Path planning method, system and equipment for inspection robot and medium
By using reverse learning to optimize population initialization in the inspection robot path planning, the problem of low efficiency of traditional COA path planning is solved, and more efficient path planning is achieved.
Patent Information
- Application Number
- CN202510042519.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-10
- Publication Date
- 2025-05-23
AI Technical Summary
In the prior art, inspection robots have low efficiency in path planning through traditional COA, which is mainly due to the uneven distribution of initial populations, which affects the search efficiency in subsequent iterations.
Reverse learning is used to initialize populations to generate forward populations and reverse populations. The reverse individual position is determined through forward individual positions, and the population initialization process is optimized to improve search efficiency.
Through reverse learning, the population initialization is optimized, the uniform distribution of the initial population in the solution space is improved, the algorithm's global search ability and path planning efficiency are improved, and the problem of low efficiency of traditional COA path planning is solved.
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Figure CN120029270A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of inspection robot path planning, and in particular to an inspection robot path planning method, system, device and medium. Background Art
[0002] With the development of intelligent robot technology, automatic inspection robots are widely used in monitoring and maintenance work in various environments. In order to ensure that these robots can complete their tasks efficiently and safely, path planning algorithms have become one of the key technologies. Path planning is not only related to the robot's work efficiency, but also directly affects its energy consumption and service life.
[0003] Among many path planning methods, swarm intelligence algorithms have attracted widespread attention because of their ability to simulate natural biological behaviors. One of them is the Cockroach Optimization Algorithm (COA), which imitates the behavior of dung beetles in finding food sources during foraging and searches for the optimal path through cooperation and competition between individuals. COA has certain advantages in dealing with path planning problems in complex environments, such as good adaptability to multi-obstacle scenarios.
[0004] However, conventional COA usually uses a random population initialization method, which often results in uneven distribution of the initial population, affecting the search efficiency in subsequent iterations. Therefore, in the prior art, the inspection robot has a low efficiency problem in path planning through conventional COA. Summary of the invention
[0005] The embodiments of the present application provide a method, system, device and medium for path planning of an inspection robot, so as to at least solve the problem of low efficiency of path planning of the inspection robot through traditional COA in the prior art.
[0006] In a first aspect, an embodiment of the present application provides a path planning method for an inspection robot, the method comprising:
[0007] Determine the starting point and target point of the path planning based on the pre-acquired map of the area to be planned;
[0008] Based on the starting point and the target point of the path planning, the population is initialized by reverse learning, wherein the population initialization includes generating a forward population and a reverse population, and determining the reverse individual position of each reverse population according to the forward individual position of each forward individual in the forward population;
[0009] Based on the initialized population, updating the initialized population;
[0010] According to the updated population status, it is determined whether the conditions for stopping updating are met. If the conditions are met, the target path of the inspection robot for inspecting the map of the area to be planned is obtained.
[0011] In one embodiment, determining the starting point and the target point of the path planning according to the pre-acquired map of the area to be planned includes:
[0012] Read and analyze the map of the area to be planned, and determine the traffic information and obstacle locations in the map of the area to be planned;
[0013] Based on the traffic information and the obstacle position, the starting point coordinates and the end point coordinates are extracted from the map of the area to be planned as the starting point and the target point of the path planning.
[0014] In one embodiment, determining the positions of the reverse individuals in each of the reverse populations according to the positions of the forward individuals in the forward population includes:
[0015] For each forward individual in the forward population, two random numbers are generated by a random number generator according to the map information of the area to be planned, wherein the two random numbers are respectively the X coordinate and the Y coordinate of the forward individual on the map of the area to be planned, and the position of the forward individual is determined based on the X coordinate and the Y coordinate, wherein the position of the forward individual includes the starting point and the target point of the path planning;
[0016] According to each of the forward individual positions, each reverse individual position corresponding to the forward individual position is calculated, wherein, according to the forward individual position, the reverse individual position is obtained by performing a symmetric transformation through the center point of the map to be planned.
