Transport vehicles and transport vehicle systems

Abstract

The objective is to improve productivity at the work site by preventing unnecessary stops for autonomous transport vehicles, which avoid contact with obstacles along their travel path without deviating significantly from it. [Solution] The control device for the transport vehicle includes: a first area calculation unit that calculates a predicted obstacle area based on obstacle information; a second area calculation unit that calculates a predicted passage area for the transport vehicle based on road surface condition information and travel path, and corrects the predicted passage area so that it does not overlap with the predicted obstacle area but is in contact with the predicted obstacle area; a control target calculation unit that calculates a target path and control target values ​​for the transport vehicle so that the transport vehicle passes through the predicted passage area; and a travel control unit that controls the travel of the transport vehicle according to the control target values.

Description

Technical Field

[0001] The present invention relates to a transport vehicle and a transport vehicle system.

Background Art

[0002] At operating sites such as open-pit mines, it has been proposed to introduce an autonomous driving system that connects transport vehicles such as dump trucks that drive autonomously to a control station via a wireless communication network. This type of transport vehicle is often a driverless vehicle that drives autonomously without an operator on board. In order to increase the amount of earth and sand or minerals transported, which corresponds to the production volume of the mine, it is necessary to increase the number of round trips within the operating hours of the transport vehicles responsible for transportation. However, in mines, there are obstacles such as other mining machines, earth and sand or minerals spilled during transportation from other transport vehicles, or rocks. Therefore, the transport vehicle cannot travel along a pre-given travel route and may stop. Therefore, in order to improve the productivity of the operating site, it is important to equip the transport vehicle with a function to avoid obstacles.

[0003] As prior art, Patent Document 1 discloses a vehicle obstacle detection device comprising: object detection means for detecting an object in front of the vehicle and detecting the relative relationship between the detected object and the vehicle; path detection means for detecting the current path of the vehicle; area assumption means for assuming a driving area in which the vehicle is expected to travel when steered to the right and left by an amount of change in steering angle corresponding to the range of steering angles that can be taken from the current steering angle relative to the path detected by the path detection means, and assuming the area in which these driving areas overlap as a judgment area; determination means for determining the possibility of the vehicle coming into contact with the detected object detected by the object detection means; and relative speed detection means for detecting the relative speed between the detected object and the vehicle. The determination means determines the possibility of contact with the vehicle only when the detected object is in the judgment area if the relative speed detected by the relative speed detection means exceeds a preset threshold, and determines the possibility of contact with the vehicle when the detected object is in the driving area corresponding to the vehicle's path if the relative speed is below the threshold. [Prior art documents] [Patent Documents]

[0004] [Patent Document 1] Patent No. 3918656 [Overview of the Initiative] [Problems that the invention aims to solve]

[0005] The device disclosed in Patent Document 1 determines the possibility of contact based on the area that cannot be avoided even when the steering angle is manipulated. However, it does not take into account the drivable area occupied by low-lying soil, etc., and therefore determines that there is a possibility of contact even with obstacles that can be driven over, so there is room for improvement.

[0006] The present invention has been made in view of the above, and aims to improve productivity at the work site by preventing unnecessary stopping of the transport vehicle by enabling the autonomous transport vehicle to avoid contact with obstacles on the travel path without deviating significantly from the travel path. [Means for solving the problem]

[0007] To solve the above problems, the present invention provides a transport vehicle that autonomously travels according to a travel path set on a transport route, comprising: an external sensor that measures objects around the transport vehicle; an internal sensor that measures the vehicle state when the transport vehicle is traveling; an obstacle detection device that detects obstacles present on the travel path based on the measurement results of the external sensor and acquires obstacle information indicating the position and shape of the detected obstacles; a road surface condition estimation device that estimates the road surface condition of the travel path based on the measurement results of the internal sensor and acquires road surface condition information indicating the estimated road surface condition; and a control device that controls the travel of the transport vehicle based on the obstacle information and the road surface condition information, wherein the control device controls the movement of the transport vehicle on the travel path based on the obstacle information. The system is characterized by comprising: a first area calculation unit that calculates a predicted obstacle area indicating an area where obstacles are expected to exist that would obstruct the passage of the transport vehicle; a second area calculation unit that calculates a predicted passage area indicating an area where the transport vehicle is expected to pass if it travels near the obstacles along the travel path, based on the road surface condition information and the travel path, and which does not overlap with the predicted obstacle area and is in contact with the predicted obstacle area in the width direction of the transport path; a control target calculation unit that calculates a target path for the transport vehicle and control target values ​​including a target speed and target steering angle for the transport vehicle, so that the transport vehicle passes through the predicted passage area; and a travel control unit that controls the travel of the transport vehicle according to the control target values. [Effects of the Invention]

[0008] According to the present invention, an autonomous transport vehicle can avoid contact with obstacles on its travel path without deviating significantly from the path, thereby preventing unnecessary stopping of the transport vehicle and improving productivity at the work site. Other issues, configurations, and effects will be clarified by the following description of the embodiments. [Brief explanation of the drawing]

[0009] [Figure 1] A diagram showing an example of a site where a transport vehicle of the first embodiment is in operation. [Figure 2] A diagram showing how transport vehicles travel along a transport route. [Figure 3] Functional block diagram of a transport vehicle. [Figure 4] A table showing examples of map information. [Figure 5] A table showing an example of obstacle information. [Figure 6] A table showing an example of road surface condition information. [Figure 7] A diagram showing examples of obstacles present in the transport path. [Figure 8] A diagram illustrating areas that cannot be crossed. [Figure 9] A diagram illustrating the predicted obstacle area. [Figure 10] A diagram illustrating the relationship between the distance to an obstacle and measurement error. [Figure 11] Another diagram illustrating the relationship between distance to an obstacle and measurement error. [Figure 12] A diagram illustrating the expected region of passage. [Figure 13] A diagram illustrating the correction of the predicted transit region. [Figure 14] A flowchart illustrating the obstacle avoidance process performed by transport vehicles. [Figure 15] A diagram showing an example of a site where a transport vehicle of the second embodiment is in operation. [Figure 16] Functional block diagram of a transport vehicle system. [Figure 17] A table showing an example of obstacle information for a transport vehicle system. [Figure 18]Table showing an example of road surface condition information of a transport vehicle system.

Embodiments for Carrying out the Invention

[0010] Hereinafter, embodiments of the present invention will be described with reference to the drawings and the like. The following description shows specific examples of the content of the present invention, and the present invention is not limited to these descriptions, and various changes and modifications can be made by those skilled in the art within the scope of the technical idea disclosed in this specification. Also, in all the drawings for explaining the present invention, those having the same function may be given the same reference numerals, and the repeated description thereof may be omitted.

[0011] [First Embodiment] The first embodiment of the present invention will be described with reference to FIGS. 1 to 14. FIG. 1 is a diagram showing an example of an operation site MS of a transport vehicle 20 according to the first embodiment.

