Determination system and determination method

Abstract

According to the present invention, an acquisition unit acquires position data indicating the position of a work vehicle on the basis of a global navigation satellite system (GNSS). A determination unit determines the accuracy of the position data on the basis of vehicle body data of the work vehicle and the position data.

Description

Determination system and determination method

[0001] This application claims priority to Japanese Patent Application No. 2023-141774, filed on August 31, 2023, the contents of which are incorporated herein by reference.

[0002] Patent Document 1 discloses a work vehicle that causes the cutting edge of a blade to follow a design surface. According to the technology disclosed in Patent Document 1, a control device identifies the position of the work vehicle using a Global Navigation Satellite System (GNSS), and determines a target height for the blade based on the identified position.

[0003] International Publication No. 2015 / 083469

[0004] Positioning using GNSS is performed by calculating the difference between the transmission time and reception time of radio waves transmitted from GNSS satellites. Therefore, if multipath of radio waves occurs due to terrain or structures, the positioning results may become inaccurate. One example of an objective of the present disclosure is to provide a determination system and a determination method that can determine whether positioning using GNSS is accurate.

[0005] According to one aspect of the present disclosure, the determination system includes an acquisition unit that acquires position data indicating the position of a work vehicle based on GNSS, and a determination unit that determines the accuracy of the position data based on the body data of the work vehicle and the position data.

[0006] According to the above aspect, as one example, the determination system can determine whether or not positioning by GNSS is accurate.

[0007] FIG. 1 is a side view of a work vehicle according to a first embodiment. FIG. 2 is a diagram showing the internal configuration of a driver's cab according to the first embodiment. FIG. 3 is a schematic diagram showing a power system of a work vehicle according to the first embodiment. FIG. 4 is a diagram showing the configuration of a measurement system and a control device of a work vehicle according to the first embodiment. FIG. 5 is a flowchart showing a method for determining the accuracy of a GNSS sensor according to the first embodiment. FIG. 6 is a flowchart showing a method for determining the accuracy of a GNSS sensor according to a second embodiment. FIG. 7 is a diagram showing the configuration of a measurement system and a control device of a work vehicle according to a third embodiment. FIG. 8 is a diagram showing the configuration of a measurement system and a control device of a work vehicle according to a fourth embodiment.

[0008] <First embodiment> Hereinafter, an embodiment will be described in detail with reference to the drawings. Fig. 1 is a side view of a work vehicle according to a first embodiment. A work vehicle 100 according to the first embodiment is, for example, a bulldozer. The work vehicle 100 includes a vehicle body 110, a traveling device 120, a work implement 130, and a driver's cab 140.

[0009] The traveling device 120 is provided on the lower part of the vehicle body 110. The traveling device 120 has a pair of crawlers 121 and sprockets 122. The pair of crawlers 121 is provided on the left and right sides of the vehicle body 110. The crawlers 121 are rotated by the drive of the sprockets 122, causing the work vehicle 100 to travel.

[0010] The work machine 130 is used to excavate and transport excavation targets such as earth and sand. The work machine 130 includes a lift frame 131, a blade 132, a blade lift cylinder 133, a ripper 134, and a ripper lift cylinder 135. The blade 132 is disposed in front of the vehicle body 110. The ripper 134 is disposed in the rear of the vehicle body 110.

[0011] The base end of the lift frame 131 is attached to the side of the vehicle body 110 via a pin extending in the vehicle width direction. The tip end of the lift frame 131 is attached to the back surface of the blade 132 via a ball joint. This supports the blade 132 so that it can move up and down relative to the vehicle body 110. A cutting edge is provided at the lower end of the blade 132. The blade lift cylinder 133 is a hydraulic cylinder. The base end of the blade lift cylinder 133 is attached to the side of the vehicle body 110. The tip end of the blade lift cylinder 133 is attached to the lift frame 131. The blade lift cylinder 133 extends and contracts using hydraulic oil, thereby driving the lift frame 131 and the blade 132 in the raising and lowering directions.

[0012] A base end of the ripper lift cylinder 135 is attached to the vehicle body 110. A tip end of the ripper lift cylinder 135 is rotatably attached to the ripper 134. The ripper lift cylinder 135 is extended and contracted by hydraulic oil, thereby driving the ripper 134 in a raising or lowering direction.

[0013] The operator's cab 140 is a space where an operator sits and operates the work vehicle 100. The operator's cab 140 is provided on top of the vehicle body 110. Figure 2 is a diagram showing the internal configuration of the operator's cab 140 according to the first embodiment. Inside the operator's cab 140, there are provided a seat 141, a console 142, a blade operation lever 143, a ripper operation lever 144, a travel operation lever 145, a brake pedal 146, and a deceleration pedal 147. The brake pedal 146 and the deceleration pedal 147 may be configured as a single pedal.

[0014] An operation panel, instruments, and switches are attached to the console 142. The operator can visually check the status of the work vehicle 100 by checking the console 142. The blade operation lever 143 is operated to set the amount of movement of the blade 132 when raised or lowered. The blade operation lever 143 receives a lowering operation when tilted forward, and a raising operation when tilted rearward. The ripper operation lever 144 is operated to set the amount of movement of the ripper 134 when raised or lowered. The ripper operation lever 144 receives a lowering operation when tilted forward, and a raising operation when tilted rearward. The blade operation lever 143 may also receive a tilt operation to rotate the blade 132 about an axis in the fore-and-aft direction, and an angle operation to rotate the blade 132 about an axis in the up-and-down direction. The travel operation lever 145 is operated to set the traveling direction of the traveling device 120. The travel control lever 145 is tilted forward to accept a forward movement operation, and tilted backward to accept a reverse movement operation. The travel control lever 145 is also tilted left to accept a left turn operation, and tilted right to accept a right turn operation. The travel control lever 145 is also provided with a gear change button for inputting a speed range. The brake pedal 146 is operated to brake the travel device 120. The deceleration pedal 147 is operated to reduce the rotation speed of the travel device 120.