[0017] In one embodiment, the step of obtaining the reverse individual position by performing a symmetric transformation through the center point of the map to be planned according to the forward individual position includes:
[0018] Determine the X coordinate of the reverse individual position according to the X coordinate of the forward individual position and the width of the map of the area to be planned;
[0019] Determine the Y coordinate of the reverse individual position according to the Y coordinate of the forward individual position and the height of the map of the area to be planned;
[0020] The reverse individual position is determined based on the X coordinate of the reverse individual position and the Y coordinate of the reverse individual position.
[0021] In one embodiment, updating the initialized population includes:
[0022] Choose crossover or mutation to update the population.
[0023] In one embodiment, the stop updating condition includes:
[0024] The maximum number of iterations has been reached;
[0025] The best path does not change over multiple consecutive iterations; or
[0026] Reach a predetermined fitness threshold.
[0027] In one embodiment, when the conditions are met, obtaining a target path of the inspection robot for inspecting the map of the area to be planned includes:
[0028] When the stop updating condition is met, the individual with the highest fitness is selected from the final population as the optimal solution, wherein the path represented by the individual with the highest fitness is the target path of the inspection robot on the map of the area to be planned, and the target path connects the starting point and the target point of the path planning.
[0029] In a second aspect, an embodiment of the present application provides a path planning system for an inspection robot, the system comprising a starting point and target point module for path planning, a population initialization module, a population update module, and a target path acquisition module, wherein:
[0030] The starting point and target point module of the path planning is used to determine the starting point and target point of the path planning according to the pre-acquired map of the area to be planned;
[0031] The population initialization module is used to perform population initialization through reverse learning based on the starting point and the target point of the path planning, wherein the population initialization includes generating a forward population and a reverse population, and determining the reverse individual position of each reverse population according to the forward individual position of each forward individual in the forward population;
[0032] The population updating module is used to update the initialized population based on the initialized population;
[0033] The target path acquisition module is used to determine whether the stop updating condition is met according to the updated population state, and if so, to obtain the target path of the inspection robot's inspection of the map of the area to be planned.
[0034] In a third aspect, an embodiment of the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, a patrol robot path planning method as described in the first aspect above is implemented.
[0035] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a patrol robot path planning method as described in the first aspect above.
[0036] The inspection robot path planning method, system, device and medium provided by the embodiments of the present application have at least the following technical effects.
[0037] The starting point and target point of the path planning are determined according to the pre-acquired map of the area to be planned. Based on the starting point and target point of the path planning, the population is initialized through reverse learning, wherein the population initialization includes generating a forward population and a reverse population, and determining the reverse individual positions in each reverse population according to the positions of each forward individual in the forward population. Based on the initialized population, the initialized population is updated. According to the updated population status, it is determined whether the stop updating condition is met. If it is met, the target path of the inspection robot to inspect the map of the area to be planned is obtained. The population initialization process is optimized through the initialization population strategy of reverse learning to more efficiently obtain a better inspection robot path. The problem of low efficiency of inspection robots performing path planning through traditional COA in the prior art is solved.
[0038] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS
[0039] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:
[0040] Figure 1 is a flow chart of a path planning method for an inspection robot according to an embodiment of the present application;
[0041] Figure 2 is a schematic diagram of the overall process of path planning according to an exemplary embodiment;
[0042] Figure 3 is a schematic diagram showing a method of determining a reverse individual position according to an exemplary embodiment;
[0043] Figure 4 is a block diagram of a system for path planning of an inspection robot according to an exemplary embodiment;
[0044] Figure 5 is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION
[0045] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.
[0046] Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. In addition, it can also be understood that although the efforts made in this development process may be complicated and lengthy, for ordinary technicians in this field related to the content disclosed in this application, some changes in design, manufacturing or production based on the technical content disclosed in this application are just conventional technical means, and should not be understood as insufficient content disclosed in this application.
[0047] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in this application may be combined with other embodiments without conflict.
