A robot path optimization method for cable tunnels

Abstract

The invention discloses a robot path optimization method for a cable tunnel, and relates to the technical field of robot path optimization. The method updates the actual distance from each intermediate node to the starting node, obtains the intermediate node adjacent to the starting node, and the intermediate node adjacent to the starting node may be one or more, adds all the intermediate nodes adjacent to the starting node to an open list, selects the intermediate node with the smallest heuristic assignment in the open list as the current node, repeats the above steps to continuously expand the current node, and jumps out of iteration after finding the target node, completes the visited marking of all intermediate nodes, and traverses backward from the target node along the parent node pointer of each node until returning to the starting node to obtain the shortest path from the starting node to the target node. The optimization method automatically searches for the global optimal solution of the cable tunnel, which not only effectively improves the efficiency and reliability of path planning, but also increases the accuracy of path planning.

Description

Technical Field

[0001] The present invention relates to the technical field of robot path optimization, and in particular to a robot path optimization method for a cable tunnel. Background Art

[0002] With the acceleration of urbanization, the construction of infrastructure such as electricity, communications, and networks is becoming increasingly important. Cable tunnels, as an important part of these infrastructures, need to be regularly inspected and maintained to ensure their normal operation and reliability. Cable tunnel robots are intelligent robot systems used to detect, maintain, and repair cable tunnels. These robots are usually designed to be able to move autonomously in narrow and complex tunnel environments and perform various tasks, including checking the status of cables, locating faults, performing maintenance and repairs, etc.

[0003] The prior art has the following deficiencies:

[0004] 1. Existing optimization methods tend to find local optimal solutions rather than global optimal solutions, which may cause the robot to choose a suboptimal path, affecting the efficiency and reliability of path planning;

[0005] 2. In the cable tunnel network, there are a large number of nodes and edges. The existing optimization methods are difficult to adapt to the complexity and diversity of the cable tunnel network, which affects the accuracy of path planning. Summary of the invention

[0006] The object of the present invention is to provide a robot path optimization method for a cable tunnel to solve the shortcomings of the background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a robot path optimization method for a cable tunnel, the optimization method comprising the following steps:

[0008] S1: The optimization system obtains the starting node, intermediate node and target node in the cable tunnel through the cable tunnel management system, uses the numbering tool to number each node, adds the starting node to the open list and marks it as visited, and sets the heuristic value of the starting node to 0;

[0009] S2: After filtering out the intermediate nodes that do not support robot passage through the node screening algorithm, determine whether the remaining intermediate nodes support robot inspection based on the association relationship between the nodes. If they do not support inspection, a warning signal is issued. If they support inspection, the remaining intermediate nodes are traversed;

[0010] S3: Update the actual distance from each intermediate node to the starting node, obtain the intermediate nodes adjacent to the starting node, add all the intermediate nodes adjacent to the starting node to the open list, and select the intermediate node with the smallest heuristic assignment in the open list as the current node;

[0011] S4: Repeat step S3 to continuously expand the current node. When the target node is found, the iteration is terminated, and the visited marking of all intermediate nodes is completed and an open list is generated.

[0012] S5: In the open list, traverse backwards from the target node along the parent node pointer of each node until returning to the starting node to obtain the shortest path from the starting node to the target node. The optimization system outputs the shortest path from the starting node to the target node and the shortest path distance, and sends the node number information in the shortest path to the robot.

[0013] In a preferred embodiment, in step S2, the intermediate nodes that do not support robot passage are screened out by a node screening algorithm, including the following steps:

[0014] S2.1: Before the robot patrols, the real-time node information is obtained through the sensor equipment set at the node. After analyzing the node information through the node screening algorithm, the node index is output for the node. The function expression is:

[0015]

[0016] ; In the formula, jdx is the node index, T(t) is the change in the node's traffic rate, za is the static obstacle index, [tx, ty] is the period of dynamic obstacle warning, and [ti, tj] is the period of temperature and humidity warning;

[0017] S2.2: The larger the node index is, the less favorable the node is for the robot to pass through. After obtaining the node index, the node index is compared with the pass threshold. The pass threshold is used to determine whether the node needs to be screened out.

[0018] In a preferred embodiment, in step S2.2, after obtaining the node index, the node index is compared with the pass threshold, and the pass threshold is used to determine whether the node needs to be screened out, including the following steps:

[0019] If the node index of the node calculated by the node screening algorithm is less than or equal to the pass threshold, the node is judged to support robot passage and the node is retained;

[0020] If the node index of the node calculated by the node screening algorithm is greater than the pass threshold, it is determined that the node does not support robot passage and the node is screened out.