[0015] 3 is a schematic diagram showing the power system of the work vehicle according to Embodiment 1. The work vehicle 100 is equipped with an engine 210, a PTO (Power Take Off) 220, a pair of HSTs (Hydro Static Transmissions) 230, a hydraulic pump 250, and a proportional control valve 260.

[0016] The engine 210 is, for example, a diesel engine. The PTO 220 transmits a portion of the driving force of the engine 210 to the hydraulic pump 250. In other words, the PTO 220 distributes the driving force of the engine 210 to the HST 230 and the hydraulic pump 250. The HST 230 changes the speed of the driving force input to its input shaft and outputs it from its output shaft. The HST 230 includes a hydraulic pump driven by the rotation of the input shaft and a hydraulic motor that rotates the output shaft. The HST 230 controls the rotation speed of the output shaft by controlling the discharge flow rate of the hydraulic pump. The input shaft of the HST 230 is connected to the PTO 220, and the output shaft is connected to the sprocket 122. In other words, the HST 230 transmits the driving force of the engine 210 distributed by the PTO 220 to the sprocket 122. The output shafts of the pair of HSTs 230 are connected to the left sprocket 122 and the right sprocket 122, respectively. The hydraulic pump 250 is driven by driving force from the engine 210. The hydraulic oil discharged from the hydraulic pump 250 is supplied to the blade lift cylinder 133 via a proportional control valve 260. The proportional control valve 260 controls the flow rate of the hydraulic oil discharged from the hydraulic pump 250. In addition to the proportional control valve 260, the hydraulic pump 250 may supply hydraulic oil to other destinations such as a steering clutch (not shown).

[0017] <<Measurement System>> Figure 4 is a diagram showing the configuration of the measurement system and control device of work vehicle 100 according to the first embodiment. The measurement system of work vehicle 100 acquires vehicle body data that indicates the state of work vehicle 100. Work vehicle 100 is equipped with a rotation sensor 310, a dynamometer 320, an oil pressure sensor 330, an IMU (Inertial Measurement Unit) 340, a blade stroke sensor 350, a ripper stroke sensor 360, and a GNSS sensor 370.

[0018] A rotation sensor 310 is provided on each of the left and right sprockets 122. The rotation sensor 310 calculates the number of rotations of the sprocket 122.

[0019] The dynamometer 320 is provided on the engine 210. The dynamometer 320 measures the power generated by the engine 210.

[0020] An oil pressure sensor 330 is provided on each of the hydraulic motors of the left and right HSTs 230. The oil pressure sensor 330 measures the oil pressure on the outlet side of the hydraulic motor.

[0021] The IMU 340 is provided on the vehicle body 110. The IMU 340 measures the tilt angles of the vehicle body 110 in the roll and pitch directions, and the angular displacement in the yaw direction. The vehicle body coordinate system is, for example, an orthogonal coordinate system whose origin is the center of the traveling device 120 and is represented by an X axis extending in the longitudinal direction of the vehicle body, a Y axis extending in the lateral direction of the vehicle body, and a Z axis extending in the vertical direction of the vehicle body. The direction of rotation of the vehicle body 110 about the X axis is defined as the roll direction, the direction of rotation of the vehicle body 110 about the Y axis is defined as the pitch direction, and the direction of rotation of the vehicle body 110 about the Z axis is defined as the yaw direction.

[0022] The blade stroke sensor 350 is provided on the blade lift cylinder 133. The blade stroke sensor 350 measures the stroke amount of the blade lift cylinder 133. The stroke amount measured by the blade stroke sensor 350 can be converted into the position of the cutting edge of the blade 132 in the vehicle body coordinate system. Specifically, the rotation angle of the lift frame 131 is calculated based on the stroke amount of the blade lift cylinder 133. Because the shapes of the lift frame 131 and the blade 132 are known, the position of the cutting edge of the blade 132 can be identified from the rotation angle of the lift frame 131. Note that work vehicles 100 according to other embodiments may detect the rotation angle of the lift frame 131 using other sensors, such as an encoder.

[0023] The ripper stroke sensor 360 is provided on the ripper lift cylinder 135. The ripper stroke sensor 360 measures the stroke amount of the ripper lift cylinder 135. The stroke amount measured by the ripper stroke sensor 360 can be converted into the position of the ripper cutting edge in the vehicle body coordinate system. Note that work vehicles 100 according to other embodiments may detect the rotation angle of the ripper 134 using another sensor such as an encoder. Note that work vehicles 100 according to other embodiments do not need to be equipped with a sensor that detects the state of the ripper 134. In this case, for example, the control device 400 may determine whether the ripper 134 is raised or lowered based on the amount of operation of the ripper operating lever 144.

[0024] The GNSS sensor 370 is provided on the vehicle body 110. The GNSS sensor 370 measures the position and orientation of the vehicle body 110 in a global coordinate system based on signals from GNSS satellites. The GNSS sensor 370 may perform real-time kinematic (RTK) positioning. In this case, the GNSS sensor 370 communicates with a fixed station provided at the work site and corrects the measurement data. The GNSS sensor 370 outputs accuracy information indicating the positioning accuracy in addition to the position and orientation as measurement data. The accuracy information may indicate, for example, whether correction by the fixed station was successful or the number of satellites that were able to measure. The accuracy information may also be a numerical value indicating the positioning accuracy. The GNSS sensor 370 is an example of a position estimation unit that estimates the position and attitude of the work vehicle 100 based on measurement data.

