Neural Network-Based Path Planning Method and Device for Inspection Robot

Abstract

The present invention provides a path planning method and device for inspection robots based on neural networks, relating to the technical field of path planning, including: searching for nodes according to the starting point and the target point, performing node collision detection and node connection during the search process to obtain an initial sampling path set; calculating the sampling probability of the nodes in the initial sampling path set, and performing iterative optimization through a neural network to obtain a sampling expansion scheme; searching for nodes for the starting point and the target point based on the sampling expansion scheme, and performing node collision detection during the search process. Among them, when colliding with an obstacle, a sampling reconstruction model is constructed, and different step size adjustments are made to the nodes obtained by searching the sampling expansion scheme according to the substation layout information, and the shortest path is selected to obtain a feasible path; the feasible path is smoothed and optimized to obtain the final inspection path. The present invention solves the problem of safety risks in the operation and maintenance management of substation equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of path planning, and more particularly, to a path planning method and device for an inspection robot based on a neural network. Background Art

[0002] In the current technical field of path planning, for the regular inspection and detection work of substation equipment, although technology is advancing rapidly, the actual operation still mainly relies on manual methods. Inspectors need to carefully check each key equipment in the substation one by one. This process is not only time-consuming and laborious, but also difficult to meet the high standards of operation and maintenance management of modern power grids. Among them, the inspection cycle is relatively long, and it is difficult to achieve real-time supervision and management during manual inspection. Coupled with possible omissions and judgment errors during manual inspection, abnormal situations that may occur during the operation of equipment are difficult to be discovered and processed in a timely manner, jointly leading to problems of safety risks in the operation and maintenance management of substation equipment.

[0003] Therefore, there is an urgent need for a path planning method and device for an inspection robot based on a neural network to solve the problem of safety risks in the operation and maintenance management of substation equipment. Summary of the Invention

[0004] The purpose of the present invention is to provide a path planning method and device for an inspection robot based on a neural network to improve the above problems. To achieve the above purpose, the technical solutions adopted by the present invention are as follows:

[0005] In a first aspect, the present application provides a path planning method for an inspection robot based on a neural network, including:

[0006] Obtain substation layout information, where the substation layout information includes a starting point and a target point;

[0007] Search for nodes according to the starting point and the target point, perform node collision detection during the search process, and connect the starting point, the target point, and the searched nodes to obtain an initial sampling path set;

[0008] Calculate the sampling probability of the nodes in the initial sampling path set, combine the collision information and time cost information obtained from the collision detection, and iteratively optimize the nodes in the initial sampling path set through a preset neural network to obtain a sampling expansion plan;

[0009] Search for nodes based on the sampling expansion plan for the starting point and the target point, and perform node collision detection during the search process. Among them, when colliding with an obstacle, construct a sampling reconstruction model, adjust the nodes obtained from the sampling expansion plan with different step lengths according to the substation layout information, and select the shortest path to obtain a feasible path;

[0010] Smoothly optimize the feasible path to obtain the final inspection path.

[0011] In a second aspect, the present application also provides an inspection robot path planning device based on a neural network, including:

[0012] An acquisition module for acquiring substation layout information, where the substation layout information includes a starting point and a target point;

[0013] A first construction module for searching for nodes according to the starting point and the target point, performing node collision detection during the search process, and connecting the starting point, the target point, and the searched nodes to obtain an initial sampling path set;

[0014] A second construction module for calculating the sampling probability of the nodes in the initial sampling path set, combining the collision information and time cost information obtained from the collision detection, and iteratively optimizing the nodes in the initial sampling path set through a preset neural network to obtain a sampling expansion scheme;

[0015] A third construction module for searching for nodes based on the sampling expansion scheme for the starting point and the target point, performing node collision detection during the search process, where when hitting an obstacle, a sampling reconstruction model is constructed, and different step size adjustments are made to the nodes obtained by searching the sampling expansion scheme according to the substation layout information and the shortest path is selected to obtain a feasible path;

[0016] A fourth construction module for smoothly optimizing the feasible path to obtain the final inspection path.