[0012] At an operation site MS such as an open-pit mine, one or more transport vehicles 20 for transporting loads such as earth and sand or ore are running. In the present embodiment, the transport vehicle 20 is an unmanned vehicle that can run autonomously under external control. Also, in the present embodiment, the transport vehicle 20 includes a frame serving as a vehicle body, wheels attached to the frame, and a loading platform (bessel). The transport vehicle 20 runs by rotating the wheels, and is configured as a dump truck in which a load is loaded on the loading platform and the load is discharged by rotating and undulating (dumping operation) the loading platform. The transport vehicle 20 runs in a transport road 10 designed according to the shape of the operation site MS. In FIG. 1, each transport vehicle 20 running autonomously is denoted by reference numerals 20-1, 2-2, ···.

[0013] FIG. 2 is a diagram showing the state of the transport vehicle 20 running on the transport road 10.

[0014] The travel path 11 is data indicating a curve within the transport path 10. The node 12 is data indicating coordinates on the travel path 11. The transport vehicle 20 is a transport vehicle that autonomously travels according to the travel path 11 set on the transport path 10. The transport vehicle 20 may autonomously travel in a manner that minimizes the deviation (following error) from the travel path 11, or it may autonomously travel in a manner that passes over the nodes 12. In this embodiment, the travel path 11 is given as a curve passing through the center of the transport path 10, and the nodes 12 are given as being located at equal intervals on the travel path 11.

[0015] Figure 3 is a functional block diagram of the transport vehicle 20. Figure 4 is a table showing an example of map information. Figure 5 is a table showing an example of obstacle information. Figure 6 is a table showing an example of road surface condition information.

[0016] The transport vehicle 20 includes, as a hardware configuration, a memory device 2000, a three-dimensional distance sensor 2010, a load sensor 2020, a position sensor 2030, a compass sensor 2040, a speed sensor 2050, a steering angle sensor 2060, an obstacle detection device 2070, a road surface condition estimation device 2080, a control device 2090, and a drive device 2100.

[0017] The storage device 2000 is a non-volatile storage medium capable of reading and writing information. The storage device 2000 stores the OS (Operating System), various control programs, application programs, and databases. The storage device 2000 includes a map information storage unit 2001, an obstacle information storage unit 2002, and a road surface condition information storage unit 2003.

[0018] The map information storage unit 2001 stores the map information table shown in Figure 4. Each piece of data stored in the map information table is associated with the node ID of node 12. Each piece of data stored in the map information table includes at least the node position, which is the coordinate of node 12; the speed limit of the transport vehicle 20, which is based on either or both the curvature of the travel path 11 and the gradient of the transport road 10; the gradient of the travel path 11; and the drivable area. The drivable area indicates the area in the width direction of the transport road 10 that the transport vehicle 20 can travel. In this embodiment, the drivable area is expressed as the distance from node 12 to the left and right boundaries in the width direction of the transport road 10. The width direction of the transport road 10 is perpendicular to the travel path 11.

[0019] The obstacle information storage unit 2002 stores the obstacle information table shown in Figure 5. Each data stored in the obstacle information table is associated with an obstacle ID. Each data stored in the obstacle information table includes at least the center position of the obstacle and the shape information of the obstacle. The shape information of the obstacle stored in the obstacle information table includes at least the path width direction length, which indicates the size in the width direction of the transport path 10, the path direction length, which indicates the size in the path direction along the travel path 11, the height distribution of the obstacle in the area enclosed by the path width direction length and the path direction length, and the distribution of the height gradient of the obstacle in that area. In other words, the obstacle information indicates the position and shape of the obstacle in the travel path 11.

[0020] The road surface condition information storage unit 2003 stores the road surface condition information table shown in Figure 6. Each data item stored in the road surface condition information table is associated with the node ID of node 12. Each data item stored in the road surface condition information table includes at least one indicator (for example, at least one of the road surface friction coefficient, road surface smoothness, and road surface moisture content) that indicates the road surface condition corresponding to the coordinates of node 12.

[0021] The 3D distance sensor 2010 is a sensor that measures objects around the transport vehicle 20. The 3D distance sensor 2010 measures the distance to objects around the transport vehicle 20 and relatively measures the 3D position of the object relative to the transport vehicle 20. For example, the 3D distance sensor 2010 may be configured as LiDAR (Light Detection And Ranging) and measure the 3D position of the object using laser light. The 3D distance sensor 2010 may be configured as a camera and estimate the 3D position of the object from the captured image. In this embodiment, the 3D distance sensor 2010 will be described as being configured as LiDAR.

[0022] The load sensor 2020 is a sensor that measures the load capacity of the transport vehicle 20, which in this embodiment is the weight of the cargo loaded on the cargo bed. For example, the load sensor 2020 measures the load capacity of the transport vehicle 20 by measuring the load acting on the suspension provided between the frame and wheels of the transport vehicle 20, or the pressure of the hydraulic fluid in the hydraulic cylinder.

[0023] The position sensor 2030 is a sensor that measures the position (vehicle position) of the transport vehicle 20. For example, the position sensor 2030 may be a GNSS (Global Navigation Satellite System) receiver such as a GPS (Global Positioning System).

[0024] The orientation sensor 2040 is a sensor that measures the orientation (vehicle orientation) of the transport vehicle 20. For example, the orientation sensor 2040 may calculate the orientation from the time change of angular acceleration measured by an inertial measurement unit (IMU). Alternatively, the orientation sensor 2040 may calculate the orientation of the transport vehicle 20 from the position trajectory acquired by a GNSS receiver.

[0025] The speed sensor 2050 is a sensor that measures the travel speed (vehicle speed) of the transport vehicle 20. For example, the speed sensor 2050 may be a wheel speed sensor that detects the rotation speed of the wheels, or the travel speed of the transport vehicle 20 may be calculated from the time change of position acquired by the GNSS receiver.

[0026] The steering angle sensor 2060 is a sensor that measures the steering angle of the transport vehicle 20. For example, the steering angle sensor 2060 may be an angle detection device that detects the steering angle of the wheels (front wheels).

[0027] The 3D distance sensor 2010 is an external sensor that measures objects around the transport vehicle 20. The load sensor 2020, position sensor 2030, orientation sensor 2040, speed sensor 2050, and steering angle sensor 2060 are internal sensors that measure the state of the transport vehicle 20 while it is in motion (vehicle state).

[0028] The obstacle detection device 2070 includes a CPU (Central Processing Unit), RAM (Random Access Memory), and ROM (Read Only Memory), which perform program calculations, read and write information to the work area, and temporarily store programs to detect obstacles.

[0029] The obstacle detection device 2070 detects obstacles present in the travel path 11 based on the measurement results of the external sensor, the 3D distance sensor 2010, and acquires obstacle information indicating the position and shape of the detected obstacles. Specifically, the obstacle detection device 2070 estimates the road surface plane based on the distance from the transport vehicle 20 to the road surface measured by the 3D distance sensor 2010. Then, based on the estimated road surface plane and the measurement results of the 3D distance sensor 2010, the obstacle detection device 2070 detects objects on the road surface within the measurement range. The obstacle detection device 2070 then detects objects present in the transport path 10 as obstacles. The obstacle detection device 2070 then acquires obstacle information by calculating the center position of the obstacle, the path width direction length, the path direction length, the height distribution, and the height gradient distribution of the obstacle based on the position and direction of the transport vehicle 20 measured by the position sensor 2030 and the orientation sensor 2040, and the distance from the transport vehicle 20 to the obstacle measured by the 3D distance sensor 2010. The obstacle detection device 2070 stores the acquired obstacle information in the obstacle information storage unit 2002.