[0025] <<Control Device>> The work vehicle 100 is equipped with a control device 400 for controlling the work vehicle 100. The control device 400 outputs control signals to the fuel injection device of the engine 210, the HST 230, and the proportional control valve 260 in accordance with the amount of operation of each operation device in the driver's cab 140 (console 142, blade operation lever 143, ripper operation lever 144, travel operation lever 145, brake pedal 146, and deceleration pedal 147). The control device 400 also measures the position of the work vehicle 100 based on measurement data from the measurement system, and displays this on the console 142. The control device 400 may autonomously control the power system based on the position of the work vehicle 100 measured based on the measurement data from the measurement system.

[0026] The control device 400 is a computer that includes a processor 410 , a main memory 430 , a storage 450 , and an interface 470 .

[0027] The storage 450 is a non-transitory tangible storage medium. Examples of the storage 450 include a magnetic disk, a magneto-optical disk, and a semiconductor memory. The storage 450 may be an internal medium directly connected to the bus of the control device 400, or an external medium connected to the control device 400 via the interface 470 or a communication line. The storage 450 stores a program for controlling the work vehicle 100. The storage 450 also stores a motion state model 451, which is a machine learning model for calculating the velocity and angular velocity of the vehicle body 110 that has been learned in advance. The motion state model 451 will be described later.

[0028] In another embodiment, the control device 400 may include a custom large-scale integrated circuit (LSI) such as a programmable logic device (PLD) in addition to or instead of the above configuration. Examples of PLDs include programmable array logic (PAL), generic array logic (GAL), complex programmable logic device (CPLD), and field programmable gate array (FPGA). In this case, some or all of the functions implemented by the processor 410 may be implemented by the integrated circuit.

[0029] By executing a program, the processor 410 is provided with an operation amount acquisition unit 411, a measurement data acquisition unit 412, a work implement height calculation unit 413, a first movement amount estimation unit 414, a second movement amount estimation unit 415, a determination unit 416, and an output unit 417. Measurement data from the measurement system is input to the processor 410 via an interface 470.

[0030] The operation amount acquisition unit 411 acquires operation amounts from the blade operation lever 143 , the ripper operation lever 144 , the travel operation lever 145 , the brake pedal 146 , and the deceleration pedal 147 .

[0031] The measurement data acquisition unit 412 acquires measurement data from the rotation sensor 310, the dynamometer 320, the oil pressure sensor 330, the IMU 340, the blade stroke sensor 350, the ripper stroke sensor 360, and the GNSS sensor 370. That is, the measurement data acquisition unit 412 acquires measurement data on the yaw angle of the vehicle body 110, the roll angle of the vehicle body 110, the pitch angle of the vehicle body 110, the rotation speeds of the left and right sprockets 122, the stroke amount of the blade lift cylinder 133, and the stroke amount of the ripper lift cylinder 135.

[0032] The work implement height calculation unit 413 calculates the height of the cutting edge of the blade 132 relative to the vehicle body, based on the measurement data of the stroke amount of the blade lift cylinder 133 acquired by the measurement data acquisition unit 412. In other words, the work implement height calculation unit 413 calculates the height of the cutting edge of the blade 132 in the vehicle body coordinate system from pre-stored dimensional information of the vehicle body 110 and the measurement data of the stroke amount of the blade lift cylinder 133. The work implement height calculation unit 413 also calculates the height of the cutting edge of the ripper 134 relative to the vehicle body, based on the measurement data of the stroke amount of the ripper lift cylinder 135 acquired by the measurement data acquisition unit 412.

[0033] The first movement amount estimation unit 414 estimates the speed and angular velocity of the work vehicle 100 by inputting the operation amount acquired by the measurement data acquisition unit 412 and the measurement data acquired by the measurement data acquisition unit 412 into the motion state model 451.

[0034] The second movement amount estimation unit 415 estimates the speed and angular velocity of the work vehicle 100 based on the measurement data of the GNSS sensor 370 acquired by the measurement data acquisition unit 412. Specifically, the second movement amount estimation unit 415 calculates the turning angular velocity of the work vehicle 100 based on the difference between the orientation indicated by the latest measurement data of the GNSS sensor 370 acquired by the measurement data acquisition unit 412 and the orientation indicated by the previous measurement data. The second movement amount estimation unit 415 calculates the speed of the work vehicle 100 in the global coordinate system based on the difference between the three-dimensional position indicated by the latest measurement data of the GNSS sensor 370 acquired by the measurement data acquisition unit 412 and the three-dimensional position indicated by the previous measurement data, and converts the speed in the global coordinate system into velocities in the X-axis, Y-axis, and Z-axis directions relative to the work vehicle 100 based on the orientation indicated by the latest measurement data of the GNSS sensor 370.

[0035] The determination unit 416 determines whether the measurement data of the GNSS sensor 370 is accurate based on the velocity and angular velocity estimated by the first movement amount estimation unit 414 and the velocity and angular velocity estimated by the second movement amount estimation unit 415. Specifically, the determination unit 416 determines that the measurement data of the GNSS sensor 370 is accurate when all of the following are smaller than predetermined thresholds: a difference between the velocity in the X-axis direction estimated by the first movement amount estimation unit 414 and the velocity in the X-axis direction estimated by the second movement amount estimation unit 415; a difference between the velocity in the Y-axis direction estimated by the first movement amount estimation unit 414 and the velocity in the Y-axis direction estimated by the second movement amount estimation unit 415; a difference between the velocity in the Z-axis direction estimated by the first movement amount estimation unit 414 and the velocity in the Z-axis direction estimated by the second movement amount estimation unit 415; and a difference between the turning angular velocity estimated by the first movement amount estimation unit 414 and the turning angular velocity estimated by the second movement amount estimation unit 415.

[0036] The output unit 417 outputs the position and orientation indicated by the measurement data of the GNSS sensor 370 to the console 142. If the determination unit 416 determines that the measurement data of the GNSS sensor 370 is not accurate, the output unit 417 outputs a warning indicating that the GNSS sensor 370 may be inaccurate.

