Train inspection robot scheduling control method and device, electronic equipment and medium

Abstract

The invention discloses a train inspection robot scheduling control method and device, electronic equipment and a medium, and the method comprises the steps: constructing a road network map of a target operation region of a controlled inspection robot, the road network map comprising information of all path points passed by the robot, path information between the path points, and the weight of each path information; the weight is calculated according to the distance between two path points in the path and a preset cost value; generating an adjacent matrix according to all path point information and path information in the road network map and the weight on each path; when the controlled inspection robot executes a task, acquiring a starting point and an end point of the current task, and traversing the adjacent matrix according to the acquired starting point and end point to calculate an optimal path; and sending the generated optimal path information to the controlled inspection robot. The system can adapt to the complex environment of train inspection, and the efficiency, safety and reliability of train inspection are improved.

Description

Technical Field

[0001] The present invention relates to the field of robot control technology, and in particular to a train inspection robot dispatching control method, device, electronic equipment and medium. Background Art

[0002] With the increase in railway operating mileage and vehicle running density, vehicle maintenance has become the key to ensuring efficient and safe train operation. Traditional vehicle maintenance is done manually, but manual maintenance has high labor costs and labor intensity. In addition, due to the concentrated time of vehicle entry into the warehouse, the operation time is basically the same, and the maintenance task is heavy, it often requires a large number of operators to work at the same time. At the same time, manual vehicle maintenance is greatly affected by the state of the maintenance personnel, and it is difficult to strictly implement unified maintenance standards. Using train intelligent inspection robots to replace manual operations in the train maintenance warehouse can conveniently move and operate in multiple scenarios such as upper and lower trenches, crossing tracks, and on the sides, bottoms, and roofs of vehicles. It can reduce labor costs and facilitate the implementation of strict unified standards to ensure maintenance quality.

[0003] In order to dispatch and control multiple inspection robots to perform inspections at different locations in the warehouse at the same time, it is necessary to perform path planning and intelligently determine the driving path during the execution of the inspection robot's task. For robot path planning and control, the existing technology usually plans according to the shortest path from the starting point to the end point, but this method is only applicable to scenes with relatively simple road conditions, while the inspection scene may involve various different environments such as level crossings, elevators, under the car, and on the side of the car. The time and cost of robot driving in different environments are different. For example, the elevator section takes longer and costs more than the flat section. The path planned according to the shortest distance may take longer and cost more, and is not the optimal path. Therefore, the traditional path planning control method is not suitable for inspection robots. Summary of the invention

[0004] The technical problem to be solved by the present invention is: in response to the technical problems existing in the prior art, the present invention provides a train inspection robot dispatching control method, device, electronic equipment and medium with simple implementation method, low cost, safety and high efficiency, which can adapt to the complex environment of train inspection and improve the efficiency and safety and reliability of train inspection.

[0005] In order to solve the above technical problems, the technical solution proposed by the present invention is:

[0006] A train inspection robot dispatching control method, the steps comprising:

[0007] Construct a road network map of the target operation area of ​​the controlled inspection robot, wherein the road network map includes information of all path points passed by the robot, path information between each path point, and weights of each path information, wherein the weights are calculated based on the distance between two path points in the corresponding path and a preset cost value;

[0008] Generate an adjacency matrix based on all path point information, path information and weights on each path in the road network map;

[0009] When the controlled inspection robot performs a task, it obtains the starting point and end point of the current task, and traverses the adjacency matrix based on the obtained starting point and end point to calculate the optimal path from the starting point to the end point;

[0010] The information of the optimal path is sent to the controlled inspection robot to control the driving path of the inspection robot.

[0011] Furthermore, the waypoint information includes location information and waypoint type information, the location information includes any one or more of map coordinate information, the robot's deflection angle, the robot's height information, and the robot's working area, and the waypoint type includes working point type and non-working point trajectory point type.

[0012] Furthermore, the path information includes the distance between two path points and a preset cost coefficient between the two path points, and also includes any one or more of a path sequence number, a starting path point of the path, and an ending path point of the path.

