position (e.g., where the implement 110 is aligned with, or nearly aligned with, the wheels 120). As part of an automatic control operation (e.g., that is associated with at least one of the machine 100 or the implement 110 and the linkage 122), the controller 114 may cause the implement 110 and the linkage 122 to be in the low position. For example, as part of the automatic control operation, the controller 114 may cause (e.g., when the machine 100 is traveling, such as in a forward direction) the implement 110 and the linkage 122 to be in the low position to enable loading and/or carrying of material, such as from a first location to a second location that is associated with a dump target (e.g., another machine, such as a dump truck). As shown in FIG. 3A, when in the low position, the implement 110 and the linkage 122 may not obstruct a field of view 302 of a first perception sensor 126 of the perception sensor system 112, and may obstruct a field of view 304 of a second perception sensor 126 of the perception sensor system 112.

As shown in FIG. 3B, the implement 110 and the linkage 122 may be in a “high” position (e.g., where the implement 110 is aligned with, or nearly aligned with, a top of the operator station 104). As part of the automatic control operation, the controller 114 may cause the implement 110 and the linkage 122 to be in the high position. For example, as part of the automatic control operation, the controller 114 may cause (e.g., when the machine 100 is traveling, such as in a forward direction) the implement 110 and the linkage 122 to be in the high position when the machine 100 is within a “dumping distance” (e.g., within a threshold distance) of the dump target (e.g., to facilitate an impending dumping of the material carried by the implement 110). As shown in FIG. 3B, when in the high position, the implement 110 and the linkage 122 may obstruct the field of view 302 of the first perception sensor 126 of the perception sensor system 112, and may not obstruct the field of view 304 of the second perception sensor 126 of the perception sensor system 112.

As shown in FIGS. 3C-3E, the machine 100 may be headed in a particular direction (e.g., a rightward direction shown in FIGS. 3C-3E). A plurality of targets 306 (e.g., dump targets) may be within the field of view 302 of the first perception sensor 126 or the field of view 304 of the second perception sensor 126 (shown as, and referred to hereinafter as, a collective field of view 302/304). For example, as shown in FIGS. 3C-3E a first target 306-A, a second target 306-B, and a third target 306-C may be within the collective field of view 302/304. Accordingly, the controller 114 may obtain perception information, from the perception sensor system 112 (e.g., from the first perception sensor 126 and/or the second perception sensor 126) that indicates respective positions of the plurality of targets 306 (e.g., respective positions of the first target 306-A, the second target 306-B, and the third target 306-C).

The controller 114 may determine respective potential paths 308 of the machine 100 to the plurality of targets 306 (e.g., based on the perception information). For example, as shown in FIG. 3C, the controller 114 may determine a first potential path 308-A to the first target 306-A, a second potential path 308-B to the second target 306-B, and a third potential path 308-C to the third target 306-C. Each potential path 308 may be, for example, an optimal path to a corresponding target 306, which may be based on one or more criteria such as a distance to the target 306 (e.g., a “straight line” distance to the target 306, a travel distance to the target 306, or another type of distance), travel time to the target 306 (e.g., a length of time to travel to the target 306 using one or more operating characteristics of the machine 100), and/or environmental factors related to operation of the machine 100 (e.g., factors related to weather conditions, lighting conditions, and/or worksite conditions), along with other examples.

As shown in FIG. 3D, the controller 114 may determine a projected path 310 of the machine 100. The projected path 310 may be an estimated actual path of the machine 100, which may be based on machine information obtained by the one or more machine sensors 132 of the machine sensor system 130. The machine information may indicate, for example, a steering angle of the machine 100 and/or a steering geometry of the machine 100.

The controller 114 then may determine that a particular target 306 is a destination 312 of the projected path 310 (e.g., based on the respective potential paths 308 and the projected path 310). For example, as shown in FIG. 3E, the controller 114 may determine that the target 306-B is the destination 312 of the projected path 310. The controller 114 may use a cost function, or another analysis technique, to determine that a particular potential path 308 is associated with the projected path 310 (e.g., the particular potential path 308 is the most similar to the projected path 310) and, therefore, the controller 114 may determine that the destination 312 is a particular target 306 that is associated with the particular potential path 308.

As indicated above, FIGS. 3A-3E are provided as an example. Other examples may differ from what is described in connection with FIGS. 3A-3E.