[0037] <Motion State Model> The motion state model 451 according to the first embodiment receives input of the operation amount of the travel control lever 145, the rotation speeds of the left and right sprockets 122, measurement data from the IMU 340, measurement data of the pressure of the HST 230 (hydraulic motor), measurement data from the dynamometer 320, the speed gear, the height of the blade 132, and the height of the ripper 134, and outputs the velocity in the X-axis direction, the Y-axis direction, the Z-axis direction, the angular velocity around the X-axis (roll angular velocity), the angular velocity around the Y-axis (pitch angular velocity), and the angular velocity around the Z-axis (swing angular velocity) in the vehicle body coordinate system. The motion state model 451 is a machine learning model such as a neural network. The motion state model 451 may be configured using a CNN or an RNN. In particular, when the motion state model 451 is configured using a recurrent neural network such as a long short-term memory (LSTM) or a transformer, the motion state of the work vehicle 100 can be appropriately estimated in accordance with changes in the state over time. Note that the motion state model 451 according to other embodiments does not necessarily need to use all of the operation amount of the travel operation lever 145, the rotation speeds of the left and right sprockets 122, the measurement data of the IMU 340, the measurement data of the pressure of the HST 230 (hydraulic motor), the measurement data of the dynamometer 320, the speed gear, the height of the blade 132, and the height of the ripper 134 to estimate the motion state. As will be described later, the motion state model 451 may calculate the movement amount of the work vehicle 100 using at least one of the height of the blade 132, the height of the ripper 134, the operation amount of the travel operation lever 145, the speed gear, the output of the engine 210, and the pressure of the hydraulic motor of the HST 230.

[0038] The motion state model 451 has been trained using a training data set consisting of a combination of the following input samples and output samples. The input samples are the operation amount of the travel control lever 145, the rotation speeds of the left and right sprockets 122, the measurement data of the IMU 340, the pressure of the HST 230, the power of the engine 210, the speed gear, the height of the blade 132, and the height of the ripper 134. The output samples are the velocity in the X-axis direction, the Y-axis direction, the Z-axis direction, the angular velocity around the X-axis, the angular velocity around the Y-axis, the angular velocity around the Z-axis, the roll angle, and the pitch angle of the vehicle body coordinate system. The velocity of the work vehicle 100 related to the output sample may be a value calculated from the measurement data of the GNSS sensor 370. The angular velocity and attitude angle of the work vehicle 100 related to the output sample may be values ​​identified from the measurement data of the IMU 340.

[0039] The rotation speed of the sprocket 122 has a strong correlation with the movement distance of the work vehicle 100. In typical autonomous navigation, the movement distance is often calculated from the rotation speed. However, when slippage occurs in the crawler 121, the movement distance calculated from the rotation speed of the sprocket 122 may differ from the actual movement distance.

[0040] The measurement data of IMU 340 has a strong correlation with the angular velocity and tilt angle of work vehicle 100. In typical autonomous navigation, the direction of travel of the vehicle body is often calculated from the angular velocity and acceleration of IMU 340. However, when work vehicle 100 is traveling while performing work, vehicle body 110 vibrates, and there is a possibility that noise due to the vibrations may be added to the measurement data of IMU 340.

[0041] The height of the blade 132 and the height of the ripper 134 relate to the reaction force that the work vehicle 100 receives from the work object. The lower the blade 132 and the ripper 134 are, the more likely it is that the blade 132 and the ripper 134 will be in contact with the work object and performing work. When the work vehicle 100 is performing work, the work vehicle 100 will receive a reaction force from the work object. The height of the blade 132 and the height of the ripper 134 are examples of values ​​related to the state of the work implement 130.

[0042] The combination of the amount of operation of the travel control lever 145 and the rotation speed of the sprocket 122 relates to the reaction force that the work vehicle 100 receives from the work object. Furthermore, measurement data related to the power of the travel device 120, such as the output of the engine 210 and the pressure of the hydraulic motor of the HST 230, relates to the reaction force that the work vehicle 100 receives from the work object and the resulting slippage.

[0043] The speed gear is related to slippage that occurs due to a reaction force that the work vehicle 100 receives from a work object. The speed gear determines the maximum speed and torque of the work vehicle 100. Therefore, the lower the speed gear, the less likely slippage occurs, and the higher the speed gear, the more likely slippage occurs.

[0044] When work vehicle 100 receives a strong reaction force from the work object, the rotation speed of sprocket 122 decreases due to static friction force acting on traveling device 120. As the rotation speed of sprocket 122 decreases, the rotation speed of the hydraulic motor of HST 230, which rotates sprocket 122, decreases. As the rotation speed of the hydraulic motor of HST 230 decreases, the pressure between the hydraulic pump and hydraulic motor of HST 230 increases. As the pressure of HST 230 increases, the rotation speed of the input shaft of HST 230 decreases, and the rotation speed of engine 210 decreases.

[0045] Subsequently, when slippage occurs due to a reaction force from the work object, the frictional force acting on the traveling device 120 changes from static frictional force to kinetic frictional force, and the rotation speed of the hydraulic motor of the HST 230 and the sprocket 122 increases. As the rotation speed of the hydraulic motor of the HST 230 increases, the pressure between the hydraulic pump and hydraulic motor of the HST 230 decreases. The decrease in pressure of the HST 230 increases the rotation speed of the input shaft of the HST 230, and the rotation speed of the engine 210 increases. For this reason, if the movement amount of the work vehicle 100 were calculated directly from the rotation speed of the sprocket 122, an error in the movement amount would be large. On the other hand, the motion state model 451 calculates the movement amount of the work vehicle 100 using at least one of the height of the blade 132, the height of the ripper 134, the operation amount of the travel operation lever 145, the speed gear, the output of the engine 210, and the pressure of the hydraulic motor of the HST 230, thereby making it possible to calculate the movement amount while suppressing the effects of slippage.