[0021] In a preferred embodiment, the logic for obtaining the time period of the dynamic obstacle warning is as follows: when a dynamic obstacle appears in the node area through a camera at the node, the existence time of the dynamic obstacle is recorded, and the time period when the existence time of the dynamic obstacle exceeds the time threshold is used as the dynamic obstacle warning time period;

[0022] The logic for obtaining the time period for temperature and humidity warning is as follows: the temperature and humidity at the node are monitored in real time by the temperature and humidity sensors set at the node, and the time period when the temperature exceeds the temperature threshold or the humidity exceeds the humidity threshold is used as the time period for temperature and humidity warning.

[0023] In a preferred embodiment, in step S3, updating the actual distance from each intermediate node to the starting node includes the following steps:

[0024] S3.1: For the currently traversed intermediate node, calculate the actual distance from the start node to the intermediate node by accumulating the calculated path distance and the path weight from the current intermediate node to the target node. If the calculated path distance is d and the path weight from the current node to the start node is w, the actual distance is d+w.

[0025] S3.2: If it is the first time to visit the intermediate node, the actual distance is set to the current path length. If the node has been visited before, the current path length is compared with the existing path length, and the smaller path length is selected as the actual distance.

[0026] S3.3: According to the calculated actual distance, update the actual distance information of the intermediate node, continue to traverse the remaining intermediate nodes, and repeat steps S3.1 and S3.2 until all the intermediate nodes are traversed.

[0027] In a preferred embodiment, in step S3, the intermediate nodes adjacent to the start node are obtained, all the intermediate nodes adjacent to the start node are added to the open list, and the intermediate node with the smallest heuristic assignment in the open list is selected as the current node, including the following steps:

[0028] S3.3: Obtain all intermediate nodes from the neighbors of the starting node, where the intermediate nodes in the neighbors are directly connected to the starting node, and the intermediate nodes in the neighbors include one or more;

[0029] S3.4: Add all the obtained intermediate nodes to the open list, and the intermediate nodes in the open list are candidate nodes for path search;

[0030] S3.5: Select an intermediate node with the minimum heuristic value from the open list as the current node, add the selected current node to the open list, and generate a visited mark for the current node;

[0031] S3.6: Repeat steps S3.4 and S3.5 until the target node is found.

[0032] In a preferred embodiment, in step S5, traversing backward from the target node along the parent node pointer of each node in the open list until returning to the start node to obtain the shortest path from the start node to the target node includes the following steps:

[0033] S5.1: Set the target node as the current node, start from the current node, traverse backwards along the parent node pointer, find the parent node of the current node, the parent node of the parent node, until returning to the starting node;

[0034] S5.2: During the traversal process, record each node passed through, which constitutes the shortest path from the starting node to the target node. When traversing back to the starting node, the reverse traversal is completed;

[0035] S5.3: Arrange the recorded nodes in reverse order to obtain the shortest path from the start node to the target node.

[0036] A robot path optimization system for a cable tunnel, comprising a node acquisition module, an access module, a node screening module, a judgment module, a distance update module, a node update module, an expansion module, a backtracking module, and an output module;

[0037] Node acquisition module: obtain the starting node, intermediate node and target node in the cable tunnel through the cable tunnel management system, and use the numbering tool to number and mark each node;

[0038] Visit module: add the starting node to the open list and mark it as visited, and set the heuristic value of the starting node to 0;

[0039] Node screening module: The node screening algorithm is used to screen out intermediate nodes that do not support robot passage;

[0040] Judgment module: Based on the association relationship between nodes, it determines whether the remaining intermediate nodes support robot inspection. If not, a warning signal is issued;

[0041] Distance update module: If inspection is supported, traverse the remaining intermediate nodes and update the actual distance from each intermediate node to the starting node;

[0042] Node update module: add all intermediate nodes adjacent to the start node to the open list, and select the intermediate node with the smallest heuristic assignment in the open list as the current node;

[0043] Extension module: Repeat the steps of the node update module to continuously expand the current node. When the target node is found, the iteration is exited to complete the visited marking of all intermediate nodes.

[0044] Backtracking module: In the open list, traverse from the target node along the parent node pointer of each node in reverse order until returning to the start node to obtain the shortest path from the start node to the target node;

[0045] Output module: Outputs the shortest path and shortest path distance from the starting node to the target node, and sends the node number information in the shortest path to the robot.

