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

Abstract

The application discloses a train inspection robot scheduling control method and device, electronic equipment and medium, and the method comprises the following steps: constructing a road network map of a target work area of a controlled inspection robot, wherein the road network map comprises all path point information of the robot, path information between each path point, and a weight value of each path information, the weight value is calculated according to the distance between two path points in the path and a preset generation value; generating an adjacency matrix according to all path point information, path information and the weight value on each path in the road network map; when the controlled inspection robot executes a task, obtaining a starting point and an ending point of the current task, and calculating an optimal path according to the obtained starting point and ending point by traversing the adjacency matrix; and sending the generated optimal path information to the controlled inspection robot. The application can adapt to the complex environment of train inspection, and improve the efficiency, safety and reliability of train inspection.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of robot control, and in particular to a train inspection robot scheduling control method and device, electronic equipment and medium. BACKGROUND

[0002] With the increase of railway operation mileage and vehicle operation density, vehicle maintenance has become the key to ensure efficient and safe operation of trains. Traditional vehicle maintenance is completed by manual work, but manual maintenance has high labor cost and high labor intensity, and because the vehicle enters the warehouse at the same time, the operation time is basically the same, and the maintenance task is heavy, a large number of workers are often required 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 a train intelligent inspection robot to replace manual work in the train maintenance warehouse can facilitate movement and operation in multiple scenarios such as up and down trenches, crossing tracks, and vehicle sides and vehicle bottoms, which can reduce labor costs and facilitate the implementation of strict unified standards to ensure maintenance quality.

[0003] In order to schedule and control multiple inspection robots to perform inspection operations at different positions in the warehouse, path planning needs to be intelligently determined during the execution of the task by the inspection robot. For robot path planning control, the prior art usually plans the path according to the shortest distance from the starting point to the ending point, but this type of method is only suitable for simple road conditions, while the inspection scene may involve various different environments such as level crossings, elevators, vehicle bottoms, and vehicle sides. The time and cost of robot travel in different environments are different, for example, the time and cost required for an elevator section are higher than those for a flat section. The path planned according to the shortest distance may take more time and cost, and is not the optimal path, so the traditional path planning control method is not suitable for inspection robots. SUMMARY

[0004] The technical problem to be solved by the present application is that in view of the technical problems existing in the prior art, the present application provides a train inspection robot scheduling control method, device, electronic equipment and medium which are simple to implement, low in cost, safe and efficient, can adapt to the complex environment of train inspection, and improve the efficiency and safety and reliability of train inspection.

[0005] To solve the above technical problems, the technical solution provided by the present application is:

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

[0007] Construct a road network map of a target operation area of a controlled inspection robot, the road network map comprising all path point information of the robot, path information between each path point, and a weight value of each path information, the weight value being calculated according to a distance between two path points in the corresponding path and a preset cost value;

[0008] Generate an adjacency matrix according to all path point information, path information, and weight values on each path in the road network map;

[0009] When the controlled inspection robot performs a task, obtain a starting point and an ending point of the current task, and calculate an optimal path from the starting point to the ending point according to the obtained starting point and ending point by traversing the adjacency matrix.

[0010] Send information of the optimal path to the controlled inspection robot to control a driving path of the inspection robot.

[0011] Further, the path point information comprises position information and path point type information, the position information comprising coordinate information of the map, a bias angle of the robot, height information of the robot, and any one or more of the operation area in which the robot is located, and the path point type comprising an operation point type and a non-operation point trajectory point type.

[0012] Further, the path information comprises a distance between two path points, a preset cost coefficient between the two path points, and any one or more of a path serial number, a starting path point of the path, and an ending path point of the path.

[0013] Further, in the calculation of the optimal path from the starting point to the ending point by traversing the adjacency matrix according to the obtained starting point and ending point, an A* algorithm is used to calculate the optimal path, and a heuristic function uses a Euclidean distance from a current node to a destination node and a cost value from the current node to the destination node in the calculation process.

[0014] Further, each element in the adjacency matrix corresponds to a distance from a current node to another node and a weight value calculated using the distance.

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

[0016] Further, the multiple issuance of the path point information in the incremental manner comprises: first sending path point information of the starting point and the destination point, and from the second sending, each time the path point information issued comprises the path point information issued last time and a specified number of new path point information.