[0046] Furthermore, when the work vehicle 100 receives a reaction force from the work object, the traveling gear 120 may lift due to the reaction force. For example, if the traveling gear 120 moves forward while the blade 132 receives a reaction force, the vehicle body 110 may move forward while the front of the traveling gear 120 lifts. In this case, the traveling direction of the work vehicle 100 does not match the inclination angle of the vehicle body 110 measured by the IMU 340, and therefore a deviation occurs in the direction of movement in typical autonomous navigation. On the other hand, the motion state model 451 calculates the amount of movement of the work vehicle 100 using at least one of the height of the blade 132, the height of the ripper 134, the operation amount of the travel operation lever 145, the output of the engine 210, and the pressure of the hydraulic motor of the HST 230, thereby making it possible to estimate the traveling direction of the work vehicle 100 even if the traveling gear 120 lifts. Note that the inclination angle output by the motion state model 451 may be the inclination angle of the traveling direction of the work vehicle 100, rather than the inclination angle of the vehicle body 110.

[0047] <<Method for determining accuracy of GNSS sensor 370>> Next, a method for determining the accuracy of the GNSS sensor 370 according to the first embodiment will be described. Figure 5 is a flowchart showing the method for determining the accuracy of the GNSS sensor 370 according to the first embodiment. The control device 400 repeatedly executes the flowchart shown in Figure 5 while the work vehicle 100 is operating.

[0048] The operation amount acquisition unit 411 acquires the operation amount and speed gear of the travel operation lever 145 (step S1). The measurement data acquisition unit 412 acquires measurement data from the rotation sensor 310, the dynamometer 320, the oil pressure sensor 330, the IMU 340, the blade stroke sensor 350, the ripper stroke sensor 360, and the GNSS sensor 370 (step S2). Next, the work implement height calculation unit 413 calculates the heights of the cutting edges of the blade 132 and the ripper 134 based on the measurement data of the stroke amount of the blade lift cylinder 133 and the stroke amount of the ripper lift cylinder 135 acquired by the measurement data acquisition unit 412 (step S3).

[0049] The first movement amount estimator 414 inputs the operation amount and speed stage of the travel operation lever 145 acquired in step S1, the rotation speeds of the left and right sprockets 122 acquired in step S2, the measurement data of the IMU 340, the pressure measurement data of the HST 230, and the measurement data of the dynamometer 320, as well as the height of the blade 132 and the height of the ripper 134 calculated in step S3, into the motion state model 451. As a result, the first movement amount estimator 414 estimates the speed in the X-axis direction, the speed in the Y-axis direction, the speed in the Z-axis direction, and the turning angular velocity of the work vehicle 100 (step S4).

[0050] The second movement amount estimation unit 415 estimates the speed in the X-axis direction, the Y-axis direction, the Z-axis direction, and the turning angular velocity of the work vehicle 100 based on the measurement data of the GNSS sensor 370 acquired by the measurement data acquisition unit 412 (step S5).

[0051] The determination unit 416 calculates the differences between the X-axis direction velocity, Y-axis direction velocity, Z-axis direction velocity, and turning angular velocity estimated by the first movement amount estimation unit 414 and the second movement amount estimation unit 415 (step S6). The determination unit 416 determines whether each of the calculated differences is smaller than a predetermined threshold value (step S7). If all of the differences are smaller than the threshold value (step S7: YES), the determination unit 416 determines that the measurement data of the GNSS sensor 370 is accurate. The output unit 417 outputs the position and orientation indicated by the measurement data of the GNSS sensor 370 acquired in step S2 to the console 142 (step S8).

[0052] If at least one difference is equal to or greater than the threshold value (step S7: NO), the determination unit 416 determines that the measurement data of the GNSS sensor 370 is inaccurate. The output unit 417 outputs a warning indicating that the measurement data of the GNSS sensor 370 is inaccurate to the console 142 (step S9). The output unit 417 may output the position and orientation indicated by the measurement data of the GNSS sensor 370 acquired in step S2 to the console 142, or may output only the warning.

[0053] <<Actions and Effects>> As described above, the control device 400 according to the first embodiment includes a measurement data acquisition unit 412, a first movement amount estimation unit 414, and a determination unit 416. The measurement data acquisition unit 412 acquires position data indicating the position of the work vehicle 100 based on GNSS. The first movement amount estimation unit 414 estimates a value related to the movement amount of the work vehicle 100 based on vehicle body data of the work vehicle 100. The determination unit 416 determines the accuracy of the position data based on the value related to the movement amount and the position data. As a result, when the accuracy of the measurement data of the GNSS sensor 370 decreases due to the effects of multipath or the like, the position indicated by the measurement data will deviate significantly from the past position, resulting in a large discrepancy with the movement amount calculated from the vehicle body data. Therefore, the control device 400 according to the first embodiment can determine the accuracy of the measurement data of the GNSS sensor 370.

[0054] Furthermore, the control device 400 according to the first embodiment estimates the amount of travel using values ​​related to the power of the travel device 120 of the work vehicle 100 and values ​​related to the state of the work implement 130. Both the power of the travel device 120 and the state of the work implement 130 are related to slippage or floating of the travel device 120, and therefore, by using these, the amount of travel of the work vehicle 100 can be accurately estimated. On the other hand, the control device 400 according to other embodiments may estimate the amount of travel using autonomous navigation from, for example, the rotation speed of the sprocket 122, without using the power of the travel device 120 or the state of the work implement 130. For example, in an environment where the influence of slippage or floating of the work vehicle 100 is small, the accuracy of the measurement data of the GNSS sensor 370 can be determined even if the amount of travel estimated by autonomous navigation is used. Furthermore, the control device 400 according to other embodiments may estimate the amount of travel of the work vehicle 100 using one of the power of the travel device 120 and the state of the work implement 130.