[0030] The road surface condition estimation device 2080 includes a CPU, RAM, and ROM, which perform program calculations, read and write information to the work area, temporarily store the program, and estimate the road surface condition.

[0031] The road surface condition estimation device 2080 estimates the road surface condition of the travel path 11 based on the measurement results of internal sensors 2020 to 2060, and acquires road surface condition information indicating the estimated road surface condition. Specifically, the road surface condition estimation device 2080 calculates the slip ratio of the transport vehicle 20 based on the wheel speed of the transport vehicle 20 measured by the speed sensor 2050 and the change in the position of the transport vehicle 20 over time measured by the position sensor 2030. For example, the road surface condition estimation device 2080 may calculate the ground speed of the transport vehicle 20 from the change in the position of the transport vehicle 20 over time, and calculate the slip ratio as the ratio of the calculated ground speed to the aforementioned wheel speed. Then, the road surface condition estimation device 2080 estimates the road surface condition by calculating the road friction coefficient, which indicates the degree of friction between the tires of the transport vehicle 20 and the road surface, from the calculated slip ratio. For example, the road surface condition estimation device 2080 may store a correspondence table between slip ratio and road surface friction coefficient in advance, and obtain the road surface friction coefficient corresponding to the calculated slip ratio from the said correspondence table.

[0032] Alternatively, the road surface condition estimation device 2080 calculates the slip angle of the transport vehicle 20 based on the orientation of the transport vehicle 20 measured by the orientation sensor 2040, the steering angle of the transport vehicle 20 measured by the steering angle sensor 2060, and the time change in the position of the transport vehicle 20 measured by the position sensor 2030. For example, the road surface condition estimation device 2080 may calculate the orientation of the tires from the orientation and steering angle of the transport vehicle 20, calculate the velocity direction of the transport vehicle 20 from the orientation of the transport vehicle 20 and the time change in the position of the transport vehicle 20, and calculate the slip angle as the difference between the orientation of the tires and the velocity direction. The road surface condition estimation device 2080 then estimates the road surface condition by calculating the road surface friction coefficient from the calculated slip angle. For example, the road surface condition estimation device 2080 may store a correspondence table between the slip angle and the road surface friction coefficient in advance and obtain the road surface friction coefficient corresponding to the calculated slip angle from the correspondence table.

[0033] Furthermore, the road surface condition estimation device 2080 may estimate the road surface plane based on the distance from the transport vehicle 20 to the road surface measured by the 3D distance sensor 2010. The road surface condition estimation device 2080 may then estimate the road surface condition by calculating the variance with respect to the estimated road surface plane as the road surface smoothness. When the road surface smoothness is low, that is, when the road surface condition is close to that of a gravel road, it becomes difficult for the tires to transmit force to the ground. In this case, for the transport vehicle 20, the ground speed (time change of position) becomes small relative to the wheel speed, and the turning radius becomes large relative to the steering angle.

[0034] The control device 2090 includes a CPU, RAM, and ROM, which perform calculations for the program, read and write information to the work area, and temporarily store the program, thereby controlling the autonomous driving of the transport vehicle 20 so that the transport vehicle 20 moves according to the map information.

[0035] The control device 2090 generates control commands to control the autonomous driving of the transport vehicle 20 based on obstacle information, road surface condition information, and map information stored in the storage device 2000. These control commands control the operation of each component of the drive unit 2100 so as to correspond to at least the brake pedal operation amount, the accelerator pedal operation amount, and the steering angle operation amount. As a function for generating these control commands, the control device 2090 has at least a first domain calculation unit 2091, a second domain calculation unit 2092, a control target calculation unit 2094, and a driving control unit 2095.

[0036] The first area calculation unit 2091 calculates an expected obstacle area based on obstacle information, indicating an area on the travel path 11 where obstacles are expected to exist that could obstruct the passage of the transport vehicle 20. The second area calculation unit 2092 calculates an expected passage area based on road surface condition information and the travel path 11, indicating an area that the transport vehicle 20 is expected to pass through if it travels along the travel path 11 near obstacles.

[0037] The control target calculation unit 2094 calculates the target path of the transport vehicle 20, and control target values ​​including the target speed and target steering angle of the transport vehicle 20, so that the transport vehicle 20 passes through the predicted passage area calculated by the second area calculation unit 2092. The driving control unit 2095 controls the autonomous driving of the transport vehicle 20 according to the control target values ​​calculated by the control target calculation unit 2094. Specifically, the driving control unit 2095 generates the aforementioned control command according to the control target values ​​calculated by the control target calculation unit 2094. The driving control unit 2095 outputs the generated control command to the drive unit 2100 and operates the drive unit 2100 according to the control command.

[0038] Details of the first domain calculation unit 2091, the second domain calculation unit 2092, and the control target calculation unit 2094 will be described later with reference to Figures 7 to 13.

[0039] The drive unit 2100 is a device that drives the transport vehicle 20 in order to move the transport vehicle 20. The drive unit 2100 includes at least a braking device for braking the transport vehicle 20, a steering motor for changing the steering angle of the transport vehicle 20, and an electric motor for accelerating the transport vehicle 20. The braking device for braking the transport vehicle 20 includes at least an electric brake that reduces the wheel speed by the regenerative action of the electric motor that brakes the transport vehicle 20, and a mechanical brake that reduces the wheel speed by friction. The drive unit 2100 operates in response to control commands generated by the control device 2090.

[0040] Figures 7 to 13 will be used to explain the details of the first region calculation unit 2091, the second region calculation unit 2092, and the control target calculation unit 2094. Figure 7 is a diagram showing an example of an obstacle present in the transport path 10. Figure 8 is a diagram illustrating the area that cannot be crossed. Figure 9 is a diagram illustrating the predicted obstacle area. Figure 10 is a diagram illustrating the relationship between the distance to the obstacle and the measurement error. Figure 11 is another diagram illustrating the relationship between the distance to the obstacle and the measurement error. Figure 12 is a diagram illustrating the predicted passage area. Figure 13 is a diagram illustrating the correction of the predicted passage area.

[0041] The first area calculation unit 2091 calculates an area where the transport vehicle 20 cannot pass over an obstacle, based on the height distribution and height gradient distribution of the obstacles included in the obstacle information, from among the areas where obstacles may exist as calculated from the obstacle information. When the transport vehicle 20 passes over an obstacle, some of its tires will ride up and the body of the transport vehicle 20 will tilt, so there is a limit to the height of the obstacle that the transport vehicle 20 can pass over, based on the allowable tilt angle of the transport vehicle 20's body. In addition, there is a limit to the height gradient of the obstacle that the transport vehicle 20 can pass over, based on the tire size and the tire torque that can be generated by the transport vehicle 20.