[0013] Furthermore, the optimal path from the starting point to the end point is calculated by traversing the adjacency matrix based on the obtained starting point and end point, and the A* algorithm is used to calculate the optimal path from the starting point to the end point. During the calculation process, the heuristic function uses the Euclidean distance from the current node to the destination node and the cost value from the current node to the destination node.

[0014] Furthermore, each element in the adjacency matrix is ​​calculated using the distance from the current node to another node and the weight.

[0015] Furthermore, when the information of the optimal path is sent to the controlled inspection robot, the path point information is sent multiple times in an incremental manner until all the path point information that needs to be passed from the starting point to the destination point is finally sent.

[0016] Furthermore, the multiple sending of the path point information in an incremental manner includes: sending the path point information of the starting point and the destination point for the first time, and starting from the second sending, each sending of the path point information includes the path point information sent last time and a specified number of new path point information.

[0017] Furthermore, it also includes: during the driving process of the controlled inspection robot, monitoring whether the controlled inspection robot enters the locked area, if so, blocking the locked area to prevent other robots from entering the current locked area until the controlled inspection robot passes through the locked area, and unlocking the locked area after monitoring that the controlled inspection robot leaves the locked area. The locked area is an area formed with a traffic control point as the center, and the traffic control point is a path point that is simultaneously connected to more than two path points.

[0018] Furthermore, the line connecting the traffic control point and each connection point is used as the radius of the closed area, so that the traffic control point and each connection point together form the closed area, and the connection point is a path point connected to the traffic control point.

[0019] A train inspection robot dispatching control device, comprising:

[0020] A road network construction module is used to construct a road network map of the target operation area of ​​the controlled inspection robot, wherein the road network map includes information of all path points passed by the robot, path information between each path point, and weights of each path information, wherein the weights are calculated based on the distance between two path points in the corresponding path and a preset cost value;

[0021] The path planning module is used to generate an adjacency matrix based on all the path point information, path information and weights on each path in the road network map; when the controlled inspection robot performs a task, it obtains the starting point and end point of the current task, and traverses the adjacency matrix based on the obtained starting point and end point to calculate the optimal path from the starting point to the end point;

[0022] The path sending module is used to send the information of the optimal path to the controlled inspection robot to control the driving path of the inspection robot.

[0023] Furthermore, it also includes a scheduling control module, which is used to monitor whether the controlled inspection robot enters a locked area during its driving process. If so, the locked area is blocked to prevent other robots from entering the current locked area until the controlled inspection robot passes through the locked area. When the controlled inspection robot is monitored to leave the locked area, the locked area is unlocked. The locked area is an area formed with a traffic control point as the center, and the traffic control point is a path point that is simultaneously connected to more than two path points.

[0024] An electronic device comprises a processor and a memory, wherein the memory is used to store a computer program, and the processor is used to execute the computer program to perform the above method.

[0025] A computer-readable storage medium storing a computer program, wherein the computer program implements the above method when executed.

[0026] Compared with the prior art, the advantages of the present invention are: the present invention constructs a road network map of the target operation area of ​​the inspection robot, and uses the distance between two path points and the cost value to jointly determine the path weight in the road network map, so as to accurately and efficiently describe the entire road network condition of the operation area; when the inspection robot is performing a task, the adjacency matrix is ​​traversed based on the starting point and end point of the task in combination with the road network map, and the adjacency matrix is ​​generated based on all path points and path weights. In the path planning process, the real-time position of the robot and the cost required for different paths can be fully considered, and finally the optimal path with the smallest distance and cost is planned, and the optimal path is sent to the robot, so as to complete the efficient path planning control of the inspection robot, effectively improve the operation efficiency and safety and reliability of the robot, and reduce the driving cost of the robot during driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a schematic diagram of the implementation flow of the train inspection robot dispatching control method according to Example 1 of the present invention.

[0028] Figure 2 It is a schematic diagram of the principle of generating an adjacency matrix in a specific application embodiment of Example 1 of the present invention.

[0029] Figure 3 It is a schematic diagram of the principle of determining a locking area for robot control in a specific application embodiment of Example 2 of the present invention.