[0046] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0047] 1. The present invention traverses the remaining intermediate nodes, updates the actual distance from each intermediate node to the starting node, obtains the intermediate nodes adjacent to the starting node, and the intermediate nodes adjacent to the starting node may be one or more, adds all the intermediate nodes adjacent to the starting node to the open list, selects the intermediate node with the smallest heuristic assignment in the open list as the current node, repeats the above steps to continuously expand the current node, jumps out of iteration when the target node is found, completes the visited marking of all intermediate nodes, and traverses backward from the target node along the parent node pointer of each node until the shortest path from the starting node to the target node is obtained after returning to the starting node. The optimization method automatically finds the global optimal solution of the cable tunnel, which not only effectively improves the efficiency and reliability of path planning, but also increases the accuracy of path planning.

[0048] 2. After the present invention uses a node screening algorithm to screen out intermediate nodes that do not support robot passage, it determines whether the remaining intermediate nodes support robot inspection based on the association relationship between the nodes. If inspection is not supported, a warning signal is issued. After receiving the warning signal, the administrator needs to manage the robot path accordingly. This optimization method can dynamically perform path planning when some nodes in the cable tunnel do not support robot passage, further improving the adaptability of the optimization system and the security of path planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0050] Figure 1 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] Example: See Figure 1 As shown, the robot path optimization method for a cable tunnel described in this embodiment includes the following steps:

[0053] S1: The optimization system obtains the starting node, intermediate node and target node in the cable tunnel through the cable tunnel management system, uses the numbering tool to number each node, adds the starting node to the open list and marks it as visited, and sets the heuristic value of the starting node to 0;

[0054] S2: After filtering out the intermediate nodes that do not support robot passage through the node screening algorithm, determine whether the remaining intermediate nodes support robot inspection based on the association relationship between the nodes. If they do not support inspection, a warning signal is issued. If they support inspection, the remaining intermediate nodes are traversed;

[0055] S3: Update the actual distance from each intermediate node to the starting node, obtain the intermediate nodes adjacent to the starting node, add all the intermediate nodes adjacent to the starting node to the open list, and select the intermediate node with the smallest heuristic assignment in the open list as the current node;

[0056] S4: Repeat step S3 to continuously expand the current node. When the target node is found, the iteration is terminated, and the visited marking of all intermediate nodes is completed and an open list is generated.

[0057] S5: In the open list, traverse backwards from the target node along the parent node pointer of each node until returning to the starting node to obtain the shortest path from the starting node to the target node. The optimization system outputs the shortest path from the starting node to the target node and the shortest path distance, and sends the node number information in the shortest path to the robot.

[0058] The optimization system obtains the starting node, intermediate node and target node in the cable tunnel through the cable tunnel management system, uses the numbering tool to mark each node, adds the starting node to the open list and marks it as visited, and sets the heuristic assignment of the starting node to 0. The heuristic assignment is the estimated distance from the starting node to the target node. After the intermediate nodes that do not support robot passage are screened out through the node screening algorithm, it is determined whether the remaining intermediate nodes support robot inspection based on the association relationship between the nodes. If they do not support inspection, a warning signal is issued. After receiving the warning signal, the administrator needs to manage the robot path accordingly. If they support inspection, the remaining intermediate nodes are traversed, the actual distance from each intermediate node to the starting node is updated, and the intermediate nodes adjacent to the starting node are obtained. There may be one or more intermediate nodes adjacent to the starting node. All intermediate nodes adjacent to the starting node are added to the open list. The intermediate node with the smallest heuristic assignment in the open list is selected as the current node. The above steps are repeated to continuously expand the current node. When the target node is found, the iteration is jumped out, the visited marking of all intermediate nodes is completed and an open list is generated. In the open list, the parent node pointer of each node is traversed backward from the target node until the shortest path from the starting node to the target node is obtained after returning to the starting node. The optimization system outputs the shortest path and the shortest path distance from the starting node to the target node, and sends the node number information in the shortest path to the robot. The robot inspects the cable tunnel based on the shortest path and node number information.

[0059] The present application traverses the remaining intermediate nodes, updates the actual distance from each intermediate node to the starting node, obtains the intermediate nodes adjacent to the starting node, and the intermediate nodes adjacent to the starting node may be one or more, adds all the intermediate nodes adjacent to the starting node to the open list, selects the intermediate node with the smallest heuristic assignment in the open list as the current node, repeats the above steps to continuously expand the current node, jumps out of the iteration when the target node is found, completes the visited marking of all intermediate nodes, and traverses backward from the target node along the parent node pointer of each node until the shortest path from the starting node to the target node is obtained after returning to the starting node. The optimization method automatically finds the global optimal solution for the cable tunnel, which not only effectively improves the efficiency and reliability of path planning, but also increases the accuracy of path planning.