[0042] In the example of an obstacle present in the transport path 10 shown in Figure 7, the first area calculation unit 2091 calculates, as shown in Figure 8, at least one of the areas where an obstacle may exist (the area of ​​the dashed line in Figure 8) calculated from the obstacle information is an area where the height limit of the obstacle that the transport vehicle 20 can overcome is exceeded (the area of ​​the dotted line in Figure 8), and an area where the height gradient limit of the obstacle that the transport vehicle 20 can overcome is exceeded (the area of ​​the dashed line in Figure 8), as an area that cannot be overcome (the area of ​​the gray-filled area in Figure 8).

[0043] The first region calculation unit 2091 then calculates the predicted obstacle region by adding a margin based on the measurement error of the external sensor, the 3D distance sensor 2010, to the calculated inaccessible region. The greater the distance from the 3D distance sensor 2010 to the obstacle, the greater the measurement error of the 3D distance sensor 2010. Therefore, as shown in Figure 9, the first region calculation unit 2091 calculates the predicted obstacle region by adding the error in the inaccessible region that may result from this measurement error as a margin to the inaccessible region.

[0044] As shown in Figure 10, if the 3D distance sensor 2010 is a LiDAR, the distance from the sensor position to the position where the laser beam hits the obstacle can be measured. In this case, the number of laser beams that hit the obstacle differs depending on the spacing between the laser beams. The closer the obstacle is to the sensor position, the narrower the spacing between the laser beams becomes, and the more laser beams that hit the obstacle increase. Similarly, as shown in Figure 11, when the obstacle is close to the sensor position, the number of laser beams that hit the obstacle increases compared to when it is farther away, and the spacing between the laser beam irradiation positions on the obstacle also becomes narrower. For these reasons, the greater the distance from the 3D distance sensor 2010 to the obstacle, the greater the measurement error due to the spacing between the laser beams. Therefore, the first region calculation unit 2091 sets a larger margin as the distance from the 3D distance sensor 2010 to the obstacle increases. For example, the first region calculation unit 2091 sets a margin that is proportional to the distance from the 3D distance sensor 2010 to the obstacle. The first area calculation unit 2091 then calculates the area obtained by adding the set margin to the area that cannot be crossed as the predicted obstacle area.

[0045] The second area calculation unit 2092 calculates a predicted passage area, which indicates the area that the transport vehicle 20 is expected to pass through when it travels along the travel path 11 near an obstacle. Here, if the road surface condition is poor, for example, if the road surface friction coefficient is small, the force that the tires can generate decreases, so the tires become more prone to slipping, and the error in the transport vehicle 20 following the travel path 11 increases. Therefore, the second area calculation unit 2092 obtains a following error according to the road surface condition and calculates the length of the predicted passage area in the path width direction by adding the obtained following error to the width of the transport vehicle 20. Then, the second area calculation unit 2092 calculates a predicted passage area having the calculated length in the path width direction. For example, the second area calculation unit 2092 calculates a predicted passage area having the calculated length in the path width direction by setting the center position of the predicted passage area to a position on the travel path 11 and the length of the predicted passage area in the path direction to the total length of the transport vehicle 20.

[0046] The tracking error corresponding to the road surface condition may be estimated in advance by simulation based on the road surface condition and the driving path 11. The second domain calculation unit 2092 may store a correspondence table between the road surface condition and the tracking error in advance, and obtain the tracking error corresponding to the road surface condition estimated by the road surface condition estimation device 2080 from the correspondence table.

[0047] Furthermore, the second domain calculation unit 2092 may refer to driving records that include tracking errors when the vehicle previously traveled along the driving path 11, narrow down the selection to driving records where the road surface conditions were similar to the current road surface conditions, and obtain the tracking errors included in the selected driving records. Here, the faster the transport vehicle 20 is going, the longer the distance traveled while the force generated by the tires is insufficient, and the larger the tracking error. Therefore, the second domain calculation unit 2092 may correct the tracking error obtained from past driving records so that it becomes a tracking error that is proportional to the speed of the transport vehicle 20. Also, the smaller the radius of curvature of the driving path 11, the greater the force required to turn, which increases the deficiency of the force generated by the tires and the larger the tracking error. Therefore, the second domain calculation unit 2092 may correct the tracking error obtained from past driving records so that it becomes a tracking error that is inversely proportional to the radius of curvature of the driving path 11.

[0048] Figure 12 shows an example of the calculated predicted obstacle area and predicted passage area. In the example in Figure 12, there is an overlapping area between the predicted obstacle area and the predicted passage area. In this case, the second area calculation unit 2092 uses the area correction unit 2093 to correct the predicted passage area calculated by the second area calculation unit 2092 so that the predicted passage area does not overlap with the predicted obstacle area and is in contact with the predicted obstacle area in the width direction of the transport path 10, as shown in Figure 13. The area correction unit 2093 calculates the length of the corrected predicted passage area in the width direction from the length of the predicted obstacle area in the path width direction and the length of the transport path 10 that the transport vehicle 20 can travel on (i.e., the drivable area of ​​the map information) so that the corrected predicted passage area is in contact with the predicted obstacle area. At this time, it is preferable that the area correction unit 2093 calculates the length of the corrected predicted passage area in the width direction so that the corrected predicted passage area does not deviate from the transport path 10. If there is no overlapping area between the predicted obstacle area and the predicted passage area, the second area calculation unit 2092 does not need to correct the predicted passage area. In this embodiment, it is assumed that there is an overlapping area between the predicted obstacle area and the predicted passage area.

[0049] The control target calculation unit 2094 calculates the target path, target speed, and target steering angle of the transport vehicle 20 based on the predicted passage area calculated by the second area calculation unit 2092. Since the speed of the transport vehicle 20 when traveling near an obstacle is related to the length in the path width direction of the predicted passage area, the control target calculation unit 2094 calculates the speed corresponding to the length in the path width direction of the corrected predicted passage area as the target speed of the transport vehicle 20. The control target calculation unit 2094 calculates the path connecting the path passing through the center of the corrected predicted passage area and the current position of the transport vehicle 20 as the target path. The control target calculation unit 2094 may connect the path passing through the center of the corrected predicted passage area and the current position of the transport vehicle 20 with a straight line, or it may connect them with a clothoid curve in which the steering angle changes continuously. The control target calculation unit 2094 calculates the target steering angle from the calculated target path. The control target calculation unit 2094 may calculate the target steering angle by forward gaze control, or it may calculate the target steering angle from the force generated by the tires calculated based on the change in direction when passing along the target path.

[0050] Furthermore, each time the transport vehicle 20 moves, the control device 2090 calculates the predicted obstacle area and predicted passage area based on newly acquired obstacle information and road surface condition information, calculates the target path and control target value, generates a control command, and outputs it to the drive device 2100. Specifically, the first area calculation unit 2091 repeatedly calculates the predicted obstacle area during the period when the transport vehicle 20 is approaching the obstacle. The second area calculation unit 2092 repeatedly calculates the predicted passage area during the period when the transport vehicle 20 is approaching the obstacle. The control target calculation unit 2094 repeatedly calculates the target path and control target value during the period when the transport vehicle 20 is approaching the obstacle. The driving control unit 2095 repeatedly controls the driving of the transport vehicle 20 during the period when the transport vehicle 20 is approaching the obstacle.