[0030] Figure 4 It is a schematic diagram of the implementation process of multi-inspection robot control in a specific application embodiment of Example 3 of the present invention. DETAILED DESCRIPTION

[0031] The present invention is further described below in conjunction with the accompanying drawings and specific preferred embodiments, but the protection scope of the present invention is not limited thereby.

[0032] Inspection scenarios may involve different environments such as level crossings, elevators, under and on the side of vehicles. Since the robot navigation methods, required positioning accuracy, and robot running speeds used in different environments are different, the time and cost of passing a unit path are ultimately different. In order to schedule and control the inspection operations of multiple inspection robots at different positions in the warehouse at the same time, the inspection robots are controlled by path planning. The present invention first constructs a road network map of the target operation area of ​​the inspection robot, and the road network map includes information on path points, paths and path weights, wherein the path weight is determined by the distance between two path points and the cost value to characterize the distance between the path points and the cost required, thereby accurately and efficiently describing the entire road network status of the operation area; when the inspection robot is performing a task, the adjacency matrix is ​​traversed based on the starting point and end point of the task in combination with the road network map, and the adjacency matrix is ​​generated based on all path points and path weights. In the path planning process, the real-time position of the robot and the cost required for different paths can be fully considered, and finally an optimal path with the smallest distance and cost is planned, and the optimal path is sent to the robot, so as to complete the efficient path planning control of the inspection robot, effectively improve the operation efficiency and safety and reliability of the robot, and reduce the driving cost of the robot during driving.

[0033] Embodiment 1:

[0034] like Figure 1 As shown, the steps of the train inspection robot dispatching control method of this embodiment include:

[0035] Step S01. Construct a road network map of the target operation area of ​​the controlled inspection robot, the road network map includes all the path point information passed by the robot, the path information between each path point and the weight of each path information, the weight is calculated according to the distance between two path points in the corresponding path and the preset cost value;

[0036] Step S02. Generate an adjacency matrix based on all path point information, path information and weights on each path in the road network map;

[0037] Step S03. When the controlled inspection robot performs a task, it obtains the starting point and end point of the current task, and traverses the adjacency matrix based on the obtained starting point and end point to calculate the optimal path;

[0038] Step S04: Send the generated optimal path information to the controlled inspection robot to control the driving path of the inspection robot.

[0039] In this embodiment, the road network map of the entire robot operation constructed in step S01 is composed of the path passed by the robot and the path between each point, that is, the road network map includes two parts: path points and paths. According to the connectivity between the path points, the path is formed by connecting the connectable path points. The path point information includes location information and path point type information. Since the inspection environment may be a flat ground or an area with a certain height, the location information specifically includes the coordinate information of the map, the deflection angle of the robot, the height information of the robot, and the operation area where the robot is located. Since the path point may be an operation point where the robot needs to operate, such as a charging station, an ordinary station, an elevator station, or a trajectory point that needs to be passed to reach the operation point, the path point type in this embodiment specifically includes the operation point type and the trajectory point type of the non-operation point. The path information specifically includes the distance between two path points and the preset cost coefficient between the two path points, as well as the path sequence number, the starting path point of the path, the ending path point of the path, and other information.

[0040] When constructing a road network map, the road network map includes all the path points that the robot can pass through, and is marked with the location, height (such as floor information), location, and type information of whether it is an operation point of each path point, etc., and also includes the information of each connected path and the weight of each path. The weight is calculated based on the distance between the two path points and the preset cost value, so that the priority weight of the path can be determined by combining the distance between the two path points and the cost value required. The cost value can be configured in advance according to the cost required for different paths. For example, the distance between A1 and A2 is the same as that between A1 and A3. A1 and A2 need to pass through an elevator, and a larger cost coefficient value is set, such as 1.2. A1 to A3 is a plane path that can be reached directly, so a smaller cost coefficient value is configured, such as 1.0, so that the weight of the path that needs to pass through the elevator will be greater than the path that is directly reached. In the subsequent path search, the optimal path is obtained by searching the path with the smallest weight, so that the path with the smallest search distance and cost is provided to the controlled inspection robot.