[0060] This application uses a node screening algorithm to screen out intermediate nodes that do not support robot passage, and then determines whether the remaining intermediate nodes support robot inspection based on the association relationship between the nodes. If inspection is not supported, a warning signal is issued. After receiving the warning signal, the administrator needs to manage the robot path accordingly. This optimization method can dynamically plan paths when some nodes in the cable tunnel do not support robot passage, further improving the adaptability of the optimization system and the security of path planning.

[0061] The optimization system includes a node acquisition module, an access module, a node screening module, a judgment module, a distance update module, a node update module, an expansion module, a backtracking module, and an output module;

[0062] Node acquisition module: obtains the starting node, intermediate node and target node in the cable tunnel through the cable tunnel management system, uses the numbering tool to number each node, and sends the node information to the access module and the output module;

[0063] Visit module: After adding the start node to the open list, mark it as visited, and set the heuristic value of the start node to 0. The heuristic value is the estimated distance from the start node to the target node, and the heuristic value is sent to the node update module;

[0064] Node screening module: The intermediate nodes that do not support the robot's passage are screened out through the node screening algorithm, and the screening information of the intermediate nodes is sent to the judgment module;

[0065] Judgment module: Based on the association relationship between nodes, it determines whether the remaining intermediate nodes support robot inspection. If not, a warning signal is issued. After receiving the warning signal, the administrator needs to manage the robot path accordingly. The judgment result is sent to the example update module;

[0066] Distance update module: If inspection is supported, the remaining intermediate nodes are traversed, the actual distance from each intermediate node to the start node is updated, and the actual distance information is sent to the node update module;

[0067] Node update module: obtain the intermediate nodes adjacent to the start node. There may be one or more intermediate nodes adjacent to the start node. Add all the intermediate nodes adjacent to the start node to the open list, select the intermediate node with the smallest heuristic assignment in the open list as the current node, and send the current node information to the extension module;

[0068] Extension module: Repeat the steps of the node update module to continuously expand the current node. When the target node is found, it will jump out of the iteration, complete the visited marking of all intermediate nodes, and send the open list to the backtracking module.

[0069] Backtracking module: In the open list, traverse backwards from the target node along the parent node pointer of each node until returning to the starting node to obtain the shortest path from the starting node to the target node. The shortest path information is sent to the output module;

[0070] Output module: outputs the shortest path and shortest path distance from the starting node to the target node, and sends the node number information in the shortest path to the robot. The robot inspects the cable tunnel based on the shortest path and node number information.

[0071] The optimization system obtains the starting node, intermediate node and target node in the cable tunnel through the cable tunnel management system, and uses a numbering tool to number and mark each node, including the following steps:

[0072] Obtain node information: Obtain information about the starting node, intermediate node, and target node from the cable tunnel management system, including their locations, connection relationships, etc.

[0073] Node number: Assign a unique number to each node, usually an integer or other unique identifier to represent the node. These numbers can be indexes used internally by the system to identify and operate on nodes in path planning algorithms.

[0074] Starting node marking: Determine the starting node and mark its number with a special value, such as 0 or other predefined identifiers, to indicate its starting position in path planning.

[0075] Destination node marking: The destination node is identified and its number is marked with a special value, such as a unique value, so that it can be easily identified in the path planning algorithm.

[0076] Intermediate node numbering: Intermediate nodes are numbered according to certain rules based on their location in the tunnel or other characteristics. These numbers are usually consecutive integers so that they can be easily processed and identified in path planning.

[0077] Save node information: Save the node number information in the system so that the subsequent path planning algorithm can use it. This information may include the mapping relationship between node number and node position, the connection relationship between nodes, etc.

[0078] Assume that there is a cable tunnel system, which includes the starting node S, intermediate nodes A, B, C and target node G, and their connection relationship is as follows:

[0079] S->A;

[0080] S->B;

[0081] A->C;

[0082] B->C;

[0083] C->G;

[0084] Now you need to use the numbering tool to number these nodes and identify the starting node and the target node.

[0085] Assign a unique number to each node. Assuming integers are used to represent nodes, node S can be numbered 0, node A can be numbered 1, node B can be numbered 2, node C can be numbered 3, and node G can be numbered 4.

[0086] According to the location or other characteristics of the node, the intermediate nodes are numbered. In this example, node A is marked as 1, node B is marked as 2, and node C is marked as 3.

[0087] Finally, we get the number label of each node:

[0088] Starting node S: numbered 0;

[0089] Intermediate node A: numbered 1;

[0090] Intermediate node B: numbered 2;

[0091] Intermediate node C: numbered 3;

[0092] Target node G: numbered 4.