[0051] The obstacle avoidance process performed by the transport vehicle 20 will be explained using Figure 14. Figure 14 is a flowchart showing the obstacle avoidance process performed by the transport vehicle 20.

[0052] In step S100, the obstacle detection device 2070 acquires obstacle information based on the measurement results of the 3D distance sensor 2010, the position sensor 2030, and the orientation sensor 2040, and stores it in the obstacle information storage unit 2002.

[0053] In step S101, the road surface condition estimation device 2080 acquires road surface condition information based on the measurement results of the 3D distance sensor 2010, position sensor 2030, orientation sensor 2040, speed sensor 2050, and steering angle sensor 2060, and stores it in the road surface condition information storage unit 2003.

[0054] In step S102, the first area calculation unit 2091 of the control device 2090 calculates the predicted obstacle area based on the measurement results of the 3D distance sensor 2010, the position sensor 2030, and the orientation sensor 2040, and the obstacle information stored in the obstacle information storage unit 2002.

[0055] In step S103, the second area calculation unit 2092 of the control device 2090 calculates the predicted traversal area based on the map information stored in the map information storage unit 2001 and the road surface condition information stored in the road surface condition information storage unit 2003.

[0056] In step S104, the second area calculation unit 2092 of the control device 2090 corrects the expected passage area using the area correction unit 2093 included in the second area calculation unit 2092. The area correction unit 2093 corrects the expected passage area based on the map information stored in the map information storage unit 2001, the expected obstacle area calculated by the first area calculation unit 2091, and the expected passage area calculated by the second area calculation unit 2092.

[0057] In step S105, the control target calculation unit 2094 of the control device 2090 calculates the target route and control target value of the transport vehicle 20 based on the measurement results of the position sensor 2030, the orientation sensor 2040, and the speed sensor 2050, the map information stored in the map information storage unit 2001, and the predicted passage area corrected by the area correction unit 2093.

[0058] In step S106, the driving control unit 2095 of the control device 2090 generates a control command for the drive unit 2100 based on the measurement results of the speed sensor 2050 and the steering angle sensor 2060, and the control target value calculated by the control target calculation unit 2094.

[0059] In step S107, the drive unit 2100 operates according to the control commands generated by the travel control unit 2095 to drive the transport vehicle 20. After that, the transport vehicle 20 completes the process shown in Figure 14.

[0060] In this embodiment, the case in which the second area calculation unit 2092 has an area correction unit 2093 has been described. However, in cases where the expected obstacle area and the expected passage area do not overlap, the calculation result of the second area calculation unit 2092 does not need to be corrected by the area correction unit 2093. For example, if the expected obstacle area and the expected passage area do not overlap, the second area calculation unit 2092 calculates the expected passage area so that the expected passage area touches the expected obstacle area, and the control target calculation unit 2094 calculates the control target value etc. according to the calculation result of the second area calculation unit 2092. Also, for example, if the target path does not need to be corrected (for example, if obstacles can be sufficiently avoided on the path at the start of travel in that section), the area calculation and correction of the control device 2090 does not need to be repeated.

[0061] In other words, the second area calculation unit 2092 is configured to calculate a predicted passage area that indicates the area that the transport vehicle 20 is expected to pass through when the transport vehicle 20 travels near an obstacle along the said travel path, based on road surface condition information and travel path, and that does not overlap with the predicted obstacle area calculated by the first area calculation unit 2091 and is in contact with the predicted obstacle area in the width direction of the transport path.

[0062] As described above, the transport vehicle 20 of the first embodiment is a transport vehicle that autonomously travels according to a travel path 11 set on the transport path 10. The transport vehicle 20 includes an external sensor (3D distance sensor 2010) that measures objects around the transport vehicle 20, internal sensors (sensors 2020 to 2060) that measure the state of the transport vehicle 20 while it is traveling, an obstacle detection device 2070 that detects obstacles present on the travel path 11 based on the measurement results of the external sensor and acquires obstacle information indicating the position and shape of the detected obstacles, a road surface condition estimation device 2080 that estimates the road surface condition of the travel path 11 based on the measurement results of the internal sensor and acquires road surface condition information indicating the estimated road surface condition, and a control device 2090 that controls the travel of the transport vehicle 20 based on the obstacle information and road surface condition information. The control device 2090 includes: a first area calculation unit 2091 that calculates a predicted obstacle area based on obstacle information, indicating an area on the travel path 11 where obstacles are expected to exist that would obstruct the passage of the transport vehicle 20; a second area calculation unit 2092 that calculates a predicted passage area based on road surface condition information and the travel path 11, indicating an area that the transport vehicle 20 is expected to pass through when it travels near obstacles along the travel path 11, and which does not overlap with the predicted obstacle area and is in contact with the predicted obstacle area in the width direction of the transport path 10; a control target calculation unit 2094 that calculates a target path for the transport vehicle 20 and control target values ​​including a target speed and target steering angle for the transport vehicle 20 so that the transport vehicle 20 passes through the predicted passage area; and a travel control unit 2095 that controls the travel of the transport vehicle 20 according to the control target values.

[0063] As a result, the transport vehicle 20 can pass over areas of obstacles that it can overcome, such as low-height sections, and can travel along a route that avoids areas that it cannot overcome. Therefore, the transport vehicle 20 can avoid unnecessary stopping by judging that there is a possibility of contact even with obstacles that it could normally overcome. Moreover, since the transport vehicle 20 calculates the expected passage area so as to be in contact with the expected obstacle area, it can obtain the maximum expected passage area in the width direction within the drivable area of ​​the transport path 10. Therefore, the transport vehicle 20 can avoid obstacles without deviating significantly from the travel path 11, and can maximize the target speed of the transport vehicle 20 when avoiding obstacles by preventing unnecessary deceleration and lateral movement. As a result, the transport vehicle 20 can shorten its transport time and improve the productivity of the operational site MS. Therefore, according to this embodiment, the autonomous transport vehicle 20 can avoid contact with obstacles on the travel path 11 without deviating significantly from the travel path 11, thereby preventing unnecessary stopping of the transport vehicle 20 and improving the productivity of the operational site MS.

[0064] Furthermore, in the transport vehicle 20 of the first embodiment, the second area calculation unit 2092 calculates the expected passage area based on road surface condition information and the travel path 11, and corrects the expected passage area if the calculated expected passage area overlaps with the expected obstacle area, so that the calculated expected passage area does not overlap with the expected obstacle area and is in contact with the expected obstacle area in the width direction of the transport path 10.

[0065] As a result, the transport vehicle 20 can reliably avoid overlapping between the expected obstacle area and the expected passage area, thereby reliably obtaining the maximum expected passage area in the width direction within the drivable area of ​​the transport path 10. Therefore, the transport vehicle 20 can reliably avoid obstacles without deviating significantly from the travel path 11, and can reliably prevent unnecessary deceleration and lateral movement, thereby maximizing the target speed of the transport vehicle 20 when avoiding obstacles. Thus, according to this embodiment, by reliably avoiding contact with obstacles on the travel path 11 without deviating significantly from the travel path 11 while autonomously traveling, the transport vehicle 20 can reliably prevent unnecessary stopping and reliably improve the productivity of the operational site MS.