[0041] In a specific application embodiment, for path points, x, y, z, theta, and area can be used to represent the location information of each path point, where x and y are the coordinate information of the map created based on the robot SLAM, theta is the deflection angle of the robot, z is the floor information where the robot is located, area refers to the robot's operating area, type represents the type of path point, and id is used to represent the node serial number. The serial number of each path point is globally unique. For each path, id is used to represent the path serial number, startNode represents the starting path point of the path (the value is the id in the path point), endNode represents the ending path point of the path (the value is the id in the path point), distance represents the distance between two path points, and cost represents the cost coefficient value between the two points. The weight on the path is specifically obtained by multiplying the distance between the two path points by the cost coefficient value, that is, distance*cost.

[0042] It is understandable that the weight can also be obtained by other calculation methods based on the distance between two path points and the cost coefficient value, and is not limited to the product calculation method. The specific calculation method can be configured and selected according to actual needs.

[0043] This embodiment configures the path points, paths and path weights in the above manner, and can accurately describe the work points and work paths of the entire work area, thereby facilitating accurate path planning for the robot. It can also effectively save the robot's work points and path information in the entire area, thereby facilitating simultaneous support for multiple robots to work together in the same area, so that it can effectively support multi-floor and regional operations.

[0044] When calculating the path, it is necessary to calculate the path that the robot passes through from the starting path point to the destination path point. The steps of this embodiment specifically generate an adjacency matrix based on all the path point information (points) and path data (edges) and the weights (distance*cost) on the path. Each element in the adjacency matrix corresponds to the distance from the current node to another node and the weight calculated, and then the adjacency matrix is ​​traversed according to the starting path point and the ending path point to calculate the optimal path. Different cost coefficients can be set according to the location of different paths, the navigation method, and the different interactive devices involved, so that the appropriate path can be calculated more accurately. The method of generating the adjacency matrix through the above-mentioned path weights can support the embedding of multiple different algorithms, thereby quickly completing the path calculation.

[0045] by Figure 2For example, Figure (a) corresponds to the path points and path information, where points 1, 2, 3, and 4 represent path points, S1, S2, S3, and S4 represent edges, and D represents the weight corresponding to the edge. The weight is calculated by multiplying the distance by the cost value. Figure (b) is the corresponding adjacency matrix generated. Each element in the matrix corresponds to the product of the distance from the current node to another node and the weight. The weight is configured according to different sections. For example, in the section with a limited speed of 1 on the path, the weight is set to 1, and the elevator section is set to 1.8. For the section with a speed of 2m / s, the weight is set to 0.5, and the section with a speed of 1m / s is set to 1.

[0046] When calculating the optimal path in this embodiment, the improved A* algorithm is specifically used. The improved A* algorithm is based on the A* algorithm. The heuristic function uses the Euclidean distance from the current point to the destination node and the cost value from the current point to the destination node, that is, the cost coefficient is added to the heuristic function (Euclidean distance from the current point to the destination node) to introduce the cost size of the path to ensure that the cost required for the planned path is as small as possible. The cost value can directly use the above-mentioned preset cost coefficient value, or it can be dynamically added according to the current position of the robot to further improve the reliability and rationality of the planning according to the position of the robot.

[0047] After the present embodiment completes the path calculation and selects the optimal path, when the optimal path information is sent to the inspection robot, the path point information is sent multiple times in an incremental manner until all the path point information required to pass from the starting point to the destination point is finally sent. The incremental manner is to add a specified amount of path point information each time it is sent. By sending in an incremental manner, since each information sent contains the information sent last time, the information sent last time is also the historical trajectory of the robot, so that the historical trajectory of the robot can be traced, and it can also avoid the robot waiting at a certain path point, so that the robot can run smoothly.

[0048] In a specific application embodiment, sending path point information multiple times in an incremental manner includes: sending the path point information of the starting point and the destination point for the first time, and starting from the second sending, each time the path point information sent includes the path point information sent last time and a specified number of new path point information.