[0017] In a third aspect, the present application also provides an inspection robot path planning device based on a neural network, including:

[0018] A memory for storing a computer program;

[0019] A processor for implementing the steps of the inspection robot path planning method based on the neural network when executing the computer program.

[0020] In a fourth aspect, the present application also provides a readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned inspection robot path planning method based on the neural network are implemented.

[0021] The beneficial effects of the present invention are:

[0022] The present invention adopts a sampling expansion scheme, a sampling reconstruction model, and a step size adjustment strategy. Specifically, an initial sampling path set is obtained based on the substation layout, the sampling probability of the initial sampling path set is calculated, and the initial sampling path set is iteratively optimized through a neural network to obtain a sampling expansion scheme. The iterative optimization through the neural network is used to identify and reduce duplicate sampling areas, effectively reducing the deviation and error in the data collection process and improving the overall sampling efficiency and data quality. The sampling reconstruction model is constructed based on sampling collision detection and is used to plan an optimal path that can safely bypass these obstacles, not only enhancing the safety of the inspection path and ensuring the smooth passage of the robot. The step size adjustment strategy is used to select an appropriate step size according to the obstacles, making its moving step size more flexible and effectively avoiding obstacles. The present invention plans the inspection path by deploying a robot for substation inspection, integrating the sampling expansion scheme, the sampling reconstruction model, and the step size adjustment strategy and performing smoothing processing, thereby solving the problem of safety risks in the operation and maintenance management of substation equipment.

[0023] Other features and advantages of the present invention will be described in the subsequent specification, and part of them will become obvious from the specification, or can be understood by implementing the embodiments of the present invention. The objectives and other advantages of the present invention can be realized and obtained by the structures specifically pointed out in the written specification, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0025] Figure 1 It is a schematic flow chart of the path planning method for an inspection robot based on a neural network described in the embodiments of the present invention;

[0026] Figure 2 It is a schematic structural diagram of the path planning device for an inspection robot based on a neural network described in the embodiments of the present invention.

[0027] Reference numerals in the figure: 800, path planning device for an inspection robot based on a neural network; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface; 805, communication component. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. The components of the embodiments of the present invention usually described and illustrated in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0029] It should be noted that similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used for descriptive distinction and cannot be understood as indicating or implying relative importance.

[0030] Embodiment 1:

[0031] This embodiment provides a path planning method for an inspection robot based on a neural network.

[0032] See Figure 1 , the figure shows that this method includes steps S1 to S5, including:

[0033] S1: Obtain substation layout information, where the substation layout information includes a starting point and a target point;

[0034] S2: Search for nodes according to the starting point and the target point. During the search process, perform node collision detection, and connect the starting point, the target point, and the searched nodes to obtain an initial sampling path set;

[0035] In this step, take the starting point as the root node, search for nodes through a probabilistic target biasing strategy during the iteration process, and detect whether the searched nodes collide with obstacles through the Collision function during the search process. If there is no collision, connect the starting point, the target point, and the searched nodes. If there is a collision, discard the currently searched node and continue to search for the next node until the target point is expanded to obtain an initial sampling path set;

[0036] Among them, the Collision function is to determine whether the line connecting the node obtained by search and the node obtained by the previous search intersects with the obstacle. This step is used to quickly eliminate those obviously unreasonable or inefficient paths from numerous candidate paths, reduce the amount of data for subsequent processing, effectively narrow the selection range, and provide a basis for more refined optimization in the future.

[0037] S3: Calculate the sampling probability of the nodes in the initial sampling path set. Combining the collision information and time cost information obtained from the collision detection, iteratively optimize the nodes in the initial sampling path set through a preset neural network to obtain a sampling expansion plan.

[0038] To clarify the specific acquisition method of the sampling expansion plan, steps S3 includes S31 to S36, specifically:

[0039] S31: Obtain historical trajectory data.

[0040] S32: Perform path planning processing on the initial sampling path set based on a preset adaptive positioning algorithm, and optimize the initial sampling path set through a preset path evaluation criterion to obtain a local path.