[0017] Further, the method further comprises: monitoring whether the controlled inspection robot enters a closed area during the driving process; if yes, locking the closed area to shield other robots from entering the current closed area until the controlled inspection robot passes through the closed area; and unlocking the closed area after monitoring that the controlled inspection robot drives away from the closed area, wherein the closed area is an area formed with a traffic control point as a center, and the traffic control point is a path point simultaneously connected with more than two path points.

[0018] Further, the method further comprises: monitoring whether the controlled inspection robot enters a closed area during the driving process; if yes, locking the closed area to shield other robots from entering the current closed area until the controlled inspection robot passes through the closed area; and unlocking the closed area after monitoring that the controlled inspection robot drives away from the closed area, wherein the closed area is an area formed with a traffic control point as a center, and the traffic control point is a path point simultaneously connected with more than two path points.

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

[0020] a road network construction module configured to construct a road network map of a target work area of the controlled inspection robot, wherein the road network map comprises all path point information of the robot, path information between each path point, and a weight value of each path information, and the weight value is calculated according to a distance between two path points in the corresponding path and a preset generation value;

[0021] a path planning module configured to generate an adjacency matrix according to all path point information, path information and weight values on each path in the road network map; when the controlled inspection robot performs a task, the path planning module is configured to acquire a starting point and an ending point of the current task, and calculate an optimal path from the starting point to the ending point according to the acquired starting point and ending point by traversing the adjacency matrix;

[0022] a path issuing module configured to send information of the optimal path to the controlled inspection robot to control a driving path of the inspection robot.

[0023] Further, the method further comprises: monitoring whether the controlled inspection robot enters a closed area during the driving process; if yes, locking the closed area to shield other robots from entering the current closed area until the controlled inspection robot passes through the closed area; and unlocking the closed area after monitoring that the controlled inspection robot drives away from the closed area, wherein the closed area is an area formed with a traffic control point as a center, and the traffic control point is a path point simultaneously connected with more than two path points.

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

[0025] A computer readable storage medium storing a computer program, wherein the computer program is configured to perform the above method when executed.

[0026] Compared with the prior art, the application has the advantages that: the application can accurately and efficiently describe the entire road network condition of the work area by constructing the road network map of the target work area of the inspection robot, determining the path weight in the road network map by the distance between two path points and the value, when the inspection robot performs the task, the starting point and the ending point of the task are combined with the road network map to perform the adjacency matrix traversal, the adjacency matrix is generated according to all path points and path weights, the real-time position of the robot and the value paid by different paths are fully considered in the path planning process, the optimal path with the minimum distance and value is finally planned, the optimal path is sent to the robot, the efficient path planning control of the inspection robot is completed, the operation efficiency and safety and reliability of the robot are effectively improved, and the driving value in the driving process of the robot is reduced. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 is the implementation flow diagram of the train inspection robot scheduling control method of the embodiment 1 of the application.

[0028] Figure 2 is the principle diagram of generating the adjacency matrix in the specific application embodiment of the embodiment 1 of the application.

[0029] Figure 3 is the principle diagram of determining the closed area to control the robot in the specific application embodiment of the embodiment 2 of the application.

[0030] Figure 4 is the implementation flow diagram of implementing the multi-inspection robot control in the specific application embodiment of the embodiment 3 of the application. DETAILED DESCRIPTION

[0031] The application is further described below in combination with the drawings of the specification and specific preferred embodiments, but the protection scope of the application is not limited by this.

[0032] The inspection scene can involve different environments such as a level crossing, an elevator, a car bottom, and a car side. Due to different robot navigation modes, required positioning accuracy, and robot running speeds in different environments, the time and cost of a unit path are different. In order to schedule and control the inspection of multiple robots at different positions in the warehouse, the path of the inspection robot is planned and controlled. The road network map of the target work area of the inspection robot is first constructed, including the information of path points, paths, and path weights. The distance between two path points and the cost value are used to determine the path weight, to represent the distance between path points and the cost of the required cost, so as to accurately and efficiently describe the entire road network condition of the work area. During the execution of the task by the inspection robot, the adjacency matrix is traversed according to the start point and end point of the task combined with the road network map. The adjacency matrix is generated according to all path points and path weights. In the path planning process, the real-time position of the robot and the cost value required by different paths are fully considered, and the optimal path with the minimum distance and cost is finally planned. The optimal path is sent to the robot, and the efficient path planning control of the inspection robot is completed, the running efficiency and safety and reliability of the robot are effectively improved, and the driving cost in the driving process of the robot is reduced.