FIGS. 4A-4B are diagrams of an example implementation 400 described herein. FIGS. 4A-4B show how the controller 114 determines that a target 306 is a destination 312 of a projected path 310 of the machine 100.

As shown in FIG. 4A, and by reference number 402, the controller 114 may obtain perception information. For example, the controller 114 may obtain the perception information from the perception sensor system 112 (e.g., from the one or more perception sensors 126 of the perception sensor system 112). The perception information may include respective perception data captured by the one or more perception sensors 126. That is, each perception sensor 126, of the one or more perception sensors 126, may send perception data that is captured by the perception sensor 126 to the controller 114 (e.g., in real time, or near real time), and therefore the controller 114 may collectively receive respective perception data captured by the one or more perception sensors 126 as perception information. The perception information may indicate respective positions of a plurality of targets 306 (e.g., that are within respective fields of view of the one or more perception sensors 126 of the perception sensor system 112).

As shown by reference number 404, the controller 114 may obtain machine information. For example, the controller 114 may obtain the machine information from the machine sensor system 130 (e.g., from the one or more machine sensors 132 of the machine sensor system 130). The machine information may include respective machine data captured by the one or more machine sensors 132. That is, each machine sensor 132, of the one or more machine sensors 132, may send machine data that is captured by the machine sensor 132 to the controller 114 (e.g., in real time, or near real time), and therefore the controller 114 may collectively receive respective machine data captured by the one or more machine sensors 132 as machine information.

The machine information may include, for example, machine state information and/or machine properties information. The machine state information may indicate at least one of information associated with a speed of the machine 100 (e.g., information indicating the speed of the machine 100 and/or any derivative of the speed of the machine 100), information associated with a steering angle of the machine 100 (e.g., information indicating the steering angle of the machine 100 and/or any derivative of the steering angle of the machine 100), information associated with a heading of the machine (e.g., information indicating the heading of the machine 100 and/or any derivative of the heading of the machine 100), or information associated with a location of the machine (e.g., information indicating the location of the machine 100 and/or any derivative of the location of the machine 100), along with other examples. The machine properties information may indicate at least one of information associated with a steering geometry of the machine 100 (e.g., that indicates how the steering angle of the machine 100 corresponds to a turning radius of the machine 100, or other information), or information associated with one or more performance limits of the machine 100 (e.g., information associated with a steering velocity limit, a deceleration limit, an acceleration limit, and/or other performance limits of the machine 100), along with other examples.

As shown by reference number 406, the controller 114 may determine respective potential paths 308 of the machine 100 to the plurality of targets 306 (e.g., based on the perception information and/or the machine information). For example, the controller 114 may determine, based on the perception information (e.g., that indicates respective positions of the plurality of targets 306), a distance (e.g., a straight line distance) to a target 306 (e.g., a particular target 306). The distance to the target 306 may be a representative distance to the target 306 of the one or more perception sensors 126, where the representative distance is a fused distance (e.g., an average, or another type of integration or merging) of distances associated with perception data captured by the one or more perception sensors 126. Accordingly, the controller 114 may determine the potential path 308 of the machine 100 to the target 306 based on the distance to the target 306 and the machine information. For example, the controller 114 may identify the machine state information and/or the machine properties information that are included in the machine information. Accordingly, the controller 114 may determine, using a mathematical curve equation (e.g., a clothoid equation, a polynomial equation, or another type of mathematical curve equations) and a cost equation (e.g., that is configured to tune non-constraint parameters of the mathematical curve equation to determine an optimal path, or another type of path, to the target 306), the potential path 308 of the machine 100 to the target 306 based on the distance to the target and at least one of the machine state information or the machine properties information.