[0041] To clarify the specific acquisition method of the local path, steps S32 includes S321 to S323, specifically:

[0042] S321: Analyze and calculate the collision information and sampling information obtained from the collision detection based on a preset adaptive positioning algorithm to obtain the global sampling point position.

[0043] In this step, the adaptive positioning algorithm uses the collision information to identify and avoid potential obstacles, ensuring that the robot can move without touching any obstacles. The adaptive positioning algorithm uses the sampling information to identify feature points in the environment, determine its position in the environment, and calculate based on the analyzed information to obtain the global sampling point position.

[0044] S322: Perform global path planning processing on the global sampling point position, and optimize the initial sampling path set through the path evaluation criterion to obtain a global path.

[0045] In this step, plan from the global sampling point position to the target point, and optimize the initial sampling path set through the constraint of the path evaluation criterion to obtain a collision-free global path.

[0046] S323: Perform local path planning processing on the global path according to a preset evaluation function to obtain a local path.

[0047] In this step, the evaluation function is:

[0048] (1);

[0049] In the above formula (1), represents the total score of the evaluation function, , , and all represent weight coefficients, represents the path cost evaluation index; represents the distance evaluation index from the starting point to the global sampling point position; represents the difference evaluation index from the global path; represents the time cost evaluation index;

[0050] Among them, the total score of the evaluation function is used to measure the quality of the local path.

[0051] S33: Make a decision on the local path according to the historical trajectory data to obtain a walking path segment;

[0052] In this step, make a decision on the local path according to the trajectory time and trajectory points in the historical trajectory data to obtain a walking path segment.

[0053] S34: Calculate the normalized probability of the walking path segment to obtain a sampling probability expression;

[0054] In this step, the sampling probability expression is:

[0055] (2);

[0056] In the above formula (2), represents the probability of selecting a feasible path segment in the section from the starting point to the target point, represents the probability, represents the feasible path segment, represents the target point, represents the starting point, and all represent parameters affecting path selection, represents the sum of the exponential scores of all possible path segments, represents the exponential function, represents the score calculated by the evaluation function for the feasible path segment, represents the scaling factor;

[0057] Among them, the sum of the exponential scores of all possible path segments is used for normalization processing.

[0058] S35: Calculate the nodes in the initial sampling path set based on the sampling probability expression, and integrate the nodes in the initial sampling path set through the collision information and the time cost information to obtain an optimized sampling path set;

[0059] S36: Iteratively optimize the nodes in the optimized sampling path set according to the neural network to obtain a sampling expansion scheme.

[0060] In this step, the neural network performs deep feature extraction on each node in the optimized sampling path set to obtain the deep features of the nodes, and iteratively adjusts each node in the optimized sampling path set based on the deep features of the nodes to obtain a sampling expansion scheme.

[0061] During the iterative adjustment process, the overall performance of the optimized sampling path set will be evaluated. Based on the evaluation results, the nodes with duplicates in the optimized sampling path set are used as the optimization objects, and the neural network is used to predict better positions or alternative paths;

[0062] Among them, the overall performance of the current path set includes multiple key indicators such as the length, smoothness, safety (i.e., the ability to avoid collisions and potential risks), and time cost of the path. The neural network is used to identify and reduce duplicate sampling areas, effectively reducing the bias and error in the data collection process, and improving the overall sampling efficiency and data quality.

[0063] S4: Search for nodes for the starting point and the target point based on the sampling expansion scheme, and perform node collision detection during the search. Among them, when colliding with an obstacle, a sampling reconstruction model is constructed, and different step size adjustments are made to the nodes obtained by searching the sampling expansion scheme according to the substation layout information, and the shortest path is selected to obtain a feasible path;

[0064] To clarify the specific way to obtain the feasible path, steps S41 to S44 are included in step S4, specifically:

[0065] S41: Search for nodes for the starting point and the target point based on the sampling expansion scheme to obtain expansion points. The expansion points are the nodes obtained by searching the sampling expansion scheme, and there are no less than two expansion points;

[0066] In this step, based on the sampling expansion scheme, nodes are searched for the starting point and the target point through a probability target biasing strategy during the iteration to obtain expansion points.