[0055] Second Embodiment A control device 400 according to a second embodiment outputs, as an estimation result of the first movement amount estimator 414, not only the velocity and angular velocity but also the estimation accuracy of each value. The first movement amount estimator 414 outputs the estimation accuracy as a variance-covariance matrix having dimensions equal to the number of items to be estimated. The first movement amount estimator 414 can calculate the estimation accuracy by, for example, determining the variance of the estimation result from the results of inference using multiple movement state models 451. Furthermore, for example, the first movement amount estimator 414 may output the average values ​​of the velocity, angular velocity, and tilt angle and the standard deviation as the estimation accuracy by using a movement state model 451 that has been trained to output the average value and standard deviation of a Gaussian distribution as the estimation result.

[0056] <<Method for determining accuracy of GNSS sensor 370>> Next, a method for determining accuracy of the GNSS sensor 370 according to the second embodiment will be described. Figure 6 is a flowchart showing a method for determining accuracy of the GNSS sensor 370 according to the second embodiment. The control device 400 repeatedly executes the flowchart shown in Figure 6 while the work vehicle 100 is operating.

[0057] The operation amount acquisition unit 411 acquires the operation amount and speed gear of the travel operation lever 145 (step S31). The measurement data acquisition unit 412 acquires measurement data from the rotation sensor 310, the dynamometer 320, the oil pressure sensor 330, the IMU 340, the blade stroke sensor 350, the ripper stroke sensor 360, and the GNSS sensor 370 (step S32). Next, the work implement height calculation unit 413 calculates the heights of the cutting edges of the blade 132 and the ripper 134 based on the measurement data of the stroke amount of the blade lift cylinder 133 and the stroke amount of the ripper lift cylinder 135 acquired by the measurement data acquisition unit 412 (step S33).

[0058] The first movement amount estimator 414 inputs the operation amount and speed gear of the travel operation lever 145 acquired in step S31, the rotation speeds of the left and right sprockets 122 acquired in step S32, the measurement data of the IMU 340, the pressure measurement data of the HST 230, and the measurement data of the dynamometer 320, as well as the height of the blade 132 and the height of the ripper 134 calculated in step S33, to the motion state model 451. As a result, the first movement amount estimator 414 estimates the speed in the X-axis direction, the speed in the Y-axis direction, the speed in the Z-axis direction, and the turning angular velocity of the work vehicle 100, and outputs a variance-covariance matrix indicating the reliability (step S34).

[0059] The determination unit 416 determines whether the reliability represented by the variance-covariance matrix is ​​equal to or greater than a predetermined threshold (step S35). For example, the determination unit 416 may regard the reciprocal of the average value of the variances, which are the diagonal components of the variance-covariance matrix, as the reliability.

[0060] If the reliability is greater than or equal to the threshold (step S35: YES), the second movement amount estimation unit 415 estimates the speed in the X-axis direction, the speed in the Y-axis direction, the speed in the Z-axis direction, and the turning angular velocity of the work vehicle 100 based on the measurement data of the GNSS sensor 370 acquired by the measurement data acquisition unit 412 (step S36).

[0061] The determination unit 416 calculates the differences between the X-axis direction velocity, Y-axis direction velocity, Z-axis direction velocity, and turning angular velocity estimated by the first movement amount estimation unit 414 and the second movement amount estimation unit 415 (step S37). The determination unit 416 determines whether each of the calculated differences is smaller than a predetermined threshold value (step S38). If all of the differences are smaller than the threshold value (step S38: YES), the determination unit 416 determines that the measurement data of the GNSS sensor 370 is accurate. The output unit 417 outputs the position and orientation indicated by the measurement data of the GNSS sensor 370 acquired in step S32 to the console 142 (step S39).

[0062] If at least one difference is equal to or greater than the threshold value (step S38: NO), the determination unit 416 determines that the measurement data of the GNSS sensor 370 is inaccurate. The output unit 417 outputs a warning indicating that the measurement data of the GNSS sensor 370 is inaccurate to the console 142 (step S40). The output unit 417 may output the position and orientation indicated by the measurement data of the GNSS sensor 370 acquired in step S2 to the console 142, or may output only a warning.

[0063] If the reliability is less than the threshold value (step S35: NO), the output unit 417 outputs a message indicating that it is not possible to determine the accuracy of the measurement data of the GNSS sensor 370 to the console 142 (step S41). The output unit 417 may output the position and orientation indicated by the measurement data of the GNSS sensor 370 acquired in step S2 to the console 142, or may output only the message.

[0064] <Third Embodiment> Figure 7 is a diagram showing the configuration of a measurement system and control device of a work vehicle 100 according to a third embodiment. The work vehicle 100 according to the third embodiment further includes, as a measurement system, an imaging device 380 that captures an image of a work target of the work vehicle 100. Examples of the imaging device 380 include a camera, LiDAR (Light Detection and Ranging), and a laser scanner. The measurement data of the imaging device 380 is a two-dimensional image or a range image.

[0065] The first movement amount estimator 414 according to the third embodiment inputs measurement data from the imaging device 380 into a motion state model 451 to estimate the speed, acceleration, and tilt angle of the work vehicle 100. The motion state model 451 according to the third embodiment receives input of the measurement data from the imaging device 380 in addition to the operation amount of the travel control lever 145, the rotation speeds of the left and right sprockets 122, measurement data from the IMU 340, measurement data of the pressure of the HST 230 (hydraulic motor), measurement data from the dynamometer 320, the speed gear, the height of the blade 132, and the height of the ripper 134. Instead of the measurement data itself from the imaging device 380, feature amount data extracted from the measurement data may be input to the motion state model 451. The feature amount of the measurement data from the imaging device 380 may be obtained, for example, from the intermediate layer of a convolutional autoencoder.