[0066] Furthermore, in the transport vehicle 20 of the first embodiment, the first area calculation unit 2091 calculates an area where the transport vehicle 20 cannot pass over an obstacle from among the areas where obstacles may exist calculated based on obstacle information, and calculates a predicted obstacle area by adding a margin based on the measurement error of the external sensor to the calculated area where obstacles cannot pass.

[0067] This prevents the transport vehicle 20 from misdetecting obstacles based on measurement errors of external sensors and unintentionally coming into contact with them. Therefore, the transport vehicle 20 can reliably avoid obstacles and reliably prevent unnecessary deceleration and lateral movement, thereby reliably maximizing the target speed. As a result, the transport vehicle 20 can reliably shorten its transport time and reliably improve the productivity of the operational site MS. Thus, according to this embodiment, the autonomous transport vehicle 20 can reliably avoid contact with obstacles on the travel path 11 without deviating significantly from the travel path 11, thereby reliably preventing unnecessary stops of the transport vehicle 20 and reliably improving the productivity of the operational site MS.

[0068] Furthermore, in the transport vehicle 20 of the first embodiment, the obstacle information includes at least one of the distribution of obstacle heights and the distribution of obstacle height gradients. The first area calculation unit 2091 calculates at least one of the areas where obstacles may exist, calculated based on the obstacle information, that exceeds the limit value of the height of obstacles that the transport vehicle 20 can overcome, and that exceeds the limit value of the height gradient of obstacles that the transport vehicle 20 can overcome, as areas that cannot be overcome.

[0069] As a result, the transport vehicle 20 can calculate the area where overcoming obstacles is impossible by considering not only the height of the obstacle but also the gradient of its height. Therefore, the transport vehicle 20 can more reliably avoid obstacles and more reliably prevent unnecessary deceleration and lateral movement, thereby more reliably maximizing the target speed. Consequently, the transport vehicle 20 can more reliably shorten its transport time and more reliably improve the productivity of the operational site MS. Thus, according to this embodiment, the autonomous transport vehicle 20 can more reliably avoid contact with obstacles on the travel path 11 without deviating significantly from the travel path 11, thereby more reliably preventing unnecessary stops of the transport vehicle 20 and more reliably improving the productivity of the operational site MS.

[0070] Furthermore, in the transport vehicle 20 of the first embodiment, the first area calculation unit 2091 calculates the predicted obstacle area by setting a larger margin as the distance from the external sensor to the obstacle increases.

[0071] This makes it possible to more reliably prevent the transport vehicle 20 from unintentionally coming into contact with obstacles due to false detection of obstacles based on measurement errors of external sensors. Therefore, the transport vehicle 20 can more reliably avoid obstacles and more reliably prevent unnecessary deceleration and lateral movement, thereby more reliably maximizing the target speed. As a result, the transport vehicle 20 can more reliably shorten its transport time and more reliably improve the productivity of the operational site MS. Thus, according to this embodiment, the autonomous transport vehicle 20 can more reliably avoid contact with obstacles on the travel path 11 without deviating significantly from the travel path 11, thereby more reliably preventing unnecessary stops of the transport vehicle 20 and more reliably improving the productivity of the operational site MS.

[0072] Furthermore, in the transport vehicle 20 of the first embodiment, the first area calculation unit 2091 repeatedly calculates the predicted obstacle area during the period when the transport vehicle 20 is approaching the obstacle. The second area calculation unit 2092 repeatedly calculates the predicted passage area during the period when the transport vehicle 20 is approaching the obstacle. The control target calculation unit 2094 repeatedly calculates the target path and control target value during the period when the transport vehicle 20 is approaching the obstacle. The driving control unit 2095 repeatedly controls the driving of the transport vehicle 20 during the period when the transport vehicle 20 is approaching the obstacle.

[0073] As a result, the transport vehicle 20 can always calculate the optimal target path and control target values ​​based on the latest information, taking into account that the measurement error of the external sensors decreases as it approaches an obstacle. Therefore, the transport vehicle 20 can avoid obstacles and travel at the maximum target speed while significantly preventing unnecessary deceleration and lateral movement. Consequently, the transport vehicle 20 can significantly reduce its transport time and significantly improve the productivity of the operational site MS. Thus, according to this embodiment, the autonomous transport vehicle 20 can avoid contact with obstacles on the travel path 11 with minimal avoidance actions without deviating significantly from the travel path 11, thereby reliably preventing unnecessary stops of the transport vehicle 20 and significantly improving the productivity of the operational site MS.

[0074] Furthermore, in the transport vehicle 20 of the first embodiment, the road surface condition information includes the road surface friction coefficient.

[0075] If the road surface friction coefficient is included in the road surface condition information, vehicle behavior analysis becomes possible based on the force the tires receive from the ground. As a result, the transport vehicle 20 can use the highly accurate vehicle behavior analysis results to obtain highly accurate tracking errors according to the road surface conditions, and thus calculate the predicted passage area with high accuracy. Therefore, the transport vehicle 20 can reliably avoid obstacles and reliably prevent unnecessary deceleration and lateral movement, and travel at the maximum target speed. For this reason, the transport vehicle 20 can reliably shorten its transport time and reliably improve the productivity of the operational site MS. Thus, according to this embodiment, the autonomous transport vehicle 20 can reliably avoid contact with obstacles on the travel path 11 without deviating significantly from the travel path 11, thereby reliably preventing unnecessary stops of the transport vehicle 20 and reliably improving the productivity of the operational site MS.

[0076] Furthermore, in the transport vehicle 20 of the first embodiment, the road surface condition information includes road surface smoothness.

[0077] If road surface smoothness is included in the road surface condition information, the road surface condition of the travel path 11 that the transport vehicle 20 is scheduled to travel can be acquired in advance. As a result, the transport vehicle 20 can acquire the tracking error according to the road surface condition in advance, and thus the expected traversal area can be calculated in advance. Therefore, the transport vehicle 20 can reliably avoid obstacles, reliably prevent unnecessary deceleration and lateral movement, and reliably travel at the maximum target speed. As a result, the transport vehicle 20 can reliably shorten its transport time and reliably improve the productivity of the operational site MS. Thus, according to this embodiment, the autonomously traveling transport vehicle 20 can reliably avoid contact with obstacles on the travel path 11 without deviating significantly from the travel path 11, thereby reliably preventing unnecessary stops of the transport vehicle 20 and reliably improving the productivity of the operational site MS.

[0078] Furthermore, in the transport vehicle 20 of the first embodiment, the road surface condition information includes the amount of moisture on the road surface.

[0079] If the road surface moisture content is included in the road surface condition information, the road surface condition of points that the transport vehicle 20 has not yet traveled can be acquired in advance. As a result, the transport vehicle 20 can acquire the tracking error according to the road surface condition in advance, and thus calculate the expected passage area in advance. Therefore, the transport vehicle 20 can reliably avoid obstacles, reliably prevent unnecessary deceleration and lateral movement, and reliably travel at the maximum target speed. For this reason, the transport vehicle 20 can reliably shorten its transport time and reliably improve the productivity of the operational site MS. Thus, according to this embodiment, the autonomous transport vehicle 20 can reliably avoid contact with obstacles on the travel path 11 without deviating significantly from the travel path 11, thereby reliably preventing unnecessary stops of the transport vehicle 20 and reliably improving the productivity of the operational site MS.