[0033] Embodiment 1

[0034] As shown in Figure 1 The steps of the train inspection robot scheduling control method of the present embodiment include:

[0035] Step S01. Construct the road network map of the target work area of the controlled inspection robot, which includes all path point information of the robot, path information between each path point, and weight value of each path information. The weight value 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 according to all path point information, path information, and weight value on each path in the road network map.

[0037] Step S03. When the controlled inspection robot executes a task, the start point and end point of the current task are obtained, and the optimal path is calculated by traversing the adjacency matrix according to the obtained start point and end point.

[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 the embodiment, the entire robot operation road network map constructed in step S01 is composed of the path passed by the robot and the path between each point, i.e., the road network map is composed of two parts of path points and paths, and the paths are formed by connecting the connectable path points according to the connectivity between the path points. The path point information includes position information and path point type information. Since the inspection environment can be a flat ground or an area with a certain height, the position information specifically includes map coordinate information, robot bias angle, robot height information, and robot operating area information. Since the path point can be an operating point, such as a charging point, a general point, and an elevator point, or a trajectory point needed to be passed to reach the operating point, the path point type in the embodiment specifically includes operating point type and non-operating point trajectory point type. The path information specifically includes the distance between two path points, the preset cost coefficient between two path points, path serial number, starting path point of the path, ending path point of the path, and the like.

[0040] When constructing the road network map, all path points that can be passed by the robot are included in the road network map, and the position, height (e.g., floor information), area, and type information of whether it is an operating point of each path point are labeled, and the connected path information and the weight of each path are also included. The weight is calculated according to the distance between two path points and the preset cost value, so that the priority weight of the path can be determined by comprehensively considering the distance between two path points and the cost value. The cost value can be configured according to the cost required by different paths. For example, the distance between A1 and A2 and A1 and A3 is the same, A1 and A2 need to pass through the elevator, and a larger cost coefficient value is set, e.g., 1.2, A1 and A3 is a flat path and can be directly reached, and a smaller cost coefficient value is configured, e.g., 1.0, so that the weight of the path that needs to pass through the elevator is greater than the directly reached path. The optimal path is obtained by searching the path with the smallest weight in the subsequent path search, so as to provide the path with the smallest distance and cost to the controlled inspection robot.

[0041] In a specific application embodiment, the position information of each path point can be represented by x, y, z, theta, area, where x and y are coordinate information of a map created based on SLAM of the robot, theta is the deflection angle of the robot, z is the floor information where the robot is located, area refers to the working area of the robot, type represents the type of the path point, id is used to represent the node number, and 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 two points. The weight on the path is specifically obtained by multiplying the distance between two path points and the cost coefficient value, i.e., distance*cost.

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

[0043] The embodiment can accurately describe the working points and working paths of the entire working area by configuring the path points, paths and path weights in the above manner, thereby facilitating accurate path planning of the robot, and effectively saving the working point and path information of the robot in the entire area, thereby facilitating simultaneous collaborative work of multiple robots in the same area, and enabling effective support for multi-floor and sub-area work.

[0044] When calculating the path, the path passed by the robot from the starting path point to the destination path point needs to be calculated. The embodiment specifically generates an adjacency matrix according to all path point information (nodes) and path data (edges) and the weight on the path (distance*cost), each element in the adjacency matrix corresponds to the distance and weight calculated from the current node to another node, and then the optimal path is calculated by traversing the adjacency matrix according to the starting path point and the ending path point. Different cost coefficients can be set according to the location, navigation method and interactive device involved in different paths, so that a suitable path can be calculated more accurately. The above method of generating an adjacency matrix by a path weight can support the embedding of multiple different algorithms, thereby quickly completing path calculation.

[0045] In the embodiment, the path point is represented by x, y, z, theta, area, type, and id, the path is represented by id, startNode, endNode, distance, and cost, and the path weight is represented by distance*cost. Figure 2For example, Fig. (a) corresponds to the path points and path information, wherein points 1, 2, 3, 4 represent path points, S1, S2, S3, S4 represent edges, D represents the weight value corresponding to the edge, and the weight value is calculated by multiplying the distance by the value. Fig. (b) is the generated adjacency matrix, and each element in the matrix corresponds to the product of the distance from the current node to another node and the weight value. The weight value is configured according to different road segments, for example, the weight value is set to 1 for a road segment with a limited speed of 1, the weight value is set to 1.8 for an elevator segment, and the weight value is set to 0.5 for a road segment with a speed of 2 m / s, and the weight value is set to 1 for a road segment with a speed of 1 m / s.