[0067] S42: Perform collision detection on each expansion point during the search. When the expansion point collides with an obstacle, the initial sampling path set needs to be reconstructed;

[0068] In this step, during the search process, the Collision function is used to detect whether the searched node collides with an obstacle. When the expansion point collides with an obstacle, the initial sampling path set needs to be reconstructed.

[0069] S43: Randomly sample and construct between the starting point and the target point to obtain a sampling reconstruction model;

[0070] To clarify the specific acquisition method of the sampling reconstruction model, steps S43 include S431 to S433, specifically:

[0071] S431: Randomly sample and construct multiple times based on the distance between the starting point and the target point to obtain a reconstructed trajectory;

[0072] To clarify the specific acquisition method of the reconstructed trajectory, steps S431 include S4311 to S43114, specifically:

[0073] S4311: Randomly sample the distance between the starting point and the target point multiple times based on a preset path planning algorithm to obtain a first random sampling point and a second random sampling point;

[0074] In this step, the first random sampling point is , and the second random sampling point is .

[0075] S4312: Calculate the probability of the first random sampling point and the second random sampling point to obtain a path selection probability;

[0076] In this step, calculate the utility values of the first random sampling point and the second random sampling point to obtain a first utility value and a second utility value, and perform an evaluation calculation based on the first utility value and the second utility value to obtain a path selection probability.

[0077] In this step, the path selection probability is:

[0078] (3);

[0079] In the above formula (3), represents the path selection probability, represents the utility value of the lower path, represents the utility value of the lower path, represents all possible paths;

[0080] Among them, the utility value of the lower path represents the first utility value, and the Utility value of the lower path Indicates the second utility value.

[0081] S4313: Perform path scoring based on the path selection probability, select the path with the highest score to obtain a matching path, where the path score is the shortest path planning, comprehensiveness of the inspection area, and real-time nature of environmental changes;

[0082] S4314: Perform priority selection on the initial sampling path set based on the matching path and the path selection probability to obtain a reconstructed trajectory.

[0083] S432: Modify the initial sampling path set according to the reconstructed trajectory, and perform constraints through the obstacle positions obtained by the collision detection to obtain a reconstructed path set;

[0084] In this step, according to the parameters and characteristics of the reconstructed trajectory, each path in the initial sampling path set is compared and analyzed one by one, the parts that do not match or conflict with the reconstructed trajectory are identified, and the modified path set is further constrained based on the obstacle positions obtained by the collision detection to obtain a reconstructed path set.

[0085] S433: Perform iterative optimization processing on the reconstructed path set according to the preset maximum number of iterations, and perform statistical analysis on the error data in the initial sampling path set to obtain a sampling reconstruction model.

[0086] In this step, the sampling reconstruction model can remove the sampling points that are far away globally, add the globally better sampling points to the path, and is used to plan the optimal path that can safely bypass these obstacles, which not only enhances the safety of the inspection path and ensures that the robot can pass smoothly.

[0087] S44: Perform asynchronous step adjustment on each of the expansion points according to the sampling reconstruction model and the substation layout information and select the shortest path to obtain a feasible path.

[0088] To clarify the specific acquisition method of the feasible path, steps S44 includes S441 to S446, specifically:

[0089] S441: Calculate according to the sampling reconstruction model, the different regional environments in the substation layout information, and the collision information to obtain the number of obstacles;

[0090] In this step, considering the dynamic obstacles on the roads in the substation layout information and the route planning in the narrow spaces in the substation, it is applicable to the daily inspection of the transmission high-voltage lines in the substation and provides the possibility for multi-robot inspection at the same time.