[0080] [Second Embodiment] A second embodiment of the present invention will be described using Figures 15 to 18. In the second embodiment, components similar to those in the first embodiment will not be described.

[0081] In the second embodiment, applying the first embodiment, a control station 40 and a wireless communication network 30 are provided to control the traffic of transport vehicles 20, and obstacle information and road surface condition information of multiple transport vehicles 20 are aggregated at the control station 40. Then, in the second embodiment, the latest obstacle information and road surface condition information is transmitted to the transport vehicles 20, thereby more reliably improving the productivity of the operational site MS.

[0082] Figure 15 shows an example of an operational site MS of the transport vehicle 20 according to the second embodiment.

[0083] In the operational site MS shown in Figure 15, which is an open-cut mine or the like, at least one transport vehicle 20 for transporting cargo such as soil or ore, and a control station 40 for controlling the traffic of the transport vehicles 20 are connected to each other via a wireless communication network 30. The transport vehicles 20 travel along transport paths 10 designed from the shape of the operational site MS. As in Figure 1, in Figure 15, each autonomously operating transport vehicle 20 is denoted as 20-1, 20-2, ...

[0084] Figure 16 is a functional block diagram of the transport vehicle system 1. Figure 17 is a table showing an example of obstacle information for the transport vehicle system 1. Figure 18 is a table showing an example of road surface condition information for the transport vehicle system 1.

[0085] The transport vehicle system 1 is a system comprising multiple transport vehicles 20 and a control station 40 that controls the traffic of the multiple transport vehicles 20. The transport vehicle system 1 constitutes an autonomous transport system (AHS) introduced at the operational site MS.

[0086] The transport vehicle 20 shown in Figure 16 further includes, in addition to the components shown in Figure 3, a wireless communication device 2110 that is communicably connected to the control station 40 via the wireless communication network 30. As shown in Figure 17, the data stored in the obstacle information table in the obstacle information storage unit 2002 of the storage device 2000 includes, in addition to the data stored in the table shown in Figure 5, the update time of the obstacle information. The update time of the obstacle information is the time when the obstacle detection device 2070 stored the obstacle information in the storage device 2000. As shown in Figure 18, the data stored in the road surface condition information table in the road surface condition information storage unit 2003 of the storage device 2000 includes, in addition to the data stored in the table shown in Figure 6, the update time of the road surface condition information. The update time of the road surface condition information is the time when the road surface condition estimation device 2080 stored the road surface condition information in the storage device 2000.

[0087] The wireless communication device 2110 is a wireless communication device for connecting to the wireless communication network 30. The wireless communication device 2110 transmits obstacle information and road surface condition information stored in the storage device 2000 to the control station 40. When the control station 40 transmits obstacle information and road surface condition information to the wireless communication device 2110, it compares the update time of the transmitted obstacle information and road surface condition information with the update time of the obstacle information and road surface condition information stored in the storage device 2000. The wireless communication device 2110 then stores the newer obstacle information and road surface condition information in the storage device 2000.

[0088] The control station 40 comprises, as a hardware configuration, an information processing device 4050, an information communication device 4110, and an information storage device 4000.

[0089] The information processing device 4050 includes a CPU, RAM, and ROM, which perform program calculations, read and write information to the work area, temporarily store programs, and so on, thereby realizing various functions of the control station 40.

[0090] The information processing device 4050 generates control commands for the transport vehicles 20 to control their traffic, and controls the information communication device 4110 to transmit the generated control commands to the transport vehicles 20. The information processing device 4050 collects obstacle information and road surface condition information from the transport vehicles 20, and controls the information communication device 4110 and the information storage device 4000 to transmit the latest obstacle information and road surface condition information to the transport vehicles 20.

[0091] The information storage device 4000, similar to the storage device 2000 of the transport vehicle 20, includes a map information storage unit 4001 for storing map information, an obstacle information storage unit 4002 for storing obstacle information collected from the transport vehicle 20, and a road surface condition information storage unit 4003 for storing road surface condition information collected from the transport vehicle 20.

[0092] The information storage device 4000 stores the obstacle information and road surface condition information with the most recent update time among the obstacle information and road surface condition information transmitted from each of the multiple transport vehicles 20. For example, in the case of obstacle information, the information storage device 4000 stores the one with the same obstacle ID but a more recent update time, and deletes the older one.

[0093] The information and communication device 4110 is connected to multiple transport vehicles 20 via a wireless communication network 30. The information and communication device 4110 transmits obstacle information and road surface condition information stored in the information storage device 4000 to the multiple transport vehicles 20.

[0094] The information processing device 4050 may acquire the amount of road surface moisture based on the operation information of the watering vehicle or weather information. The information processing device 4050 may estimate the road surface condition by assuming that a road surface with a high amount of road surface moisture is a slippery road surface and a road surface with a low amount of road surface moisture is a non-slippery road surface, and acquire road surface condition information indicating the estimated road surface condition. The road surface condition information storage unit 4003 of the information storage device 4000 may store the road surface condition information acquired by the information processing device 4050 together with the road surface condition information transmitted from the transport vehicle 20.

[0095] As described above, the transport vehicle system 1 of the second embodiment is a system comprising a plurality of transport vehicles 20 and a control station 40 that controls the traffic of the plurality of transport vehicles 20. Each transport vehicle 20 is equipped with a wireless communication device 2110 that is communicably connected to the control station 40 via a wireless communication network 30, and a storage device 2000 that stores obstacle information and road surface condition information. The wireless communication device 2110 transmits the obstacle information and road surface condition information stored in the storage device 2000 to the control station 40. The control station 40 is equipped with an information communication device 4110 that is communicably connected to the plurality of transport vehicles 20 via a wireless communication network 30, and an information storage device 4000 that stores obstacle information and road surface condition information. The information storage device 4000 stores the obstacle information and road surface condition information with the most recent update time among the obstacle information and road surface condition information transmitted from each of the plurality of transport vehicles 20. The information and communication device 4110 transmits obstacle information and road surface condition information stored in the information storage device 4000 to multiple transport vehicles 20. The transport vehicles 20 compare the update time of the obstacle information and road surface condition information transmitted from the control station 40 with the update time of the obstacle information and road surface condition information stored in the storage device 2000, and store the newer obstacle information and road surface condition information in the storage device 2000.

[0096] As a result, the control station 40 can aggregate obstacle information and road surface condition information acquired by each transport vehicle 20 and transmit the latest information to each transport vehicle 20, thereby sharing information about obstacles and road surface conditions at points that each transport vehicle 20 has not yet traveled to with each transport vehicle 20 in advance. Therefore, each transport vehicle 20 can calculate the expected obstacle area and expected passage area in advance, reliably avoid obstacles, reliably prevent unnecessary deceleration and lateral movement, and reliably travel at the maximum target speed. For this reason, the transport vehicle system 1 can reliably shorten the transport time of the transport vehicles 20 and reliably improve the productivity of the operational site MS. Thus, according to this embodiment, by reliably avoiding contact with obstacles on the travel path 11 without deviating significantly from the travel path 11 while autonomously traveling, unnecessary stops of the transport vehicles 20 can be reliably prevented and the productivity of the operational site MS can be reliably improved.