[0046] In the embodiment, the improved A* algorithm is used to calculate the optimal path. The improved A* algorithm is based on the A* algorithm, and the heuristic function uses the Euclidean distance from the current point to the destination node and the value of 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, so as to ensure that the cost required by the planned path is as small as possible. The value can be directly used as the above-mentioned preset cost coefficient value, or can be dynamically added according to the current position of the robot to further improve the reliability and rationality of the planned path according to the position of the robot.

[0047] After the optimal path is calculated in the embodiment, the optimal path information is sent to the inspection robot. Specifically, the path point information is issued multiple times in an incremental manner until all path points that need to pass through from the starting point to the destination point are finally issued. The incremental manner means that a specified amount of path point information is added each time. By issuing in an incremental manner, the information issued each time includes the information issued last time, that is, the historical trajectory of the robot, so that the historical trajectory of the robot can be traced, and the robot can be prevented from waiting at a certain path point, so that the robot can run smoothly.

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

[0049] Taking the robot from path point 1 to path point 10 as an example, two trajectory points are issued each time, and the path issuing is completed in five times. The issuing manner is as follows:

[0050] First issuance: path points are path point 1, path point 2, and destination point 10;

[0051] Second issuance: trajectory information is path point 1, path point 2, trajectory point 3, trajectory point 4, and destination point 10;

[0052] Third time: trajectory information is waypoint 1, waypoint 2, trajectory point 3, trajectory point 4, path point 5, waypoint 6, destination point 10;

[0053] Fourth time: trajectory information is waypoint 1, waypoint 2, trajectory point 3, trajectory point 4, waypoint 5, waypoint 6, waypoint 7, path point 8, destination point 10;

[0054] Fifth time: 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, destination point 10.

[0055] The waypoints are related work points, and the trajectory points are path points.

[0056] Since the path points are sparse trajectory points in the road network, the robot end interpolates and encrypts the sparse trajectory. For example, the robot receives two path points (from path point 1 to path point 2), and the robot end adds some transition points between path point 1 and path point 2, so that the robot can reach.

[0057] Further, the robot can be made to wait and re-plan by pausing the delivery and stopping in order.

[0058] The train inspection robot path planning device of the embodiment comprises:

[0059] A road network construction module is configured to construct a road network map of a target work area of a controlled inspection robot, wherein the road network map comprises all path point information of the robot, path information between each path point, and a weight value of each path information, and the weight value is calculated according to a distance between two path points in the corresponding path and a preset generation value;

[0060] A path planning module is configured to generate an adjacency matrix according to all path point information, path information, and weight values on each path in the road network map; when the controlled inspection robot executes a task, the start point and the end point of the current task are obtained, and the optimal path from the start point to the end point is calculated by traversing the adjacency matrix according to the obtained start point and end point;

[0061] A path delivery module is configured to send 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 the embodiment corresponds to the train inspection robot path planning described above, wherein the road network construction module corresponds to step S1 to construct a road network, the path planning module respectively executes step S2 to generate an adjacency matrix and step S3 to plan a path, and the path delivery module corresponds to step S4 to deliver a path. Details are not described one by one.

[0063] Example 2:

[0064] In inspection robot systems, multiple inspection robots typically operate simultaneously, requiring collaborative work between each robot and other robots, pedestrians, trains, etc. Collisions and deadlocks can occur during multi-robot collaborative operations. This embodiment is essentially the same as Embodiment 1, except that it also includes traffic control for each inspection robot. This includes monitoring whether the controlled inspection robot enters a locked area during its movement. If so, the locked area is blocked to prevent other robots from entering until the controlled inspection robot passes through. Once the controlled inspection robot leaves the locked area, the locked area is unlocked. The locked area is a region centered on a traffic control point, which is a path point simultaneously connected to more than two path points. This method effectively reduces robot deadlocks and avoids robot collisions, thus enabling multiple robots to work collaboratively within 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 is sealed off, and other robots cannot enter the closed area and must wait outside the area. When the robot leaves the closed area, the closed area is unlocked, and the robots that were 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 connecting the traffic control points and the connecting points is used as the radius of the closed area, so that the closed area is formed by the traffic control points and the connecting points. The connecting points are the path points that connect to the traffic control points.