[0048] Unless otherwise defined, the technical terms or scientific terms involved in this application should be understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "a", "a", "the" and the like involved in this application do not indicate a quantitative limitation, and may represent the singular or plural. The terms "include", "comprise", "have" and any of their variations involved in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "multiple" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there may be three relationships, for example, "A and / or B" can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.
[0049] In this document, it should be understood that the terms involved may be technical means for implementing a part of the present invention or other summary technical terms. For example, the terms may include:
[0050] Population: refers to a group of candidate solutions (individuals), each of which represents a possible path solution. These individuals are continuously optimized through evolutionary operations to find the optimal path.
[0051] Forward Population: A group of randomly generated individuals. The position of each individual is randomly selected on the map and is ensured to be within the traversable area.
[0052] Reverse Population: The reverse population is a group of individuals whose positions are symmetrically transformed with respect to the center point of the map.
[0053] Forward Individual: A member of the forward population whose location is randomly generated on the map and is located within the traversable area.
[0054] Reverse Individual: A member of the reverse population, whose position is obtained by symmetrically transforming the position of the forward individual about the center point of the map.
[0055] Among many path planning methods, swarm intelligence algorithms have attracted widespread attention because of their ability to simulate natural biological behaviors. One of them is the Cockroach Optimization Algorithm (COA), which imitates the behavior of dung beetles in finding food sources during foraging and searches for the optimal path through cooperation and competition between individuals. COA has certain advantages in dealing with path planning problems in complex environments, such as good adaptability to multi-obstacle scenarios.
[0056] However, the traditional dung beetle optimization algorithm also has some limitations:
[0057] Slow convergence speed: Since the movement of dung beetles is relatively simple, the algorithm may conduct a large amount of ineffective exploration in the search space, thus affecting the overall convergence speed.
[0058] Easy to fall into local optimality: When there are multiple local optimal points in the solution space, it may be difficult for the algorithm to jump out of the local optimal solution, and thus it is difficult to find the global optimal solution.
[0059] Poor initial population quality: Traditional COA usually adopts the method of randomly initializing the population, which often leads to uneven distribution of the initial population and affects the search efficiency in subsequent iterations.
[0060] Therefore, in the prior art, the inspection robot has the problem of low efficiency in path planning through traditional COA. The present application provides a method, system, device and medium for path planning of an inspection robot, which has solved the technical problem.
[0061] In a first aspect, an embodiment of the present application provides a path planning method for an inspection robot, which is applied to the stator winding end of a steam turbine generator. Figure 1 This is a flow chart of the inspection robot path planning. Figure 2 FIG. 1 is a schematic diagram of the overall process of path planning according to an exemplary embodiment. Figure 1 Shown and Figure 2 As shown, a path planning method for an inspection robot includes:
[0062] Step S101: Determine the starting point and target point of the path planning according to the pre-acquired map of the area to be planned.
[0063] Step S102: Based on the starting point and the target point of the path planning, the population is initialized through reverse learning, wherein the population initialization includes generating a forward population and a reverse population, and determining the reverse individual position in each reverse population according to the position of each forward individual in the forward population.
[0064] Step S103: Based on the initialized population, update the initialized population.
[0065] Step S104: determine whether the update stop condition is met according to the updated population status, and if so, obtain the target path of the inspection robot for inspecting the map of the area to be planned.
[0066] In summary, the embodiment of the present application provides a patrol robot path planning method, which applies reverse learning to the dung beetle path planning algorithm. The dung beetle patrol robot path planning algorithm based on reverse learning mainly improves the process of initializing the dung beetle algorithm population to improve the uniform distribution of the initialized population in the entire initial solution space. Based on this uniform distribution of the initial solution, the efficiency of the algorithm operation can be effectively improved. The problem of low efficiency of the patrol robot in the prior art in path planning through traditional COA is solved.
[0067] In one embodiment, step S101, based on a pre-acquired map of the area to be planned, determines the starting point and the target point of the path planning. Specifically, it includes:
[0068] Read and parse the map of the area to be planned, and determine the traffic information and obstacle locations in the map of the area to be planned.