[0091] S442: Construct according to different range constraints of the number of obstacles and the preset number of obstacles to obtain a dynamic step size expression;

[0092] In this step, the dynamic step size expression is:

[0093] (4);

[0094] In the above formula (4), represents the dynamic step size, represents the maximum value of the step size, means that the step size increases when the number of current obstacles is small, means that the step size is moderate when the number of obstacles reaches a certain range, means that the step size decreases when the number of obstacles is large, represents the minimum value of the step size, represents the number of current obstacles, represents the preset number of obstacles;

[0095] S443: Judge each of the expansion points according to the dynamic step size expression to obtain a node judgment result;

[0096] In this step, each of the expansion points is judged according to the number of current obstacles;

[0097] Among them, the number of current obstacles is ;

[0098] When , there are no obstacles, and the step size is x;

[0099] When is greater than 0 but less than or equal to , the number of obstacles is small, and the step size is ;

[0100] When is greater than but less than or equal to , the number of obstacles is moderate, and the step size is , is the initial step size;

[0101] When is greater than but less than , the number of obstacles is large, and the step size is ;

[0102] When , there is a complete obstacle or the number of obstacles is too large within the range, and the step size is .

[0103] S444: Select different step sizes based on the node judgment results to adjust each of the expansion points, and obtain the final expansion points;

[0104] S445: Connect the starting point, the target point, and the final expansion points to obtain the final path set;

[0105] S446: Perform shortest path selection on each cumulative distance in the final path set to obtain the feasible paths.

[0106] In this step, the strategy for step size adjustment selects appropriate step sizes according to obstacles, making the movement step sizes more flexible and effectively avoiding obstacles.

[0107] S5: Smoothly optimize the feasible paths to obtain the final inspection path.

[0108] To clarify the specific acquisition method of the final inspection path, S5 in step S5 includes S51 to S53, specifically:

[0109] S51: Divide the feasible paths into multiple sub-regions to obtain path blocks, and the number of path blocks is not less than two;

[0110] S52: Construct functions for each of the path blocks according to the preset cubic spline interpolation method to obtain the piecewise interpolation function expressions;

[0111] In this step, the piecewise interpolation function expression is:

[0112] (5);

[0113] In the above formula (5), represents the th piecewise interpolation function, , , and represent the coefficients of each sub-interval, represents the independent variable and the difference between the node of the piecewise interpolation function, represents the independent variable and the square of the difference between the node of the piecewise interpolation function, represents the independent variable and the cube of the difference between the node of the piecewise interpolation function, represents the interpolation point of the th interval.

[0114] Among them, the conditions for interpolation are satisfied, that is: For all = 0, 1, ..., n; the interpolation curve is smooth, that is and its derivative and are continuous.

[0115] S53: Based on the piecewise interpolation function expression, construct the starting point and the target point, analyze the safety distance of obstacles and the path rotation limit, and perform smoothing optimization on the feasible path to obtain the final inspection path.

[0116] Embodiment 2:

[0117] This embodiment provides an inspection robot path planning device based on a neural network, and the device includes:

[0118] An acquisition module, configured to acquire substation layout information, where the substation layout information includes a starting point and a target point;

[0119] A first construction module, configured to search for nodes according to the starting point and the target point, perform node collision detection during the search process, and connect the starting point, the target point, and the searched nodes to obtain an initial sampling path set;

[0120] A second construction module, configured to calculate the sampling probability of the nodes in the initial sampling path set, combine the collision information and time cost information obtained from the collision detection, and perform iterative optimization on the nodes in the initial sampling path set through a preset neural network to obtain a sampling expansion scheme;

[0121] To clarify the specific acquisition method of the second construction module, specifically:

[0122] A first acquisition unit, configured to acquire historical trajectory data;

[0123] A first processing unit, configured to perform path planning on the initial sampling path set based on a preset adaptive positioning algorithm, optimize the initial sampling path set through a preset path evaluation criterion, and obtain a local path;

[0124] A second processing unit, configured to make a decision on the local path according to the historical trajectory data to obtain a walking path segment;

[0125] A third processing unit, configured to calculate the normalized probability of the walking path segment to obtain a sampling probability expression;

[0126] A fourth processing unit for calculating the nodes in the initial sampling path set based on the sampling probability expression, and integrating the nodes in the initial sampling path set through the collision information and the time cost information to obtain an optimized sampling path set;

[0127] A fifth processing unit for iteratively optimizing the nodes in the optimized sampling path set according to the neural network to obtain a sampling expansion scheme.