[0067] by Figure 3 For example, a road network map describes the connectivity of each point using points and edges. A point that is simultaneously connected to more than two other points is considered a traffic control point. For instance, path point 5 in the diagram can connect to points 2, 3, 4, 6, and 8. Therefore, node 5 is designated as a traffic control point, and a closed area is formed with the radius of two path points. That is, node 5, along with nodes 2, 3, 4, 6, and 8, constitutes a closed area. Once a robot enters this closed area, the area is blocked, preventing other robots from entering. They must wait at points outside the area, thus ensuring the safe passage of robots through the control point.

[0068] This embodiment of the train inspection robot scheduling and control device, based on the train inspection robot scheduling and control device of Embodiment 1, further includes a scheduling and control module. This module monitors whether the controlled inspection robot enters a locked area during its operation. If so, it blocks the locked area to prevent other robots from entering until the controlled inspection robot passes through. Once the controlled inspection robot leaves the locked area, it unlocks the area. The locked area is a region centered on a traffic control point, which is a path point simultaneously connected to more than two path points. By setting up the scheduling and control module, robot deadlock is effectively reduced, robot collisions are avoided, and multiple robots can work collaboratively within the same area.

[0069] Example 3:

[0070] This embodiment describes the implementation of train inspection robot scheduling and control using embodiments 1 and 2 in a specific application scenario. Figure 4 As shown, the specific implementation process includes:

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

[0072] Step 2: Road network loading: Load the entire road network data in the scheduling system, generate 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: Based on the obtained starting and ending points of the task, complete the path calculation and planning as described in Example 1;

[0075] Step 5: Path Distribution: After planning is completed, incrementally distribute paths to the robot;

[0076] Step 6: Traffic Control: During the execution of the task, the robot monitors whether any inspection robots enter the locked area, as described in Example 2. If so, the locked area is sealed off to prevent other robots from entering, until the inspection robot leaves the locked area and the locked area is unlocked.

[0077] Through the above steps, the embodiment can not only accurately plan the path of the robot according to the work points and the work paths of the whole work area, effectively save the work points and the path information of the robot in the whole area, but also support the collaborative work of multiple robots in the same area, realize the work control of multiple robots in multiple floors and in different areas, and reduce the robot deadlock and avoid the collision between the robots.

[0078] The application further includes an electronic device including a processor and a memory, the memory being configured to store a computer program, and the processor being configured to execute the computer program to perform the method of the above embodiment of the application.

[0079] It can be understood that the method of the application can be executed by a single device, such as a computer or a server, and can also be applied to a distributed scenario in which multiple devices cooperate to complete the method. In the distributed scenario, one of the multiple devices can only execute one or more steps of the above method, and the 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, and is configured to execute a related program to implement the above method of the 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, and when the above method of the embodiment is implemented by software or firmware, the related program code is stored in the memory and executed by the processor.

[0080] The application further includes a computer-readable storage medium storing a computer program, which is executed to implement the method of the above embodiment of the application.

[0081] Those skilled in the art will appreciate that the embodiments of the present application described above can be provided as a method, system, or computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-readable storage media (including, but not limited to, disk memory, CD-ROMs, optical storage devices, etc.) embodying computer readable code thereon. The present application is described in terms of flowcharts and / or block diagrams in which each block can represent a program instruction, a piece of code, or a combination of program instructions and / or code. These computer program instructions, when executed by a computer or other programmable data processing apparatus, cause the computer or other programmable data processing apparatus to perform a process, e.g., a method as described in the flowcharts and / or block diagrams. The computer program instructions, when executed by a computer or other programmable data processing apparatus, cause the computer or other programmable data processing apparatus to produce a machine, e.g., that presents a graphical user interface, as described in the flowcharts and / or block diagrams. Figure 1 one or more functions specified in the flow or flows and / or block or blocks. Figure 1 one or more functions specified in the flow or flows and / or block or blocks. Figure 1 one or more functions specified in the flow or flows and / or block or blocks. Figure 1 one or more functions specified in the flow or flows and / or block or blocks. Figure 1 one or more functions specified in the flow or flows and / or block or blocks. Figure 1 one or more functions specified in the flow or flows and / or block or blocks.