[0069] Based on the traffic information and obstacle locations, the starting point and end point coordinates are extracted from the map of the area to be planned as the starting point and target point of the path planning.
[0070] Optionally, a data file of a map of the area to be planned is read from a storage device or a network. The data file includes detailed information of the map, such as the passable area, the location of obstacles, etc. The map data file is parsed to identify the boundaries of the map, the passable area, and the location of inaccessible obstacles. For example, image processing technology can be used to identify different areas on the map, or structured data files (such as XML, JSON) can be parsed to extract relevant information. Based on the pass information and the location of obstacles, appropriate starting and ending points are selected from the map. These points are located in the passable area and do not overlap with any obstacles. The selection of the starting point and the end point can be manually input by the user or automatically recognized by the system. If it is automatically recognized, it can be determined by preset marking points or specific areas. For example, specific marking points can be set on the map as the starting point and the end point, and the system detects the location of these marking points through image recognition technology.
[0071] Step S101 can ensure that the path planning algorithm can accurately avoid obstacles and find the shortest or optimal path in the actual environment by analyzing the map data in detail and determining the traffic information and obstacle locations. This helps to improve the working efficiency and safety of the inspection robot. By analyzing the map data in detail and determining the starting and ending points, it not only improves the accuracy of path planning, but also enhances the adaptability and efficiency of the system and improves the user experience.
[0072] Figure 3 is a schematic diagram showing a method of determining a reverse individual position according to an exemplary embodiment. Figure 3 As shown, step S102, based on the starting point and target point of the path planning, the population is initialized through reverse learning, wherein the population initialization includes generating a forward population and a reverse population, and determining the reverse individual position in each reverse population according to the forward individual position in the forward population. Specifically, the following steps are included:
[0073] Step S1021: for each forward individual in the forward population, two random numbers are generated by a random number generator according to the map information of the area to be planned, and the two random numbers are the X coordinate and Y coordinate of the forward individual on the map of the area to be planned. Based on the X coordinate and the Y coordinate, the position of the forward individual is determined, wherein the position of the forward individual includes the starting point and the target point of the path planning.
[0074] Step S1022: Calculate each reverse individual position corresponding to each forward individual position according to each forward individual position, wherein the reverse individual position is obtained by performing a symmetrical transformation through the center point of the map to be planned according to the forward individual position.
[0075] According to the forward individual position, a reverse individual position is obtained by performing a symmetric transformation through the center point of the map to be planned, including: determining the X coordinate of the reverse individual position according to the X coordinate of the forward individual position and the width of the map of the area to be planned; determining the Y coordinate of the reverse individual position according to the Y coordinate of the forward individual position and the height of the map of the area to be planned; and determining the reverse individual position based on the X coordinate of the reverse individual position and the Y coordinate of the reverse individual position.
[0076] Optionally, for each positive individual in the positive population, a random number generator is used to generate two random numbers, representing the X coordinate and Y coordinate of the individual on the map of the area to be planned. Ensure that the generated positive individual position is within the passable area and does not overlap with obstacles. If the generated position is invalid, regenerate a new position until a valid starting position is found. Based on the generated X coordinates and Y coordinates, determine the position of the positive individual. These positions can be arbitrary, but it must be ensured that they are feasible path points. In particular, the positive individual position can also include the starting point and target point of the path planning to ensure that the algorithm can take these key points into account. For each positive individual position (x, y), calculate its corresponding reverse individual position (x', y'), where x' and y' are obtained by symmetrically transforming (x, y) about the center point of the map. The specific calculation formula is:
[0077] X coordinate of the reverse individual position: x' = Width – x;
[0078] Y coordinate of the reversed individual position: y' = Height – y;
[0079] Among them, Width and Height represent the width and height of the map respectively.
[0080] Ensure that the position of each reverse individual is also within the passable area and does not overlap with obstacles. If the position of a reverse individual is invalid, regenerate the corresponding forward individual position until the positions of all reverse individuals are valid. Based on the calculated X and Y coordinates of the reverse individual position, determine the specific position of the reverse individual, record these positions, and form a reverse population.