As shown in FIG. 4B, and by reference number 408, the controller 114 may determine a projected path 310 of the machine 100 (e.g., based on the machine information). To determine the projected path 310, the controller 114 may determine, based on the machine information, information associated with a steering angle of the machine 100 and information associated with a steering geometry of the machine 100. For example, the controller 114 may identify the machine state information and the machine properties information that are included in the machine information. The controller 114 may thereby determine, based on the machine state information, such as by processing (e.g., parsing and/or reading, along with other examples) the machine state information, the information associated with the steering angle of the machine 100, and/or may thereby determine, based on the machine properties information, such as by processing the machine properties information, the information associated with the steering geometry of the machine 100. Accordingly, the controller 114 may determine based on the information associated with the steering angle of the machine 100 and the information associated with the steering geometry of the machine 100, a projected path radius of the machine 100 (e.g., with the steering angle of the machine 100 held constant), and may determine, based on the projected path radius of the machine 100, the projected path 310 of the machine 100. The projected path 310 of the machine 100 (or at least a portion of the projected path 310 of the machine 100) may therefore be, in some implementations, a circle (or a portion of a circle) defined by the projected path radius.

As shown by reference number 410, the controller 114 may determine that a particular target 306 (e.g., the second target 306-B shown in FIG. 3E), of the plurality of targets 306, is a destination 312 of the projected path 310 (e.g., based on the respective potential paths 308 of the machine 100 to the plurality of targets 306 and the projected path 310 of the machine 100).

For example, the controller 114 may determine, using a cost function, or another analysis technique, and based on the respective potential paths 308 of the machine 100, the machine information (e.g., that includes the machine state information and/or the machine properties information), and the projected path 310 of the machine 100, that a particular potential path 308 (e.g., the second potential path 308-B shown in FIG. 3E), of the respective potential paths 308, to the particular target 306 (e.g., the second target 306-B shown in FIG. 3E) is associated with the projected path 310. That is, the controller 114 may determine that the particular potential path 308 is the most similar, of the respective potential paths 308, to the projected path 310. Accordingly, the controller 114 may determine (e.g., based on determining that the particular potential path 308 to the particular target 306 is associated with the projected path 310) that the particular target 306 is the destination 312 of the projected path 310. That is, the controller 114 may determine that the particular target 306 is the destination 312 of the projected path 310 because the particular target 306 is associated with the particular potential path 308, which is associated with the projected path 310 (e.g., because the particular potential path 308 is the most similar, of the respective potential paths 308, to the projected path 310).

As another example, the controller 114 may determine, using a cost function, or another analysis technique, and based on the respective potential paths 308 of the machine 100, the machine information (e.g., that includes the machine state information and/or the machine properties information), and the projected path 310 of the machine 100, that multiple potential paths 308, of the respective potential paths 308, are associated with the projected path 310. That is, the controller 114 may determine that the multiple potential paths 308 are similar, or are sufficiently similar, to the projected path 310. The controller 114 may therefore determine, based on determining that the multiple potential paths 308 are associated with the projected path 310, and based on the perception information, respective distances to the plurality of targets 306 (e.g., straight line distances to the plurality of targets 306, travel distances to the plurality of targets 306, or other types of distances). Accordingly, the controller 114 may determine that a distance to a particular target 306, which is associated with a particular potential path 308, of the multiple potential paths 308, is less than or equal to respective distances to one or more other targets 306, of the plurality of targets 306, that are associated with one or more other potential paths 308 of the multiple potential paths 308. That is, the controller 114 may determine that a particular target 306 is closer to the machine 100 than one or more other targets 306 of the plurality of targets 306. The controller 114, therefore, may determine (e.g., based on determining that the distance to the particular target 306 is less than or equal to the respective distances to the one or more other targets 306) that the particular target 306 is the destination 312 of the projected path 310.

As further shown in FIG. 4B, and by reference number 412, the controller 114 may cause (e.g., based on determining that the particular target 306 is the destination 312 of the projected path 310) a portion of the perception information that is associated with the particular target 306 (e.g., a portion of the perception information that indicates a position of the particular target 306) to be used in association with an automatic control operation. That, is the controller 114 may process the portion of the perception information that is associated with the particular target 306, and may not process, or may exclude from processing, any other portion of the perception information that is not associated with the particular target 306, to facilitate the automatic control operation. For example, the controller 114 may process the portion of the perception information that indicates the position of the particular target 306, and not any other portion of the perception information that does not indicate the position of the particular target 306. By only processing only a portion of the perception information that is relevant to the destination 312 of the projected path 310, computing resources (e.g., processing resources, memory resources, communication resources, and/or power resources, among other examples) of the controller 114 are conserved that would otherwise be used to process other portions of the perception information.