[0066] The measurement data of the imaging device 380 relates to slippage that occurs due to the reaction force that the work vehicle 100 receives from the work object. For example, the likelihood of the work vehicle 100 slipping varies depending on the work object and the properties of the ground. If the ground is muddy, the work vehicle 100 is more likely to slip. Therefore, the images and their feature quantities, which are the measurement data of the imaging device 380, may reflect the properties of the work object and the ground.

[0067] Furthermore, when the motion state model 451 is configured as a regression neural network, the measurement data of the image capture device 380 correlates with the movement amount and angular velocity of the work vehicle 100. As the work vehicle 100 travels, the scenery captured in the image, which is the measurement data of the image capture device 380, changes. The magnitude of the change in scenery over time differs depending on the movement amount and angular velocity of the work vehicle 100. Furthermore, when the image capture device 380 is a LiDAR, the measurement data indicates distance. The change in distance correlates with the movement amount of the work vehicle 100.

[0068] Therefore, the first movement amount estimating unit 414 according to the third embodiment can improve reliability by estimating the position and orientation of the work vehicle 100 using measurement data from the imaging device 380 .

[0069] Fourth Embodiment The work vehicle 100 according to the first embodiment determines the accuracy of the GNSS sensor 370 based on the difference in speed and acceleration. In contrast, the control device 400 according to the fourth embodiment estimates the position and orientation of the work vehicle 100 from the estimated amount of movement, and determines the accuracy of the GNSS sensor 370 based on the difference in position and orientation.

[0070] 8 is a diagram showing the configuration of a measurement system and a control device of a work vehicle 100 according to the fourth embodiment. The control device 400 according to the fourth embodiment includes a position estimator 418 instead of the second movement amount estimator 415 according to the first embodiment.

[0071] The position estimation unit 418 estimates the current position, orientation, and inclination angle of the work vehicle 100 based on the previous estimation results of the position, orientation, and inclination angle of the work vehicle 100, and the speed, angular velocity, and inclination angle of the work vehicle 100 estimated by the first movement amount estimation unit 414. The position estimation unit 418 may be realized by, for example, a Bayes filter. In other words, the position estimation unit 418 may be a Bayes filter that estimates the position, orientation, and inclination angle at time t using the estimation results of the position and inclination angle at time t-1 as a "state (posterior belief)," the speed and angular velocity of the work vehicle 100 at time t estimated by the first movement amount estimation unit 414 as a "control value," and the inclination angle of the work vehicle 100 at time t estimated by the first movement amount estimation unit 414 as an "observation value."

[0072] The determination unit 416 according to the fourth embodiment determines the accuracy of the GNSS sensor 370 based on the difference between the position and orientation indicated by the measurement data of the GNSS sensor 370 and the position and orientation estimated by the position estimation unit 418 .

[0073] Fifth Embodiment The control device 400 according to the above-described embodiments estimates the amount of movement of the work vehicle 100 and determines the accuracy of the GNSS sensor 370 based on the amount of movement. In contrast, the control device 400 according to the fifth embodiment determines the accuracy of the GNSS sensor 370 without estimating the amount of movement. Therefore, the control device 400 according to the fifth embodiment does not need to include the first movement amount estimator 414 and the second movement amount estimator 415.

[0074] In the storage 450 according to the fifth embodiment, a GNSS determination model is stored instead of the motion state model 451. The GNSS determination model receives the operation amount of the travel control lever 145, the rotation speeds of the left and right sprockets 122, measurement data from the IMU 340, measurement data of the pressure of the HST 230 (hydraulic motor), measurement data from the dynamometer 320, the speed gear, the height of the blade 132, the height of the ripper 134, and measurement values ​​from the GNSS sensor 370, and outputs a value indicating the accuracy of the GNSS sensor 370. The GNSS determination model is a machine learning model such as a neural network. The GNSS determination model may be configured as a CNN or an RNN. In particular, when the GNSS determination model is configured as a neural network that references past steps, such as a long short-term memory (LSTM) or a transformer, the accuracy of the GNSS sensor 370 can be appropriately estimated according to the slip state of the work vehicle 100, which changes over time. Transformer is a neural network that can refer to information from past steps using attention.

[0075] The GNSS determination model is trained using a training dataset consisting of a combination of the following input samples and output samples. The input samples are the operation amount of the travel control lever 145, the rotation speeds of the left and right sprockets 122, the measurement data of the IMU 340, the pressure of the HST 230, the power of the engine 210, the speed gear, the height of the blade 132, the height of the ripper 134, and the measurement value of the GNSS sensor 370. The output sample is a value indicating the accuracy of the GNSS sensor 370. The value indicating the accuracy of the GNSS sensor 370 may be a value calculated from the calculation results of the motion state model 451 according to the above-described embodiment and the measurement data of the GNSS sensor 370. For example, the ratio of the speed indicated by the calculation results of the motion state model 451 to the speed calculated from the measurement data of the GNSS sensor 370 can be set as the value indicating the accuracy of the GNSS sensor 370. Furthermore, the value indicating the accuracy of the GNSS sensor 370 may be a value calculated by comparing the position measured by the total station with the position indicated by the measurement data of the GNSS sensor 370 .

[0076] <Other Embodiments> Although one embodiment has been described in detail above with reference to the drawings, the specific configuration is not limited to that described above, and various design modifications and the like are possible. That is, in other embodiments, the order of the above-described processes may be changed as appropriate. Furthermore, some processes may be executed in parallel. The control device 400 according to the above-described embodiment may be configured by a single computer, or the configuration of the control device 400 may be divided into multiple computers, and the multiple computers may function as the control device 400 by cooperating with each other. In this case, some of the computers configuring the control device 400 may be mounted inside the work vehicle 100, and other computers may be provided outside the work vehicle 100.