[0081] Step S102 can ensure that the initial population is distributed more evenly in the solution space by randomly generating the forward individual position and calculating the corresponding reverse individual position. This helps to avoid the algorithm from falling into the local optimal solution too early and improves the global search capability. The combination of forward individuals and reverse individuals enables the population to cover more solution space areas during the search process. This strategy helps the algorithm to better adapt to complex environments, especially when there are multiple local optimal points. Through the reverse learning strategy, the population can more effectively jump out of the local optimum and move towards the global optimum. Since the initial population has higher diversity and uniform distribution, the algorithm can find a better solution in a shorter time, can reach a satisfactory solution faster, and reduce the number of unnecessary iterations. By improving the population initialization, the present invention can converge to the global optimal solution faster and improve the efficiency of path planning. The robot can find a suitable solution more quickly when exploring and planning the path, thereby reducing the search time and unnecessary movement. By ensuring that the positions of the forward and reverse individuals are both located in the passable area and do not overlap with obstacles, the quality of path planning can be improved. This helps to find a more reasonable and safer path.
[0082] In one embodiment, after the population is initialized in step S102, the method further includes detecting whether all individuals of the forward population and the reverse population have completed initialization. If not, selecting the next pair of forward individuals and reverse individuals to continue the initialization operation of step S102. If completed, recording the initial optimal path.
[0083] Optionally, after generating each forward individual and its corresponding reverse individual, check whether all individuals to be generated have completed initialization. The specific implementation can track the number of individuals generated by a counter. When the counter reaches a preset population size, it means that all individuals have completed initialization. If initialization is not completed, select the next pair of forward individuals and reverse individuals, and continue to perform the initialization operation of step S102. Repeat steps S1021 and S1022 until all individuals have completed initialization. Calculate the fitness value of each individual (i.e., each path) using path length or other relevant indicators. Select the individual with the highest fitness from the initial population as the initial optimal path. By detecting whether all individuals have completed initialization, the integrity and diversity of the population can be ensured. This helps the algorithm to better explore the solution space in subsequent iterative processes.
[0084] In one embodiment, step S103 , based on the initialized population, the initialized population is updated.
[0085] Specifically include:
[0086] Optionally, crossover or mutation is selected to update the population. Specifically, crossover or mutation is selected through a roulette strategy. Roulette is to set a mutation crossover threshold, and through the selection of random numbers, crossover or mutation can be performed when it is within the threshold range to update the initialized population.
[0087] In one embodiment, step S104, judging whether a stop updating condition is met according to the updated population state, and if so, obtaining a target path of the inspection robot for inspecting the map of the area to be planned.
[0088] Specifically include:
[0089] The individual with the highest fitness is selected from the final population as the optimal solution, where the path represented by the individual with the highest fitness is the target path of the inspection robot on the map of the area to be planned, and the target path connects the starting point and the target point of the path planning. The conditions for stopping the update include reaching the maximum number of iterations; the optimal path has not changed in multiple consecutive iterations; or reaching a predetermined fitness threshold.
[0090] Optionally, check whether the current number of iterations reaches the preset maximum number of iterations. If the optimal path does not change in multiple consecutive iterations within the maximum number of iterations, the algorithm is considered to have converged. Or in order to improve the iteration efficiency, check whether the fitness of the optimal individual in the current population reaches a predetermined fitness threshold. This threshold can be set according to the needs of the specific problem, indicating that the solution found by the algorithm is good enough. If the set stop condition is met, stop further iterative updates. If no stop condition is met, continue to perform selection, crossover and mutation operations to update the population and perform the next iteration. When the stop update condition is met, select the individual with the highest fitness from the final population as the optimal solution. The path represented by the optimal solution is the target path of the inspection robot on the map of the area to be planned. Output the determined target path for use by the inspection robot. The inspection robot moves according to the output target path to complete the inspection task.