[0093] After adding the start node to the open list, mark it as visited, and set the heuristic value of the start node to 0. The heuristic value is the estimated distance from the start node to the target node, including the following steps:

[0094] Add the start node to the open list: Add the start node to the open list, which is the starting point of the path search.

[0095] Mark the starting node as visited: Mark the starting node as visited to ensure that the node is not visited repeatedly.

[0096] Set the heuristic value: Set the heuristic value of the starting node to 0. The heuristic value usually represents the estimated distance from the current node to the target node. The heuristic value is used to guide the search direction. The closer the node is to the target node, the smaller the heuristic value should be.

[0097] Estimate the distance from the starting node to the target node: Estimate the distance from the starting node to the target node based on the specific problem and heuristic function, including straight-line distance, Manhattan distance, Euclidean distance, etc.

[0098] Set heuristic assignment: Use the estimated distance from the start node to the target node as the heuristic assignment.

[0099] The node screening algorithm is used to screen out intermediate nodes that do not support robot passage, including the following steps:

[0100] Before the robot patrols, the real-time node information is obtained through the sensor equipment set at the node. After analyzing the node information through the node screening algorithm, the node index is output for the node. The function expression is:

[0101]

[0102] ; In the formula, jdx is the node index, T(t) is the change in the node's traffic rate, za is the static obstacle index, [tx, ty] is the period of dynamic obstacle warning, and [ti, tj] is the period of temperature and humidity warning;

[0103] The larger the node index is, the more unfavorable the node is for the robot to pass through. After obtaining the node index, the node index is compared with the pass threshold, and the pass threshold is used to determine whether the node needs to be screened out.

[0104] If the node index of the node calculated by the node screening algorithm is less than or equal to the pass threshold, the node is judged to support robot passage and the node is retained;

[0105] If the node index of the node calculated by the node screening algorithm is greater than the pass threshold, it is determined that the node does not support robot passage and the node is screened out.

[0106] The calculation logic of the static obstacle index is: when the camera set at the node captures the existence of an obstacle in the node area, the image processing technology is used to identify the minimum distance between the edge of the obstacle and the channel. If the minimum distance between the edge of the obstacle and the channel is greater than or equal to the distance threshold (the distance threshold is equal to the maximum width of the robot), the static obstacle index zastatic=1; if the minimum distance between the edge of the obstacle and the channel is less than the distance threshold (the distance threshold is equal to the maximum width of the robot), the static obstacle index zastatic=5, indicating that the node cannot support the robot's passage.

[0107] The logic for obtaining the time period for dynamic obstacle warning is as follows: dynamic obstacles include animals appearing in cable tunnels, etc. Dynamic obstacles are uncertain, but they will affect the normal passage of the robot. Therefore, when a dynamic obstacle appears in the node area through the camera at the node (the infrared camera can be used to determine whether the obstacle is alive or dead), the duration of the dynamic obstacle is recorded, and the time period when the dynamic obstacle exists for more than the duration threshold is used as the time period for dynamic obstacle warning. The longer the dynamic obstacle exists, the more unfavorable it is for the robot to pass.

[0108] The logic for obtaining the temperature and humidity warning period is as follows: Since excessive temperature or humidity will affect the stable operation of the robot, the temperature and humidity at the node are monitored in real time through the temperature and humidity sensors set at the node, and the period when the temperature exceeds the temperature threshold or the humidity exceeds the humidity threshold is used as the temperature and humidity warning period. The longer the temperature and humidity warning period is, the more likely it is to have the following impacts on the operation of the robot:

[0109] Temperature changes may affect the operational stability of the robot. For example, at high temperatures, the robot's electronic components may malfunction or degrade, causing the robot to be unable to move or perform tasks normally.

[0110] A high humidity environment may have an adverse effect on the robot's working environment. For example, the robot's sensors may be affected by humidity and cause misoperation or reduced accuracy.

[0111] Based on the association relationship between the nodes, it is determined whether the remaining intermediate nodes support robot inspection. If they do not support inspection, a warning signal is issued. After receiving the warning signal, the administrator needs to manage the robot path accordingly, including the following steps:

[0112] Determine whether the remaining intermediate nodes support robot inspection: For each intermediate node, check the status of its adjacent nodes to determine whether there is a passable path. If all adjacent nodes of the current node do not support robot inspection, the current node is considered not to support robot inspection.

[0113] Send out a warning signal: When it is found that the current node does not support robot inspection, the system should immediately send out a warning signal to notify the administrator that there is a path interruption or unavailability.