[0049] Take the robot from path point 1 to path point 10 as an example. Two trajectory points are sent each time, and the path is sent in five times. The sending method is as follows:

[0050] The first delivery: the path points are waypoint 1, waypoint 2, and destination point 10;

[0051] Second delivery: the trajectory information is waypoint 1, waypoint 2, trajectory point 3, trajectory point 4, and destination point 10;

[0052] The third time the trajectory information is sent: waypoint 1, waypoint 2, trajectory point 3, trajectory point 4, path point 5, waypoint 6, and destination point 10;

[0053] The fourth delivery: the trajectory information is waypoint 1, waypoint 2, trajectory point 3, trajectory point 4, waypoint 5, waypoint 6, waypoint 7, path point 8, and destination point 10;

[0054] The fifth issuance: the trajectory information is waypoint 1, waypoint 2, trajectory point 3, trajectory point 4, waypoint 5, waypoint 6, waypoint 7, waypoint 8, waypoint 9, waypoint 10, and destination point 10.

[0055] The above-mentioned midway points are related operation points, and the trajectory points are the path points along the way.

[0056] Since the waypoints are sent down multiple times in an incremental manner as sparse trajectory points of the road network, the robot will interpolate and encrypt the sparse trajectory on the robot side. For example, when the robot receives two waypoints (from waypoint 1 to waypoint 2), the robot side adds some transition points between waypoint 1 and waypoint 2 so that the robot can reach them.

[0057] Furthermore, the robot can be made to wait or re-plan by pausing the sending or stopping the robot.

[0058] The path planning device for the train inspection robot in this embodiment includes:

[0059] A road network construction module is used to construct a road network map of the target operation area of ​​the controlled inspection robot. The road network map includes information of all path points passed by the robot, path information between each path point, and weights of each path information. The weights are calculated based on the distance between two path points in the corresponding path and a preset cost value.

[0060] The path planning module is used to generate an adjacency matrix based on all path point information, path information and weights on each path in the road network map; when the controlled inspection robot performs a task, it obtains the starting point and end point of the current task, and traverses the adjacency matrix based on the obtained starting point and end point to calculate the optimal path from the starting point to the end point;

[0061] The path sending module is used to send the information of the optimal path to the controlled inspection robot to control the driving path of the inspection robot.

[0062] The train inspection robot path planning device of this embodiment corresponds to the above-mentioned train inspection robot path planning, wherein the road network construction module corresponds to step S1 to perform road network construction, the path planning module executes step S2 to generate the adjacency matrix and step S3 to plan the path, and the path sending module corresponds to step S4 to execute path sending, and the details will not be repeated one by one.

[0063] Embodiment 2:

[0064] In the inspection robot system, there are usually multiple inspection robots working at the same time, and each inspection robot needs to work collaboratively with other robots, pedestrians, trains, etc. In the process of multi-robot collaborative operation, there will be problems such as collision and deadlock. This embodiment is basically the same as embodiment 1, except that it also includes traffic control for each inspection robot, including: during the driving process of the controlled inspection robot, monitoring whether the controlled inspection robot enters the locked area, if so, blocking the locked area to prevent other robots from entering the current locked area until the controlled inspection robot passes through the locked area, and when the controlled inspection robot is monitored to leave the locked area, the locked area is unlocked. The locked area is an area formed with the traffic control point as the center, and the traffic control point is a path point that is simultaneously connected to more than two path points. Through the above method, robot deadlock can be effectively reduced and robot collision can be avoided, so that it can support multiple robots to work collaboratively in the same area.

[0065] In a specific application embodiment, the traffic control point is the intersection of multiple paths in the road network. A closed area is formed with the traffic control point as the center. Once a robot enters the closed area, the closed area will be blocked, and other robots cannot enter the closed area and need to wait outside the area. When the robot leaves the closed area, the closed area will be unlocked, and the robot waiting outside the area can enter the closed area, ensuring the safe driving of each inspection robot and avoiding collisions and deadlock problems.

[0066] In this embodiment, the line between the traffic control point and each connection point is used as the radius of the closed area, so that the traffic control point and each connection point together form a closed area, and the connection point is a path point connected to the traffic control point.

[0067] by Figure 3 For example, the road network map describes the connectivity of each point through points and edges. If a point is connected to more than two points at the same time, it is considered to be a traffic control point. For example, the path point 5 in the figure can be connected to 2, 3, 4, 6, and 8 at the same time. Therefore, node 5 is used as a traffic control point, and the two path points form a closed area with a radius, that is, node 5 and nodes 2, 3, 4, 6, and 8 together form a closed area. Once the robot enters the closed area, the area is blocked, and other robots cannot enter the area and must wait at a point outside the area to ensure that the robot passes the control point safely.