[0091] By setting a variety of stop conditions (maximum number of iterations, the best path has not changed in multiple consecutive iterations, reaching a predetermined fitness threshold), it is possible to ensure that the algorithm converges to a satisfactory solution within a reasonable time. This helps to avoid the algorithm running indefinitely and improves computational efficiency. By selecting the individual with the highest fitness as the optimal solution, it is possible to ensure that the path found is the best in the current population. The design of the fitness function directly affects the quality of the path. Path length or other related indicators are usually used to measure fitness, thereby ensuring that the path found is the shortest or optimal.
[0092] In summary, the embodiments of the present application provide a method for patrol robot path planning, by determining the starting point and target point of the path planning according to a pre-acquired map of the area to be planned. Based on the starting point and target point of the path planning, the population is initialized through reverse learning, wherein the population initialization includes generating a forward population and a reverse population, and determining the reverse individual positions in each reverse population according to the positions of each forward individual in the forward population. Based on the initialized population, the initialized population is updated. According to the updated population status, it is determined whether the stop updating condition is met. If it is met, the target path of the patrol robot's patrol map of the area to be planned is obtained. The population initialization process is optimized by the initialization population strategy of reverse learning, so as to more efficiently obtain a better patrol robot path. The problem of low efficiency of patrol robots performing path planning through traditional COA in the prior art is solved.
[0093] The embodiments of the present application also include the following beneficial effects:
[0094] Combination of reverse learning and dung beetle path planning: This invention combines the reverse learning method with the dung beetle inspection robot path planning algorithm for the first time. This combination integrates biological behavior simulation and machine learning optimization technology, bringing new thinking and methods to the field of path planning. This innovative combination makes the dung beetle path planning algorithm more autonomous and adaptable when exploring unknown environments.
[0095] Uniform distribution strategy for population initialization: This paper improves the population initialization process of the dung beetle algorithm to achieve uniform distribution of solutions in the entire initial solution space. The traditional dung beetle algorithm may fall into a local optimum during the search process, while this paper can better explore in different areas by optimizing population initialization, thus improving the global search capability.
[0096] Improvement of path planning efficiency: Based on the uniformly distributed initial solution, the present invention enables the dung beetle inspection robot to converge to the global optimal solution more quickly. This is very critical for the robot path planning problem, which can greatly reduce the search time and improve the efficiency of path planning.
[0097] Multi-field application potential: The present invention is not limited to a certain field, and its innovative ideas and methods can be applied in multiple fields. From the path planning of unmanned exploration robots to the path planning of intelligent logistics warehousing robots, the present invention can provide an effective solution to the path planning problem in complex environments.
[0098] Enhanced algorithm intelligence: By introducing reverse learning, the present invention enhances the intelligence of the dung beetle path planning algorithm. The robot can better adapt to environmental changes by learning past path information, thereby better planning the path.
[0099] In summary, the innovation of the present invention is that it combines reverse learning with the dung beetle path planning algorithm, improves population initialization, improves path planning efficiency, has multi-field application potential, and enhances algorithm intelligence. These innovations together constitute the uniqueness and practical application value of the present invention.
[0100] In a second aspect, an embodiment of the present application provides a path planning system for an inspection robot. Figure 4 FIG. 1 is a block diagram of a system for grading defect text of a relay protection device according to an exemplary embodiment. Figure 4 As shown, the system includes a starting point and target point module 410 for path planning, a population initialization module 420, a population update module 430, and a target path acquisition module 440, wherein:
[0101] The starting point and target point module 410 of the path planning is used to determine the starting point and target point of the path planning according to the pre-acquired map of the area to be planned;
[0102] A population initialization module 420 is used to initialize the population through reverse learning based on the starting point and the target point of the path planning, wherein the population initialization includes generating a forward population and a reverse population, and determining the reverse individual position in each reverse population according to the forward individual position in the forward population;
[0103] An updating population module 430 is used to update the initialized population based on the initialized population;
[0104] The target path acquisition module 440 is used to determine whether the stop updating condition is met according to the updated population state, and if so, to obtain the target path of the inspection robot for inspecting the map of the area to be planned.