[0114] What the administrator does after receiving the warning signal: After receiving the warning signal, the administrator needs to manage the robot path accordingly to ensure that the robot can continue to patrol to the target node. The main processing method is to re-plan the path to ensure that there is at least one neighboring node that supports it, otherwise the robot cannot patrol to the target node.

[0115] If inspection is supported, the remaining intermediate nodes are traversed to update the actual distance from each intermediate node to the starting node, including the following steps:

[0116] Traverse the remaining intermediate nodes: Traverse the intermediate nodes that have not been visited, that is, those nodes that have not been marked as visited.

[0117] Calculate the actual distance: For the currently traversed intermediate node, calculate the actual distance from the start node to the intermediate node. This actual distance is usually calculated based on the known path weight and the calculated path distance. If this is the first time to visit the intermediate node, the actual distance can be set to the current path length; if the node has been visited, compare the current path length with the existing path length and select the shorter path length as the actual distance.

[0118] Cumulative path weight: If the weight of the path is known and non-negative (e.g., path length, time, etc.), the actual distance can be obtained by accumulating the calculated path distance and the path weight from the current node to the target node. For example, if the calculated path distance is d and the path weight from the current node to the starting node is w, the actual distance is d+w.

[0119] Compare path weights: If there are multiple paths to the same intermediate node, you can compare the calculated path distance and the new path distance, and select the shortest path as the actual distance. This ensures that the actual distance recorded is always the shortest path from the starting node to each intermediate node.

[0120] Update actual distance: Update the actual distance information of the intermediate nodes based on the calculated actual distance. This ensures that the shortest path from the starting node to each intermediate node is always recorded during the path search process.

[0121] Continue traversal: Continue traversing the remaining intermediate nodes and repeat the above steps until all intermediate nodes have been traversed.

[0122] Get the intermediate nodes adjacent to the start node. There may be one or more intermediate nodes adjacent to the start node. Add all the intermediate nodes adjacent to the start node to the open list, and select the intermediate node with the smallest heuristic assignment in the open list as the current node, including the following steps:

[0123] Get adjacent intermediate nodes: Get all intermediate nodes from the neighbors of the starting node. These nodes are intermediate nodes directly connected to the starting node, and they may be one or more.

[0124] Add to open list: Add all the intermediate nodes obtained to the open list. These nodes will become candidate nodes for path search.

[0125] Select the node with the smallest heuristic assignment: Select the middle node with the smallest heuristic assignment from the open list as the current node. The heuristic assignment is usually the estimated distance from the current node to the target node, giving priority to nodes closer to the target node.

[0126] Add current node: Add the selected current node to the open list and generate a visited mark for the current node to ensure that it will not be visited repeatedly.

[0127] Continue searching: Repeat the above steps until the target node is found. This ensures that the search continues until the target node is reached or there is no feasible path within the search range.

[0128] Based on the connection relationship between the starting node S, intermediate nodes A, B, C and target node G:

[0129] S->A;

[0130] S->B;

[0131] A->C;

[0132] B->C;

[0133] C->G;

[0134] According to the starting node S, its adjacent intermediate nodes are obtained, and the intermediate node with the smallest heuristic assignment is selected as the current node.

[0135] Get adjacent intermediate nodes: Get all intermediate nodes from the neighbors of the starting node S. In this example, the adjacent intermediate nodes of the starting node S are A and B.

[0136] Add to open list: Add intermediate nodes A and B to the open list.

[0137] Select the node with the smallest heuristic assignment: Compare the heuristic assignments of the middle nodes A and B, and select the node with the smallest heuristic assignment as the current node. Assume that the heuristic assignment is the straight-line distance from the node to the target node. Therefore, assume that the heuristic assignment is:

[0138] Heuristic assignment (A) = estimated distance (A to G)

[0139] Heuristic assignment (B) = estimated distance (B to G)

[0140] If it is assumed that the distances between A and B are equal, then the heuristic assignment (A) = the heuristic assignment (B), and one of them can be randomly selected as the current node.

[0141] If node A is selected as the current node, after generating a visited mark for node A, node A is added to the open list.

[0142] Repeat the above steps to continuously expand the current node. When the target node is found, the iteration is terminated. The visited marking of all intermediate nodes is completed and an open list is generated, including the following steps:

[0143] Expand the current node:

[0144] a. Select the node with the smallest heuristic value from the open list as the current node.

[0145] b. If the current node is the target node, the search is successful and the iteration is exited.

[0146] Update neighbor nodes:

[0147] a. Get the adjacent nodes of the current node.

[0148] b. For each adjacent node, if it has been visited, add the adjacent node to the open list and mark it as visited.