[0068] The train inspection robot dispatching control device of this embodiment is based on the train inspection robot dispatching control device of embodiment 1 and further includes: a dispatching control module, which is used to monitor whether the controlled inspection robot enters the locked area during the driving process. If so, the locked area is blocked to prevent other robots from entering the current locked area until the controlled inspection robot passes through the locked area. When the controlled inspection robot is monitored to leave the locked area, the locked area is unlocked. The locked area is an area formed with the traffic control point as the center. The traffic control point is a path point that is connected to more than two path points at the same time. By setting the dispatching control module, the deadlock of the robot is effectively reduced and the collision of the robot is avoided, thereby supporting the collaborative operation of multiple robots in the same area.

[0069] Embodiment 3:

[0070] This embodiment adopts embodiments 1 and 2 in a specific application embodiment to realize the dispatching control of the train inspection robot. Figure 4 As shown in the figure, the specific implementation process includes:

[0071] Step 1: Road network construction: Build the entire road network map based on the situation of the on-site operation area, including the robot's operation points on the entire road network and the weights and connectivity between each point;

[0072] Step 2: Road network loading: Complete the loading of the entire road network data in the dispatching system, generate the path point information, edge information, and adjacency matrix based on the road network information, and identify traffic control points and closed areas;

[0073] Step 3: Task issuance: The business system issues tasks to the controlled inspection robot;

[0074] Step 4: Path calculation: According to the acquired starting point and end point of the task, the path calculation and planning are completed in the manner of Example 1;

[0075] Step 5: Path delivery: After planning is completed, the path is incrementally delivered to the robot;

[0076] Step 6: Traffic control: When the robot is performing the task, it monitors whether there is an inspection robot entering the locked area in accordance with the method in Example 2. If so, the locked area is blocked to prevent other robots from entering the locked area until the inspection robot leaves the locked area and unlocks the locked area.

[0077] Through the above steps, this embodiment can not only accurately determine the work points and work paths of the entire work area, accurately plan the path of the robot, and effectively save the work points and path information of the robot in the entire area, but also support multiple robots to work together in the same area, realize multi-robot multi-floor and regional operation control, and at the same time reduce robot deadlock and avoid collisions between robots.

[0078] The present invention also includes an electronic device, including a processor and a memory, wherein the memory is used to store a computer program, and the processor is used to execute the computer program to perform the method as described in the above embodiment of the present invention.

[0079] It is understandable that the method of the present invention can be executed by a single device, such as a computer or server, etc., and can also be applied to a distributed scenario and completed by multiple devices in cooperation with each other. In the case of a distributed scenario, one of the multiple devices can only execute one or more steps in the above method of this embodiment, and multiple devices interact to complete the above method. The processor can be implemented in the form of a general-purpose CPU, a microprocessor, an application-specific integrated circuit, or one or more integrated circuits, etc., for executing related programs to implement the above method of this embodiment. The memory can be implemented in the form of a read-only memory ROM, a random access memory RAM, a static storage device, and a dynamic storage device. The memory can store an operating system and other application programs. When the above method of this embodiment is implemented by software or firmware, the relevant program code is stored in the memory and called and executed by the processor.

[0080] The present invention also includes a computer-readable storage medium storing a computer program, and when the computer program is executed, the method of the above embodiment of the present invention is implemented.

[0081] Those skilled in the art should understand that the above-mentioned embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application can take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the functions in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the functions specified in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0082] The above is only a preferred embodiment of the present invention, and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment, it is not intended to limit the present invention. Therefore, any simple modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the content of the technical solution of the present invention shall fall within the scope of protection of the technical solution of the present invention.