The automatic control operation may be associated with at least one of the machine 100, or the implement 110 and the linkage 122. For example, the automatic control operation may cause the machine 100 to travel to or from the particular target 306 (e.g., as the destination 312 of the projected path 310 of the machine 100) and/or may cause the implement 110 and the linkage 122 to move to particular positions (e.g., based on a proximity of the machine 100 to the particular target 306).

The controller 114 may repeatedly perform one or more operations described herein in relation to FIGS. 4A-4B, such as at subsequent time (e.g., after causing the portion of the perception information that is associated with the particular target 306 to be used in association with the automatic control operation). For example, the controller 114 may obtain other perception information from the perception sensor system 112 and/or other machine information from the machine sensor system 130, and may thereby determine other respective potential paths 308 of the machine to the plurality of targets 306, such as in a similar manner as that described herein in relation to reference numbers 402, 404, and 406 in FIG. 4A. The controller 114 then may determine another projected path 310 of the machine 100, and may determine (e.g., based on the other respective potential paths 308 and the other projected path 310), that a particular target 306 (e.g., that is the same as, or different than, the particular target 306 described elsewhere herein), of the plurality of targets 306, is a destination 312 of the other projected path 310, such as in a similar manner as that described herein in relation to reference numbers 408 and 410 in FIG. 4B. Accordingly, the controller 114 may cause a portion of the other perception information that is associated with the particular target 306 to be used in association with the automatic control operation, such as in a similar manner as that described herein in relation to reference number 412 in FIG. 4B.

As indicated above, FIGS. 4A-4B are provided as an example. Other examples may differ from what is described in connection with FIGS. 4A-4B.

FIG. 5 is a diagram of example components of a device 500 associated with determining that a target is a destination of a projected path of a machine. The device 500 may correspond to the perception sensor system 112, the controller 114, the plurality of perception sensors 126, the machine sensor system 130, and/or the plurality of machine sensors 132. The perception sensor system 112, the controller 114, the plurality of perception sensors 126, the machine sensor system 130, and/or the plurality of machine sensors 132 may include one or more devices 500 and/or one or more components of the device 500. As shown in FIG. 5, the device 500 may include a bus 510, a processor 520, a memory 530, an input component 540, an output component 550, and/or a communication component 560.

The bus 510 may include one or more components that enable wired and/or wireless communication among the components of the device 500. The bus 510 may couple together two or more components of FIG. 5, such as via operative coupling, communicative coupling, electronic coupling, and/or electric coupling. For example, the bus 510 may include an electrical connection (e.g., a wire, a trace, and/or a lead) and/or a wireless bus. The processor 520 may include a central processing unit, a graphics processing unit, a microprocessor, a controller, a microcontroller, a digital signal processor, a field-programmable gate array, an application-specific integrated circuit, and/or another type of processing component. The processor 520 may be implemented in hardware, firmware, or a combination of hardware and software. The processor 520 may include one or more processors capable of being programmed to perform one or more operations or processes described elsewhere herein.

The memory 530 may include volatile and/or nonvolatile memory. For example, the memory 530 may include random access memory (RAM), read only memory (ROM), a hard disk drive, and/or another type of memory (e.g., a flash memory, a magnetic memory, and/or an optical memory). The memory 530 may include internal memory (e.g., RAM, ROM, or a hard disk drive) and/or removable memory (e.g., removable via a universal serial bus connection). The memory 530 may be a non-transitory computer-readable medium. The memory 530 may store information, one or more instructions, and/or software (e.g., one or more software applications) related to the operation of the device 500. The memory 530 may include one or more memories that are coupled (e.g., communicatively coupled) to one or more processors (e.g., processor 520), such as via the bus 510. Communicative coupling between a processor 520 and a memory 530 may enable the processor 520 to read and/or process information stored in the memory 530 and/or to store information in the memory 530.

The input component 540 may enable the device 500 to receive input, such as user input and/or sensed input. For example, the input component 540 may include a touch screen, a keyboard, a keypad, a mouse, a button, a microphone, a switch, a sensor, a global positioning system sensor, a global navigation satellite system sensor, an accelerometer, a gyroscope, and/or an actuator. The output component 550 may enable the device 500 to provide output, such as via a display, a speaker, and/or a light-emitting diode. The communication component 560 may enable the device 500 to communicate with other devices via a wired connection and/or a wireless connection. For example, the communication component 560 may include a receiver, a transmitter, a transceiver, a modem, a network interface card, and/or an antenna.