[0105] In summary, the path planning system for a patrol robot provided by the embodiment of the present application solves the problem of low efficiency in the path planning of the patrol robot through the traditional COA in the prior art through the starting point and target point module 410 of the path planning, the population initialization module 420, the updating population module 430 and the obtaining target path module 440. Specifically, the starting point and target point of the path planning are determined according to the pre-acquired map of the area to be planned. Based on the starting point and target point of the path planning, the population is initialized by reverse learning, wherein the population initialization includes generating a forward population and a reverse population, and determining the reverse individual position in each reverse population according to the forward individual position in the forward population. Based on the initialized population, the initialized population is updated. According to the updated population state, it is judged whether the stop update condition is met, and if it is met, the target path of the patrol robot patrol map of the area to be planned is obtained. The population initialization process is optimized by the initialization population strategy of reverse learning to obtain a better patrol robot path more efficiently. The problem of low efficiency in the path planning of the patrol robot through the traditional COA in the prior art is solved.
[0106] It should be noted that the inspection robot path planning system provided in this embodiment is used to implement the above-mentioned implementation mode, and the descriptions that have been made will not be repeated. As used above, the terms "module", "unit", "subunit", etc. can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the above embodiment is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.
[0107] In a third aspect, an embodiment of the present application provides an electronic device, Figure 5 FIG. 1 is a block diagram of an electronic device according to an exemplary embodiment. Figure 5 As shown, the electronic device may include a processor 51 and a memory 52 storing computer program instructions.
[0108] Specifically, the processor 51 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.
[0109] Among them, the memory 52 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 52 may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 52 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 52 may be inside or outside the data processing device. In a specific embodiment, the memory 52 is a non-volatile memory. In a specific embodiment, the memory 52 includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (Programmable Read-Only Memory, PROM for short), an erasable PROM (Erasable ProgrammableRead-Only Memory, EPROM for short), an electrically erasable PROM (Electrically Erasable ProgrammableRead-Only Memory, EEPROM for short), an electrically alterable ROM (Electrically Alterable Read-Only Memory, EAROM for short) or a flash memory (FLASH) or a combination of two or more of these. Under appropriate circumstances, the RAM can be a static random access memory (SRAM) or a dynamic random access memory (DRAM), wherein the DRAM can be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.
[0110] The memory 52 may be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 51 .
[0111] The processor 51 implements any one of the inspection robot path planning methods in the above embodiments by reading and executing computer program instructions stored in the memory 52 .
[0112] In one embodiment, a device for inspection robot path planning may further include a communication interface 53 and a bus 50. Figure 5 As shown, the processor 51, the memory 52, and the communication interface 53 are connected via a bus 50 and communicate with each other.
[0113] The communication interface 53 is used to implement communication between the modules, devices, units and / or equipment in the embodiment of the present application. The communication port 53 can also implement data communication with other components such as: external devices, image / data acquisition equipment, databases, external storage, and image / data processing workstations.
[0114] The bus 50 includes hardware, software or both, and couples the components of a device for inspection robot path planning to each other. The bus 50 includes but is not limited to at least one of the following: a data bus, an address bus, a control bus, an expansion bus, and a local bus. By way of example and not limitation, bus 50 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses or a combination of two or more of these. Where appropriate, bus 50 may include one or more buses. Although embodiments of the present application describe and illustrate a particular bus, the present application contemplates any suitable bus or interconnect.
[0115] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements a patrol robot path planning method provided in the first aspect.
[0116] The readable storage medium may include but is not limited to: a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device or any suitable combination of the above.
[0117] In a possible implementation, the present invention can also be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to enable the terminal device to execute the steps of a patrol robot path planning method provided in the first aspect.
[0118] Among them, the program code for executing the present invention can be written in any combination of one or more programming languages, and the program code can be executed completely on the user device, partially on the user device, as an independent software package, partially on the user device and partially on a remote device, or completely on the remote device.