[0128] A third construction module for searching for nodes for the starting point and the target point based on the sampling expansion scheme, and performing node collision detection during the search process. Wherein, when colliding with an obstacle, a sampling reconstruction model is constructed, and different step size adjustments are made to the nodes obtained by searching the sampling expansion scheme according to the substation layout information and the shortest path is selected to obtain a feasible path;

[0129] To clarify the specific acquisition method of the third construction module, specifically:

[0130] A fifth processing unit for searching for nodes for the starting point and the target point based on the sampling expansion scheme to obtain expansion points, where the expansion points are the nodes obtained by searching the sampling expansion scheme, and there are no less than two expansion points;

[0131] A sixth processing unit for performing collision detection on each expansion point during the search process. When the expansion point collides with an obstacle, the initial sampling path set needs to be reconstructed;

[0132] A seventh processing unit for randomly sampling and constructing between the starting point and the target point to obtain a sampling reconstruction model;

[0133] An eighth processing unit for making different step size adjustments to each expansion point according to the sampling reconstruction model and the substation layout information and selecting the shortest path to obtain a feasible path.

[0134] A fourth construction module for smoothing and optimizing the feasible path to obtain a final inspection path.

[0135] To clarify the specific acquisition method of the fourth construction module, specifically:

[0136] A first optimization unit for dividing the feasible path into multiple sub-regions to obtain path blocks, where there are no less than two path blocks;

[0137] A second optimization unit for constructing a function for each path block according to the preset cubic spline interpolation method to obtain a piecewise interpolation function expression;

[0138] A third optimization unit is configured to construct the starting point and the target point based on the piecewise interpolation function expression, analyze the safety distance of the obstacle and the path rotation limit, and perform a smoothing optimization process on the feasible path to obtain a final inspection path.

[0139] It should be noted that regarding the device in the above embodiments, the specific manners in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated herein.

[0140] Embodiment 3:

[0141] Corresponding to the above method embodiments, a path planning device for an inspection robot based on a neural network is further provided in this embodiment. The path planning device for an inspection robot based on a neural network described below can be mutually corresponding and referred to with the path planning method for an inspection robot based on a neural network described above.

[0142] Figure 2 It is a block diagram of a path planning device 800 for an inspection robot based on a neural network shown according to an exemplary embodiment. As Figure 2 shown, the path planning device 800 for an inspection robot based on a neural network may include: a processor 801, a memory 802. The path planning device 800 for an inspection robot based on a neural network may further include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0143] Among them, the processor 801 is used to control the overall operation of the inspection robot path planning device 800 based on a neural network to complete all or part of the steps in the above-mentioned inspection robot path planning method based on a neural network. The memory 802 is used to store various types of data to support the operation of the inspection robot path planning device 800 based on a neural network. These data may include, for example, instructions for any application or method operating on the inspection robot path planning device 800 based on a neural network, as well as application-related data, such as contact data, sent and received messages, pictures, audio, video, and so on. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 802 or sent through the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the above-mentioned other interface modules may be a keyboard, a mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the inspection robot path planning device 800 based on a neural network and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination of one or more of them. Accordingly, the communication component 805 may include: a Wi-Fi module, a Bluetooth module, and an NFC module.

[0144] In an exemplary embodiment, the neural network-based inspection robot path planning device 800 can be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components, and is used to execute the above-mentioned neural network-based inspection robot path planning method.

[0145] In another exemplary embodiment, there is also provided a computer-readable storage medium including program instructions, and when the program instructions are executed by a processor, the steps of the above-mentioned neural network-based inspection robot path planning method are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 802 including program instructions, and the above-mentioned program instructions can be executed by the processor 801 of the neural network-based inspection robot path planning device 800 to complete the above-mentioned neural network-based inspection robot path planning method.