[0097] It should be noted that the present invention is not limited to the embodiments described above, and various modifications are included. For example, the embodiments described above are described in detail to make the present invention easier to understand, and are not necessarily limited to those comprising all the components described. Furthermore, it is possible to replace some of the components of one embodiment with components of another embodiment, and it is also possible to add components of another embodiment to the components of one embodiment. In addition, it is possible to add, delete, or replace some of the components of each embodiment with components of other embodiments.

[0098] Furthermore, each of the above-mentioned components, functions, processing units, or processing means may be implemented in hardware, in whole or in part, for example, by designing them as integrated circuits. Alternatively, each of the above-mentioned components or functions may be implemented in software by a processor interpreting and executing programs that implement each function. Information such as programs, tables, or files that implement each function can be stored in a recording device such as memory, a hard disk, or an SSD, or in a recording medium such as an IC card, an SD card, or a DVD.

[0099] Furthermore, the control lines and information lines shown are those deemed necessary for explanatory purposes, and not all control lines and information lines are necessarily shown in the actual product. In reality, it can be assumed that almost all components are interconnected. [Explanation of Symbols]

[0100] 1...Transport vehicle system, 10...Transport route, 11...Travel route, 20...Transport vehicle, 30...Wireless communication network, 40...Control station, 2000...Memory device, 2001...Map information storage unit, 2002...Obstacle information storage unit, 2003...Road surface condition information storage unit, 2010...3D distance sensor (external sensor), 2020...Load sensor (internal sensor), 2030...Position sensor (internal sensor), 2040...Direction sensor (internal sensor), 2050...Speed ​​sensor (internal sensor), 2060...Steering angle Sensor (internal sensor), 2070... Obstacle detection device, 2080... Road surface condition estimation device, 2090... Control device, 2091... First area calculation unit, 2092... Second area calculation unit, 2093... Area correction unit, 2094... Control target calculation unit, 2095... Driving control unit, 2100... Drive unit, 4000... Information storage device, 4001... Map information storage unit, 4002... Obstacle information storage unit, 4003... Road surface condition information storage unit, 4050... Information processing device, 4110... Information communication device, MS... Operating site

Claims

1. A transport vehicle that autonomously travels according to a travel path set on a transport route, An external sensor for measuring objects around the transport vehicle, An internal sensor for measuring the vehicle status of the transport vehicle while it is in motion, An obstacle detection device that detects obstacles present in the travel path based on the measurement results of the external sensor and acquires obstacle information indicating the location and shape of the detected obstacle, A road surface condition estimation device that estimates the road surface condition of the travel route based on the measurement results of the internal sensor and acquires road surface condition information indicating the estimated road surface condition, The system includes a control device that controls the movement of the transport vehicle based on the aforementioned obstacle information and the aforementioned road surface condition information, The control device is A first region calculation unit calculates a predicted obstacle region that indicates an area on the travel path where obstacles are expected to exist that would obstruct the passage of the transport vehicle, based on the aforementioned obstacle information. A second region calculation unit calculates a predicted passage region that indicates the area the transport vehicle is expected to pass through when the transport vehicle travels near the obstacle according to the travel path, based on the road surface condition information and the travel path, and which does not overlap with the predicted obstacle region and is in contact with the predicted obstacle region in the width direction of the transport path. A control target calculation unit calculates a target path for the transport vehicle and control target values ​​including the target speed and target steering angle of the transport vehicle, so that the transport vehicle passes through the predicted passage area. The vehicle includes a travel control unit that controls the movement of the transport vehicle according to the control target value. A transport vehicle characterized by the following features.

2. The second region calculation unit, Based on the road surface condition information and the travel route, the predicted area to be passed is calculated. If the calculated predicted passage area overlaps with the predicted obstacle area, the predicted passage area is corrected so that the calculated predicted passage area does not overlap with the predicted obstacle area and is in contact with the predicted obstacle area in the width direction of the transport path. The transport vehicle according to feature 1.

3. The first area calculation unit calculates an area where the transport vehicle cannot pass over the obstacle from among the areas where the obstacle may exist, calculated based on the obstacle information, and calculates the predicted obstacle area by adding a margin based on the measurement error of the external sensor to the calculated area where the obstacle cannot pass. The transport vehicle according to feature 1.

4. The obstacle information includes at least one of the distribution of the height of the obstacle and the distribution of the height gradient of the obstacle. The first area calculation unit calculates, as the inaccessible area, at least one of the areas where the obstacle may exist, calculated based on the obstacle information, that exceeds the limit of the height of the obstacle that the transport vehicle can overcome, and that exceeds the limit of the height gradient of the obstacle that the transport vehicle can overcome. The transport vehicle according to feature 3.

5. The first region calculation unit calculates the predicted obstacle region by setting a larger margin as the distance from the external sensor to the obstacle increases. The transport vehicle according to feature 3.

6. The first area calculation unit repeatedly calculates the predicted obstacle area during the period when the transport vehicle approaches the obstacle. The second area calculation unit repeatedly calculates the predicted passage area during the period in which the transport vehicle approaches the obstacle. The control target calculation unit repeatedly calculates the target path and the control target value during the period in which the transport vehicle approaches the obstacle. The aforementioned driving control unit repeatedly executes driving control of the transport vehicle during the period in which the transport vehicle approaches the obstacle. The transport vehicle according to feature 1.

7. The aforementioned road surface condition information includes the road surface friction coefficient. The transport vehicle according to feature 1.

8. The aforementioned road surface condition information includes road surface smoothness. The transport vehicle according to feature 1.

9. The aforementioned road surface condition information includes the amount of moisture on the road surface. The transport vehicle according to feature 1.

10. A transport vehicle system comprising a plurality of transport vehicles as described in claim 1, and a control station for controlling the traffic of the plurality of transport vehicles, The transport vehicle comprises a wireless communication device that is communicably connected to the control station via a wireless communication network, and a storage device that stores the obstacle information and the road surface condition information. The wireless communication device transmits the obstacle information and road surface condition information stored in the storage device to the control station. The control station comprises an information communication device that is communicably connected to a plurality of transport vehicles via the wireless communication network, and an information storage device that stores the obstacle information and the road surface condition information. The information storage device stores the obstacle information and road surface condition information with the most recent update time among the obstacle information and road surface condition information transmitted from each of the multiple transport vehicles. The information storage device transmits the obstacle information and road surface condition information stored in the information storage device to a plurality of transport vehicles. The transport vehicle compares the update time of the obstacle information and road surface condition information transmitted from the control station with the update time of the obstacle information and road surface condition information stored in the storage device, and stores the newer of the obstacle information and road surface condition information in the storage device. A transport vehicle system characterized by the following features.

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