[0149] Generate an open list: List all nodes added to the open list during the search process, that is, all visited nodes, including intermediate nodes and target nodes.

[0150] In the open list, traverse backwards from the target node along the parent node pointer of each node until returning to the start node to obtain the shortest path from the start node to the target node, including the following steps:

[0151] Start from the target node: Set the target node as the current node.

[0152] Reverse traversal: Starting from the current node, traverse backwards along the parent node pointer until you get back to the starting node. This means starting from the target node, find the target node's parent node, the parent node's parent node, until you get back to the starting node.

[0153] Record path: During the traversal process, record each node passed through, which constitutes the shortest path from the start node to the target node.

[0154] Complete traversal: When backtracking to the starting node, the reverse traversal is completed.

[0155] Generate the shortest path: Arrange the recorded nodes in reverse order to obtain the shortest path from the start node to the target node.

[0156] The optimization system outputs the shortest path and the shortest path distance from the starting node to the target node, and sends the node number information in the shortest path to the robot, including the following steps:

[0157] Assume that we have performed a path search and found the shortest path from the start node S to the target node G. Now, there is an open list containing all the nodes that have been visited during the search, and each node contains a pointer to its parent node.

[0158] Start from the target node G and traverse backward until you return to the starting node S:

[0159] Start from the target node: Set the current node to the target node G.

[0160] Reverse traversal: Starting from the target node G, traverse backwards along the parent node pointer to find the parent node of the current node. Assuming that the parent node of the current node G is node C, set the current node to node C.

[0161] Record the path: record the nodes passed, that is, node G.

[0162] Continue reverse traversal: Starting from node C, traverse in reverse along the parent node pointer to find the parent node of the current node.

[0163] Assume that the parent node of the current node C is node A, set the current node to node A.

[0164] Record the path: record the nodes passed, that is, node C.

[0165] Continue reverse traversal: starting from node A, traverse in reverse along the parent node pointer to find the parent node of the current node.

[0166] Assume that the parent node of the current node A is the starting node S, and set the current node to the starting node S.

[0167] Record the path: record the nodes passed, that is, the starting node S.

[0168] Complete traversal: When backtracking to the starting node S, the reverse traversal is completed.

[0169] Generate the shortest path: Arrange the recorded nodes in reverse order to obtain the shortest path from the start node to the target node: S->A->C->G.

[0170] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0171] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0172] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A robot path optimization method for a cable tunnel, characterized in that: The optimization method comprises the following steps: S1: The optimization system obtains the starting node, intermediate node and target node in the cable tunnel through the cable tunnel management system, uses the numbering tool to number each node, adds the starting node to the open list and marks it as visited, and sets the heuristic value of the starting node to 0; S2: After filtering out the intermediate nodes that do not support robot passage through the node screening algorithm, determine whether the remaining intermediate nodes support robot inspection based on the association relationship between the nodes. If they do not support inspection, a warning signal is issued. If they support inspection, the remaining intermediate nodes are traversed; S3: Update the actual distance from each intermediate node to the starting node, obtain the intermediate nodes adjacent to the starting node, add all the intermediate nodes adjacent to the starting node to the open list, and select the intermediate node with the smallest heuristic assignment in the open list as the current node; S4: Repeat step S3 to continuously expand the current node. When the target node is found, the iteration is terminated, and the visited marking of all intermediate nodes is completed and an open list is generated. S5: In the open list, traverse backward from the target node along the parent node pointer of each node until returning to the starting node to obtain the shortest path from the starting node to the target node. The optimization system outputs the shortest path from the starting node to the target node and the shortest path distance, and sends the node number information in the shortest path to the robot; In step S2, the intermediate nodes that do not support robot passage are screened out by a node screening algorithm, including the following steps: S2.1: Before the robot patrols, the real-time node information is obtained through the sensor equipment set at the node. After analyzing the node information through the node screening algorithm, the node index is output for the node. The function expression is: Where jdx is the node index, T(t) is the change in the node's traffic rate, and za is the static obstacle index. [ tx, ty is the time period for dynamic obstacle warning, [ ti, tj ] The period for temperature and humidity warning; S2.2: The larger the node index is, the less favorable the node is for the robot to pass through. After obtaining the node index, the node index is compared with the pass threshold. The pass threshold is used to determine whether the node needs to be screened out.

2. A robot path optimization method for a cable tunnel according to claim 1, characterized in that: In step S2.2, after obtaining the node index, the node index is compared with the pass threshold. The pass threshold is used to determine whether the node needs to be screened out. The following steps are included: If the node index of the node calculated by the node screening algorithm is less than or equal to the pass threshold, the node is judged to support robot passage and the node is retained; If the node index of the node calculated by the node screening algorithm is greater than the pass threshold, it is determined that the node does not support robot passage and the node is screened out.