[0146] Embodiment 4:

[0147] Corresponding to the above method embodiment, in this embodiment, there is also provided a readable storage medium, and the following-described readable storage medium can be correspondingly referred to with the above-described neural network-based inspection robot path planning method.

[0148] A readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the neural network-based inspection robot path planning method in the above method embodiment are implemented.

[0149] The readable storage medium can specifically be various readable storage media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disc that can store program codes.

[0150] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0151] As described above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or replacements, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A path planning method for an inspection robot based on a neural network, characterized in that Including: Obtain substation layout information, where the substation layout information includes a starting point and a target point; Search for nodes based on the starting point and the target point, perform node collision detection during the search process, and connect the starting point, the target point, and the searched nodes to obtain an initial sampling path set; Calculate the sampling probability of the nodes in the initial sampling path set, combine the collision information and time cost information obtained from the collision detection, and iteratively optimize the nodes in the initial sampling path set through a preset neural network to obtain a sampling expansion plan; Among them, the specific way to obtain the sampling expansion plan includes: Obtain historical trajectory data; Perform path planning processing on the initial sampling path set based on a preset adaptive positioning algorithm, and optimize the initial sampling path set through a preset path evaluation criterion to obtain a local path; Make a decision on the local path according to the historical trajectory data to obtain a walking path segment; Perform normalized probability calculation on the walking path segment to obtain a sampling probability expression; Calculate the nodes in the initial sampling path set based on the sampling probability expression, and integrate the nodes in the initial sampling path set through the collision information and the time cost information to obtain an optimized sampling path set; Iteratively optimize the nodes in the optimized sampling path set according to the neural network to obtain a sampling expansion plan; Search for nodes based on the sampling expansion plan for the starting point and the target point, and perform node collision detection during the search process. Among them, when colliding with an obstacle, construct a sampling reconstruction model, and perform asynchronous step adjustment and select the shortest path for the nodes obtained by searching the sampling expansion plan according to the substation layout information to obtain a feasible path; Among them, the specific way to obtain the feasible path includes: Search for nodes based on the sampling expansion plan for the starting point and the target point to obtain expansion points. The expansion points are the nodes obtained by searching the sampling expansion plan, and there are no less than two expansion points; Perform collision detection on each expansion point during the search process. When the expansion point collides with an obstacle, it is necessary to reconstruct the initial sampling path set; Construct based on random sampling between the starting point and the target point to obtain a sampling reconstruction model; Among them, the specific way to obtain the sampling reconstruction model includes: Perform multiple random samplings and construct based on the distance between the starting point and the target point to obtain a reconstructed trajectory; Modify the initial sampling path set according to the reconstructed trajectory, and be constrained by the obstacle positions obtained from the collision detection to obtain a reconstructed path set; Perform iterative optimization processing on the reconstructed path set according to a preset maximum number of iterations, and perform statistical analysis on the error data in the initial sampling path set to obtain a sampling reconstruction model; Perform asynchronous step adjustment and select the shortest path for each expansion point according to the sampling reconstruction model and the substation layout information to obtain a feasible path; Smoothly optimize the feasible path to obtain the final inspection path.

2. The method for path planning of an inspection robot based on a neural network according to claim 1, wherein Adjust the step sizes of each of the extended points differently according to the sampling reconstruction model and the substation layout information, and select the shortest path to obtain a feasible path, including: Calculate the number of obstacles based on the sampling reconstruction model, the different regional environments in the substation layout information, and the collision information; Construct a dynamic step size expression according to the number of obstacles and different range constraints of the preset number of obstacles; Judge each of the extended points according to the dynamic step size expression to obtain a node judgment result; Adjust each of the extended points with different step sizes based on the node judgment result to obtain the final extended points; Connect the starting point, the target point, and the final extended points to obtain a final path set; Select the shortest path for each cumulative distance in the final path set to obtain a feasible path.