Claims

1. A train inspection robot dispatching control method, characterized in that the steps include: Construct a road network map of the target operation area of ​​the controlled inspection robot, wherein the road network map includes information of all path points passed by the robot, path information between each path point, and weights of each path information, wherein the weights are calculated based on the distance between two path points in the corresponding path and a preset cost value; Generate an adjacency matrix based on all path point information, path information and weights on each path in the road network map; When the controlled inspection robot performs a task, it obtains the starting point and end point of the current task, and traverses the adjacency matrix based on the obtained starting point and end point to calculate the optimal path from the starting point to the end point; The information of the optimal path is sent to the controlled inspection robot to control the driving path of the inspection robot.

2. The train inspection robot dispatching control method according to claim 1, characterized in that: The waypoint information includes location information and waypoint type information. The location information includes any one or more of map coordinate information, the robot's deflection angle, the robot's height information, and the robot's working area. The waypoint type includes working point type and non-working point trajectory point type.

3. The train inspection robot dispatching control method according to claim 1, characterized in that: The path information includes the distance between two path points and a preset cost coefficient between the two path points, and also includes any one or more of a path sequence number, a starting path point of the path, and an ending path point of the path.

4. The train inspection robot dispatching control method according to claim 1, characterized in that: The optimal path from the starting point to the end point is calculated by traversing the adjacency matrix based on the obtained starting point and end point, and the A* algorithm is used to calculate the optimal path from the starting point to the end point. During the calculation process, the heuristic function uses the Euclidean distance from the current node to the destination node and the cost value from the current node to the destination node.

5. The train inspection robot dispatching control method according to claim 1, characterized in that: Each element in the adjacency matrix is ​​calculated using the distance from the current node to another node and the weight.

6. The train inspection robot dispatching control method according to any one of claims 1 to 5, characterized in that: When the information of the optimal path is sent to the controlled inspection robot, the path point information is sent multiple times in an incremental manner until all the path point information that needs to be passed from the starting point to the destination point is finally sent.

7. The train inspection robot dispatching control method according to claim 6, characterized in that: The incrementally sending of the path point information multiple times includes: sending the path point information of the starting point and the destination point for the first time, and starting from the second sending, each time the path point information sent includes the path point information sent last time and a specified number of new path point information.

8. The train inspection robot dispatching control method according to any one of claims 1 to 5, characterized in that: Also includes: During the driving process of the controlled inspection robot, monitor whether the controlled inspection robot enters the locked area. If so, block the locked area to prevent other robots from entering the current locked area until the controlled inspection robot passes through the locked area. When the controlled inspection robot is monitored to leave the locked area, the locked area is unlocked. The locked area is an area formed with a traffic control point as the center, and the traffic control point is a path point that is simultaneously connected to more than two path points.

9. The train inspection robot dispatching control method according to claim 8, characterized in that: The traffic control point and the connecting line between each connection point are used as the radius of the closed area, so that the traffic control point and each connection point together form the closed area, and the connection point is a path point connected to the traffic control point.

10. A train inspection robot dispatching control device, characterized in that: include: A road network construction module is used to construct a road network map of the target operation area of ​​the controlled inspection robot, wherein the road network map includes information of all path points passed by the robot, path information between each path point, and weights of each path information, wherein the weights are calculated based on the distance between two path points in the corresponding path and a preset cost value; A path planning module, used to generate an adjacency matrix based on all path point information, path information and weights on each path in the road network map; When the controlled inspection robot performs a task, it obtains the starting point and end point of the current task, and traverses the adjacency matrix based on the obtained starting point and end point to calculate the optimal path from the starting point to the end point; The path sending module is used to send the information of the optimal path to the controlled inspection robot to control the driving path of the inspection robot.

11. The train inspection robot dispatching control device according to claim 10, characterized in that: It also includes a scheduling control module, which is used to monitor whether the controlled inspection robot enters a locked area during its driving process. If so, the locked area is blocked to prevent other robots from entering the current locked area until the controlled inspection robot passes through the locked area. When the controlled inspection robot is monitored to leave the locked area, the locked area is unlocked. The locked area is an area formed with a traffic control point as the center, and the traffic control point is a path point that is simultaneously connected to more than two path points.

12. An electronic device comprising a processor and a memory, wherein the memory is used to store a computer program, wherein: The processor is configured to execute the computer program to perform the method according to any one of claims 1 to 9.

13. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed, the method according to any one of claims 1 to 9 is implemented.

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