[0082] The foregoing is merely illustrative of the principles of the application, and various modifications can be made by those skilled in the art without departing from the scope of the application. The above description is embodied in the best mode presently contemplated by the inventors. The application, however, is capable of other embodiments and of being practiced and carried out in various ways. Accordingly, the application is not intended to be limited to the embodiments described above but is to be accorded the widest scope consistent with the principles and features disclosed herein.

Claims

1. A train inspection robot dispatching control method, characterized by the steps of The application relates to a method for controlling a robot to patrol and inspect a target work area. The method comprises the following steps: constructing a road network map of the target work area of the controlled robot, wherein the road network map comprises all path point information of the robot, path information between the path points, and weight values of the path information, the weight values being calculated according to distances between two path points in the corresponding path and preset cost values; generating an adjacency matrix according to all the path point information, the path information and the weight values on the paths in the road network map; when the controlled robot executes a task, obtaining a starting point and an ending point of the task, and calculating an optimal path from the starting point to the ending point by traversing the adjacency matrix according to the obtained starting point and ending point; sending the information of the optimal path to the controlled robot to control the driving path of the robot; 2. The train inspection robot dispatching control method according to claim 1, wherein the method further comprises the following steps:

3. The method of claim 1, wherein, during the driving process of the controlled robot, monitoring whether the controlled robot enters a closed area, if yes, locking the closed area to shield other robots from entering the current closed area, until the controlled robot passes through the closed area, and unlocking the closed area after the controlled robot drives out of the closed area, wherein the closed area is an area formed by a traffic control point as a center, and the traffic control point is a path point simultaneously connected with more than two path points.

4. The method of claim 1, wherein, The path point information comprises position information and path point type information, the position information comprises coordinate information of the map, a robot bias angle, height information of the robot and any one or more of the work area where the robot is located, and the path point type comprises a work point type and a non-work point trajectory point type.

5. The method of claim 1, wherein, The path information comprises distances between two path points, preset cost coefficients between the two path points, and any one or more of a path serial number, a starting path point of the path and an ending path point of the path.

6. The method of claim 1-5, wherein, In the step of calculating the optimal path from the starting point to the ending point by traversing the adjacency matrix according to the obtained starting point and ending point, an A* algorithm is used to calculate the optimal path from the starting point to the ending point, and a heuristic function uses a Euclidean distance from a current node to a destination node and a cost value from the current node to the destination node in the calculation process.

7. The method of claim 6, wherein, Each element in the adjacency matrix corresponds to a distance from a current node to another node and a weight value.

8. The method of claim 1, wherein, When the information of the optimal path is sent to the controlled robot, path point information is sent in an incremental manner for multiple times until all the path point information from the starting point to the destination point is finally sent.

9. A train inspection robot dispatching control device characterized by comprising: The path point information is sent in the incremental manner for multiple times, including sending the path point information of the starting point and the destination point for the first time, and sending path point information including the path point information sent last time and a specified number of new path point information for each time from the second time. The radius of the closed area is a line between the traffic control point and each connection point, and the closed area is formed by the traffic control point and the connection points. The application relates to a method for controlling a robot to patrol and inspect a target work area. The road network construction module is configured to construct a road network map of a target operation area of the controlled inspection robot, wherein the road network map comprises all path point information of the robot, path information between each path point, and a weight value of each path information, and the weight value is calculated according to a distance between two path points in a corresponding path and a preset generation value. The path planning module is configured to generate an adjacency matrix according to all path point information, path information, and the weight value on each path in the road network map. When the controlled inspection robot performs a task, a starting point and an ending point of the current task are obtained, and an optimal path from the starting point to the ending point is calculated according to the obtained starting point and ending point by traversing the adjacency matrix. The path issuing module is configured to send information of the optimal path to the controlled inspection robot to control a driving path of the inspection robot. The scheduling control module is further configured to monitor whether the controlled inspection robot enters a closed area during driving, and if so, to lock the closed area to shield other robots from entering the current closed area until the controlled inspection robot passes through the closed area, and to unlock the closed area after the controlled inspection robot drives away from the closed area, wherein the closed area is an area formed around a traffic control point, and the traffic control point is a path point that is simultaneously connected to more than two path points.

10. An electronic device comprising a processor and a memory for storing a computer program, characterized in that The processor is configured to execute the computer program to perform the method of any one of claims 1-8.

11. A computer readable storage medium storing a computer program, characterized in that, The computer program is configured to perform the method of any one of claims 1-8 when executed.

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