3. The path planning method for the inspection robot based on neural network according to claim 1, characterized in that, Smoothly optimize the feasible path to obtain a final inspection path, including: Divide the feasible path into multiple sub-regions to obtain path blocks, and the number of path blocks is not less than two; Construct a function for each of the path blocks according to the preset cubic spline interpolation method to obtain a piecewise interpolation function expression; Construct the starting point and the target point based on the piecewise interpolation function expression, analyze the safe distance of the obstacles and the path rotation limit, and perform smooth optimization processing on the feasible path to obtain the final inspection path.

4. The path planning device for the inspection robot based on a neural network, characterized in that, Including: An acquisition module for acquiring substation layout information, where the substation layout information includes a starting point and a target point; A first construction module for searching for nodes according to the starting point and the target point, performing node collision detection during the search process, and connecting the starting point, the target point, and the searched nodes to obtain an initial sampling path set; A second construction module for calculating the sampling probability of the nodes in the initial sampling path set, combining the collision information and time cost information obtained by the collision detection, and iteratively optimizing the nodes in the initial sampling path set through a preset neural network to obtain a sampling expansion scheme; Among them, the second construction module includes: A first acquisition unit for acquiring historical trajectory data; A first processing unit for performing path planning processing on the initial sampling path set based on a preset adaptive positioning algorithm, optimizing the initial sampling path set through a preset path evaluation criterion to obtain a local path; A second processing unit for making a decision on the local path according to the historical trajectory data to obtain a walking path segment; A third processing unit for calculating the normalized probability of the walking path segment to obtain a sampling probability expression; A fourth processing unit for calculating the nodes in the initial sampling path set based on the sampling probability expression, integrating the nodes in the initial sampling path set through the collision information and the time cost information to obtain an optimized sampling path set; A fifth processing unit for iteratively optimizing the nodes in the optimized sampling path set according to the neural network to obtain a sampling expansion scheme; The third construction module is used to search for nodes based on the sampling expansion scheme for the starting point and the target point, and perform node collision detection during the search process. Wherein, when colliding with an obstacle, a sampling reconstruction model is constructed, and different step size adjustments are made to the nodes obtained by searching the sampling expansion scheme according to the substation layout information, and the shortest path is selected to obtain a feasible path; Wherein, the third construction module includes: The fifth processing unit is used to search for nodes based on the sampling expansion scheme for the starting point and the target point to obtain expansion points, where the expansion points are nodes obtained by searching the sampling expansion scheme, and there are no less than two expansion points; The sixth processing unit is used to perform collision detection on each expansion point during the search process. When the expansion point collides with an obstacle, it is necessary to reconstruct the initial sampling path set; The seventh processing unit is used to construct a sampling reconstruction model based on random sampling between the starting point and the target point; Wherein, the specific acquisition method of the sampling reconstruction model includes: Perform multiple random samplings and construct based on the distance between the starting point and the target point to obtain a reconstructed trajectory; Modify the initial sampling path set according to the reconstructed trajectory, and be constrained by the obstacle positions obtained through the collision detection to obtain a reconstructed path set; Perform iterative optimization processing on the reconstructed path set according to a preset maximum number of iterations, and perform statistical analysis on the error data in the initial sampling path set to obtain a sampling reconstruction model; The eighth processing unit is used to perform different step size adjustments and select the shortest path for each expansion point according to the sampling reconstruction model and the substation layout information to obtain a feasible path; The fourth construction module is used to perform smoothing optimization on the feasible path to obtain the final inspection path.

5. The path planning device for the inspection robot based on a neural network according to claim 4, characterized in that, The fourth construction module includes: The first optimization unit is used to divide the feasible path into multiple sub-regions to obtain path blocks, where there are no less than two path blocks; The second optimization unit is used to construct a function for each path block according to the preset cubic spline interpolation method to obtain a piecewise interpolation function expression; The third optimization unit is used to construct the starting point and the target point based on the piecewise interpolation function expression, analyze the safety distance of the obstacle and the path rotation limit, and perform smoothing optimization processing on the feasible path to obtain the final inspection path.

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