3. A robot path optimization method for a cable tunnel according to claim 2, characterized in that: The acquisition logic of the dynamic obstacle warning period is as follows: when a dynamic obstacle appears in the node area through the camera at the node, the existence time of the dynamic obstacle is recorded, and the period when the existence time of the dynamic obstacle exceeds the duration threshold is used as the dynamic obstacle warning period; The logic for obtaining the time period for temperature and humidity warning is as follows: the temperature and humidity at the node are monitored in real time by the temperature and humidity sensors set at the node, and the time period when the temperature exceeds the temperature threshold or the humidity exceeds the humidity threshold is used as the time period for temperature and humidity warning.

4. A robot path optimization method for a cable tunnel according to claim 3, characterized in that: In step S3, the actual distance from each intermediate node to the starting node is updated, including the following steps: S3.1: For the currently traversed intermediate node, calculate the actual distance from the start node to the intermediate node by accumulating the calculated path distance and the path weight from the current intermediate node to the target node. If the calculated path distance is d and the path weight from the current node to the start node is w, the actual distance is d+w. S3.2: If it is the first time to visit the intermediate node, the actual distance is set to the current path length. If the node has been visited before, the current path length is compared with the existing path length, and the smaller path length is selected as the actual distance. S3.3: According to the calculated actual distance, update the actual distance information of the intermediate node, continue to traverse the remaining intermediate nodes, and repeat steps S3.1 and S3.2 until all the intermediate nodes are traversed.

5. A robot path optimization method for a cable tunnel according to claim 4, characterized in that: In step S3, the intermediate nodes adjacent to the start node are obtained, all the intermediate nodes adjacent to the start node are added to the open list, and the intermediate node with the smallest heuristic assignment in the open list is selected as the current node, including the following steps: S3.3: Obtain all intermediate nodes from the neighbors of the starting node, where the intermediate nodes in the neighbors are directly connected to the starting node, and the intermediate nodes in the neighbors include one or more; S3.4: Add all the obtained intermediate nodes to the open list, and the intermediate nodes in the open list are candidate nodes for path search; S3.5: Select an intermediate node with the minimum heuristic value from the open list as the current node, add the selected current node to the open list, and generate a visited mark for the current node; S3.6: Repeat steps S3.4 and S3.5 until the target node is found.

6. A robot path optimization method for a cable tunnel according to claim 5, characterized in that: In step S5, the shortest path from the starting node to the target node is obtained by traversing the parent node pointer of each node in the open list in reverse order until returning to the starting node, including the following steps: S5.1: Set the target node as the current node, start from the current node, traverse backwards along the parent node pointer, find the parent node of the current node, the parent node of the parent node, until returning to the starting node; S5.2: During the traversal process, record each node passed through, which constitutes the shortest path from the starting node to the target node. When traversing back to the starting node, the reverse traversal is completed; S5.3: Arrange the recorded nodes in reverse order to obtain the shortest path from the start node to the target node.

7. A robot path optimization system for a cable tunnel, used to implement the optimization method according to any one of claims 1 to 6, characterized in that: It includes a node acquisition module, an access module, a node screening module, a judgment module, a distance update module, a node update module, an expansion module, a backtracking module, and an output module; Node acquisition module: obtain the starting node, intermediate node and target node in the cable tunnel through the cable tunnel management system, and use the numbering tool to number and mark each node; Visit module: add the start node to the open list and mark it as visited, and set the heuristic value of the start node to 0; Node screening module: The node screening algorithm is used to screen out intermediate nodes that do not support robot passage; Judgment module: Based on the association relationship between nodes, it determines whether the remaining intermediate nodes support robot inspection. If not, a warning signal is issued; Distance update module: If inspection is supported, traverse the remaining intermediate nodes and update the actual distance from each intermediate node to the starting node; Node update module: add all intermediate nodes adjacent to the start node to the open list, and select the intermediate node with the smallest heuristic assignment in the open list as the current node; Extension module: Repeat the steps of the node update module to continuously expand the current node. When the target node is found, the iteration is exited to complete the visited marking of all intermediate nodes. Backtracking module: In the open list, traverse from the target node along the parent node pointer of each node in reverse order until returning to the start node to obtain the shortest path from the start node to the target node; Output module: Outputs the shortest path and shortest path distance from the starting node to the target node, and sends the node number information in the shortest path to the robot.

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