[0119] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0120] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.
Claims
1. A path planning method for an inspection robot, characterized in that: The method comprises: Determine the starting point and target point of the path planning based on the pre-acquired map of the area to be planned; Based on the starting point and the target point of the path planning, the population is initialized by reverse learning, wherein the population initialization includes generating a forward population and a reverse population, and determining the reverse individual position of each reverse population according to the forward individual position of each forward individual in the forward population; Based on the initialized population, updating the initialized population; According to the updated population status, it is determined whether the conditions for stopping updating are met. If the conditions are met, the target path of the inspection robot for inspecting the map of the area to be planned is obtained.
2. The method according to claim 1, characterized in that According to the pre-acquired map of the area to be planned, determine the starting point and target point of the path planning, including: Read and analyze the map of the area to be planned, and determine the traffic information and obstacle locations in the map of the area to be planned; Based on the traffic information and the obstacle position, the starting point coordinates and the end point coordinates are extracted from the map of the area to be planned as the starting point and the target point of the path planning.
3. The method according to claim 1, characterized in that: The step of determining the positions of the reverse individuals in each of the reverse populations according to the positions of the forward individuals in the forward population comprises: For each forward individual in the forward population, two random numbers are generated by a random number generator according to the map information of the area to be planned, wherein the two random numbers are respectively the X coordinate and the Y coordinate of the forward individual on the map of the area to be planned, and the position of the forward individual is determined based on the X coordinate and the Y coordinate, wherein the position of the forward individual includes the starting point and the target point of the path planning; According to each of the forward individual positions, each reverse individual position corresponding to the forward individual position is calculated, wherein, according to the forward individual position, the reverse individual position is obtained by performing a symmetric transformation through the center point of the map to be planned.
4. The inspection robot path planning method according to claim 3, characterized in that: The step of acquiring the reverse individual position by performing a symmetric transformation based on the forward individual position through the center point of the map to be planned includes: Determine the X coordinate of the reverse individual position according to the X coordinate of the forward individual position and the width of the map of the area to be planned; Determine the Y coordinate of the reverse individual position according to the Y coordinate of the forward individual position and the height of the map of the area to be planned; The reverse individual position is determined based on the X coordinate of the reverse individual position and the Y coordinate of the reverse individual position.
5. The inspection robot path planning method according to claim 1, characterized in that: The updating of the initialized population comprises: Choose crossover or mutation to update the population.
6. The inspection robot path planning method according to claim 1, characterized in that: The update stop conditions include: The maximum number of iterations has been reached; The best path does not change over multiple consecutive iterations; or Reach a predetermined fitness threshold.
7. The method according to claim 6, characterized in that When the above conditions are met, obtaining the target path of the inspection robot for inspecting the map of the area to be planned includes: When the stop updating condition is met, the individual with the highest fitness is selected from the final population as the optimal solution, wherein the path represented by the individual with the highest fitness is the target path of the inspection robot on the map of the area to be planned, and the target path connects the starting point and the target point of the path planning.
8. A patrol robot path planning system, characterized in that: The system includes a starting point and target point module for path planning, a population initialization module, a population update module and a target path acquisition module, wherein: The starting point and target point module of the path planning is used to determine the starting point and target point of the path planning according to the pre-acquired map of the area to be planned; The population initialization module is used to perform population initialization through reverse learning based on the starting point and the target point of the path planning, wherein the population initialization includes generating a forward population and a reverse population, and determining the reverse individual position of each reverse population according to the forward individual position of each forward individual in the forward population; The population updating module is used to update the initialized population based on the initialized population; The target path acquisition module is used to determine whether the stop updating condition is met according to the updated population state, and if so, to obtain the target path of the inspection robot's inspection of the map of the area to be planned.
9. An electronic device, characterized in that: The invention comprises a memory and a processor, a computer program stored in the memory and executable on the processor, and the processor implements a path planning method for an inspection robot as claimed in any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a path planning method for an inspection robot according to any one of claims 1 to 7 is implemented.