[0077] Although the work vehicle 100 according to the above-described embodiment includes the HST 230 as the power transmission device, this is not limited thereto. For example, the power transmission device of the work vehicle 100 according to another embodiment may be a hydromechanical continuously variable transmission (HMT) (which performs gear shift control by combining the HST 230 and a planetary gear mechanism). Furthermore, the power transmission device of the work vehicle 100 according to another embodiment may be a clutch. Even when the power transmission device is an HMT or a clutch, the output of the engine 210 is related to the reaction force that the work vehicle 100 receives from the work object and the resulting slippage. This is because, when the engine 210 and the power transmission device are connected, the reaction force affects the engine 210. Therefore, by inputting the measurement data of the dynamometer 320 into the motion state model 451, the control device 400 can estimate the movement amount of the work vehicle 100 while suppressing the influence of the reaction force from the work object. Furthermore, for example, the power transmission device of the work vehicle 100 according to another embodiment may be an electric mechanical transmission (EMT) having a generator and an electric motor instead of a hydraulic pump and a hydraulic motor. In this case, the control device 400 may estimate the amount of movement of the work vehicle 100 by inputting measurement data of the electric motor current value into the motion state model 451. The measurement data of the electric motor current value is an example of a value related to the power of the traveling body.

[0078] Furthermore, the determination unit 416 of the control device 400 according to the embodiment described above determines the accuracy of the GNSS sensor 370 by comparing the speed and angular velocity of the work vehicle 100 calculated by the motion state model 451 with the speed and angular velocity calculated from the measurement data of the GNSS sensor 370, but is not limited to this. For example, the determination unit 416 according to other embodiments may determine the accuracy of the GNSS sensor 370 by comparing the position indicated by the measurement data of the GNSS sensor with the position of the work vehicle 100 calculated from the calculation results of the motion state model 451.

[0079] Furthermore, while the work vehicle 100 according to the embodiment described above is a bulldozer, the work vehicle 100 according to other embodiments is not limited to this. For example, the work vehicle 100 according to other embodiments may be a wheel loader, a motor grader, or the like. Furthermore, the work vehicle 100 according to other embodiments may be an electric vehicle equipped with a battery and an electric motor instead of the engine 210.

[0080] Furthermore, although the control device 400 according to the embodiment described above displays the estimated position, orientation, and tilt angle of the work vehicle 100 on the console 142, this is not limited to this. For example, the control device 400 according to other embodiments may perform autonomous control that controls the drivetrain based on the estimated position, orientation, and tilt angle of the work vehicle 100.

[0081] According to the present disclosure, as an example, the determination system can determine whether the GNSS positioning is accurate.

[0082] 100...Work vehicle 110...Vehicle body 120...Traveling device 121...Crawler 122...Sprocket 123...Travel motor 130...Work machine 131...Lift frame 132...Blade 133...Blade lift cylinder 134...Ripper 135...Ripper lift cylinder 140...Operator's cab 141...Seat 142...Console 143...Blade operation lever 144...Ripper operation lever 145...Travel operation lever 146...Brake pedal 147...Deceleration pedal 210...Engine 220...PTO 230...HST 250...Hydraulic pump 260...Proportional control valve 310...Rotation sensor 320...Dynamometer 330...Hydraulic pressure sensor 340...IMU 350...Blade stroke sensor 360...Ripper stroke sensor 370...GNSS sensor 380...Imaging device 400: Control device 410: Processor 411: Operation amount acquisition unit 412: Measurement data acquisition unit 413: Work implement height calculation unit 414: First movement amount estimation unit 415: Second movement amount estimation unit 416: Determination unit 417: Output unit 418: Position estimation unit 430: Main memory 450: Storage 451: Motion state model 470: Interface

Claims

1. A judgment system comprising: an acquisition unit that acquires position data indicating the position of a work vehicle based on GNSS (Global Navigation Satellite System); and a judgment unit that judges the accuracy of the position data based on the body data of the work vehicle and the position data.

2. A judgment system as described in claim 1, further comprising an estimation unit that estimates a value relating to the amount of movement of the work vehicle based on vehicle body data of the work vehicle, and the judgment unit judges the accuracy of the position data based on the value relating to the amount of movement and the position data.

3. The determination system according to claim 2, wherein the determination unit determines that the position data is accurate if the difference between the value relating to the amount of movement and the position data when converted into the same type of amount is smaller than a predetermined threshold value.

4. The judgment system described in claim 3, wherein the estimation unit further outputs a reliability of the value relating to the amount of movement of the work vehicle, and the judgment unit judges that the position data is accurate if the reliability is equal to or greater than a predetermined threshold and the difference between the value relating to the amount of movement and the position data when converted into the same type of quantity is smaller than a predetermined threshold.

5. The determination system described in claim 1, wherein the determination unit determines the accuracy of the position data by inputting the vehicle body data and the position data into a trained model trained to output the accuracy of the position data from the vehicle body data of the work vehicle and the position data measured based on the GNSS.

6. A determination system as claimed in any one of claims 2 to 4, wherein the estimation unit estimates the value relating to the amount of movement by inputting the vehicle body data into a trained model trained to output a value relating to the amount of movement from the vehicle body data.

7. A determination system according to any one of claims 1 to 5, wherein the vehicle body data includes a value related to the power of the running body of the work vehicle.

8. A determination system according to any one of claims 1 to 5, wherein the vehicle body data includes a value relating to a state of a work implement of the work vehicle.

9. A determination system according to any one of claims 1 to 5, wherein the vehicle body data further includes measurement data from an imaging device that images the outside of the work vehicle.

10. A method for determining the accuracy of the position data based on the vehicle body data and the position data of a work vehicle, the method comprising: acquiring position data indicating the position of the work vehicle based on the Global Navigation Satellite System (GNSS); and determining the accuracy of the position data based on the vehicle body data and the position data of the work vehicle.

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