The device 500 may perform one or more operations or processes described herein. For example, a non-transitory computer-readable medium (e.g., memory 530) may store a set of instructions (e.g., one or more instructions or code) for execution by the processor 520. The processor 520 may execute the set of instructions to perform one or more operations or processes described herein. Execution of the set of instructions, by one or more processors 520, causes the one or more processors 520 and/or the device 500 to perform one or more operations or processes described herein. Hardwired circuitry may be used instead of or in combination with the instructions to perform one or more operations or processes described herein. Additionally, or alternatively, the processor 520 may be configured to perform one or more operations or processes described herein. Thus, implementations described herein are not limited to any specific combination of hardware circuitry and software.

The number and arrangement of components shown in FIG. 5 are provided as an example. The device 500 may include additional components, fewer components, different components, or differently arranged components than those shown in FIG. 5. A set of components (e.g., one or more components) of the device 500 may perform one or more functions described as being performed by another set of components of the device 500.

INDUSTRIAL APPLICABILITY

Implementations described herein may be used with any machine that includes a controller and a perception sensor system that includes one or more perception sensors, such as any machine that utilizes an implement and linkage, such as a wheel loader that includes an implement and linkage to load, carry, and dump material (e.g., into a dump target).

A machine can include one or more perception sensors to detect a position of a target (e.g., a dump target, such as a dump truck), such as to facilitate an automatic control operation (e.g., that controls a movement of the machine or an operation of an implement and a linkage of the machine, such as in relation to the target). For example, the one or more perception sensors can capture and pass perception information that indicates a position of the target to a controller of the machine, and the controller thereby causes an automatic control operation to be performed with respect to the target based on the perception information.

However, in many cases, many targets are available, and the one or more perception sensors capture and pass perception information that indicates respective positions of multiple targets to the controller of the machine. Consequently, because the controller is unable to determine which of the targets is an intended destination of the machine, the controller causes the automatic control operation to be performed any time the machine approaches within a threshold distance of a target, even when the target is not the intended destination of the machine.

This can affect a performance of the machine (e.g., due to unnecessary performance of the automatic control operation). For example, when the automatic control operation includes lifting the implement (e.g., that holds material for eventual dumping) and the linkage to a high position, the center of the gravity of the machine is elevated. This can reduce a stability and a control of the machine, which can lead to spillage of the material carried by the machine. Further, unnecessary lifting of the implement and the linkage, particularly when carrying a heavy load of material, can increase wear and tear on the implement and on the linkage, which can impact a performance of, as well as reduce an operable life of, the implement and the linkage, and the machine.

In some implementations, a controller of a machine may obtain perception information (e.g., that indicates respective positions of a plurality of targets) from a perception sensor system and machine information from a machine sensor system. The controller determines, based on the perception information and the machine information, respective potential paths of the machine to the plurality of targets. The controller also determines, based on the machine information, a projected path of the machine. Accordingly, the controller determines, based on the respective potential paths of the machine and the projected path of the machine, that a particular target, of the plurality of targets, is a destination of the projected path of the machine. For example, the controller may determine that a particular potential path is the most similar, of the respective potential paths, to the projected path and may therefore determine that the particular target, because the particular target is associated with the particular potential path, is the destination of the projected path. Accordingly, the controller may cause a portion of the perception information that is associated with the particular target to be used in association with an automatic control operation.

In this way, the controller of the machine is able to determine that a target, of multiple targets, is a destination of a projected path of a machine, which is not otherwise practically possible when only relying on perception information captured by one or more perception sensors. Accordingly, the controller prevents, or at least reduces a likelihood of, an automatic control operation being erroneously performed by the machine when in proximity of a target that is not an intended destination of the machine. Thus, unnecessary lifting of an implement (e.g., that holds material for eventual dumping) and a linkage of the machine is reduced, such as when the automatic control operation includes lifting the implement and the linkage to a high position,. Therefore a stability and a control of the machine is improved (e.g., by not unnecessarily elevating a center of gravity of the machine), which can reduce a likelihood of spillage of the material carried by the implement of machine. Additionally, a wear and tear on the on the implement and the linkage is reduced, which improves a performance of, as well as increases an operable life of, the implement and the linkage, and the machine.