A planar mobile robot homotopy class sunlight irradiation sampling path planning method

Abstract

This invention relates to a path planning method for homotopy classes in planar mobile robots, based on sunlight sampling. The method includes dividing a two-dimensional planar map to be searched into a topological map composed of obstacle modules and free convex polygon modules using convex decomposition. Simultaneously, it records the relationships between convex polygon modules and their vertices, as well as the datasets of convex polygon modules and their adjacent convex polygon modules. A heuristic function is set to expand from the starting block, continuously recording the blocks traversed by the path and excluding parent and ancestor blocks to prevent path looping when advancing an adjacent block. Upon reaching the target block, the method stops and records a set of non-repeating path blocks as a path homotopy class. Sunlight sampling is performed on each path homotopy class to obtain the intra-class shortest path. This invention can quickly search for path homotopy classes in the topological map and uses the sunlight principle to search for intra-class shortest paths, achieving rapid partitioning of path homotopy classes and significantly improving the search efficiency for intra-class shortest paths.

Description

Technical Field

[0001] This invention relates to the field of automatic path planning technology for planar mobile robots, and in particular to a method for planning a homotopic sunlight irradiation sampling path for a planar mobile robot. Background Technology

[0002] Path planning, as one of the core technologies supporting autonomous decision-making in unmanned systems, has wide applications in cutting-edge fields, including autonomous vehicles, industrial robots, maritime navigation, and drones. Currently, the main goal of path planning is to plan an optimal or near-optimal collision-free path in a given environment, minimizing the path distance while minimizing energy consumption. With the development of science and technology, path planning is continuously evolving towards greater adaptability to dynamically changing environments, more accurate multi-objective optimization, and lower computational costs; this trend has become a research hotspot in the field.

[0003] Classical path planning algorithms include two types: graph search-based algorithms and random sampling-based algorithms. The former is exemplified by Dijkstra's algorithm, A.S.K.S. The first type of algorithm is the grid-based algorithm, which discretizes the environment using a grid and can guarantee finding the global optimum. However, its computational efficiency is limited by the grid resolution. In complex environments, the number of graph nodes and edges increases exponentially, leading to even higher computational complexity. The second type of algorithm is represented by Probabilistic Path Graph (PRM) and Rapid Exploratory Random Tree (RRT) series algorithms. These algorithms circumvent explicit grid partitioning constraints by randomly sampling the continuous state space. However, with limited sampling, they can only obtain suboptimal solutions and have a slow convergence speed, making them unsuitable for real-time path planning and multi-track path planning. Summary of the Invention

[0004] To address the aforementioned problems, the present invention aims to provide a homotopy-based path planning method for planar mobile robots based on sunlight sampling. This method involves convex decomposition of a two-dimensional planar map into convex polygon free space modules and obstacle modules. A heuristic function is then used to advance through the free space modules, starting from the starting block and progressing to the target block. The sequence of path blocks traversed is recorded as a homotopy class. Based on the principle of sunlight illumination, vertices of the homotopy classes are rapidly sampled and advanced block by block. Each homotopy class contains no obstacles, allowing for rapid calculation of the intra-class shortest path. By solving for low-cost visible vertices within each homotopy class, the convergence speed of the algorithm is significantly improved while achieving shortest path planning, demonstrating significant advantages over traditional random sampling algorithms.

[0005] The technical solution adopted in this invention is as follows:

[0006] The method for homotopy-based sunlight irradiation sampling path planning for a planar mobile robot proposed in this invention specifically includes the following steps: S1. Divide the space to be searched into obstacle spaces. and free space The free space is divided into a finite number of triangular free region modules by using the constrained triangulation method. Then, the triangular free region modules are merged into convex polygon free region modules by the greedy merging method, and the region index of each convex polygon free region module is recorded. S2. Set up a heuristic function to expand from the starting block, continuously record the blocks traversed by the path and exclude parent and ancestor blocks to prevent looping, stop when the target block is reached and record the sequence of path blocks traversed as a path homotopy class; S3. For each homotopy class, perform solar selection and illumination sampling, calculate the intra-class shortest path for each homotopy class, and finally obtain the shortest path for all homotopy classes.

[0007] Furthermore, step S1 includes: S11. Divide the entire search space into obstacle modules. and free space module Setting the starting point for the robot's travel path and target point ; S12. Using constrained triangulation to divide the free space module The union of modules divided into countable triangular free regions; S13. Based on the principle of greedy merging, merge triangular regions that share boundaries and remain convex polygons after merging, ultimately obtaining the free space of convex polygons. .

[0008] Further step S12 includes: extracting the coordinates of all vertices and the positions of obstacle modules in the search space, as well as the constraint relationships of the space boundary; performing triangulation by hollowing out the obstacle modules using constrained Delaunay triangulation, thereby dividing the space into a countable number of triangular free spaces.

[0009] Further step S13 includes: given the large number of triangular free regions, to simplify the topological space, a greedy merging strategy is adopted. All triangular free regions and their shared edges are traversed. Triangular free regions with shared edges that remain convex polygons after merging are continuously merged until all convex polygons in the topological map cannot be merged. The region index of each convex polygon module is recorded to obtain the convex polygon free space. .

[0010] Further step S2 includes: S21. Extract Records For each convex polygon, there is a set of coordinate relationships between its vertices. For each convex polygon, there is a set of relationships between adjacent convex polygons, and this set records the relationships between them. The starting block With inclusion target block ; S22. Set up a heuristic function to first calculate the distance from the geometric center of all convex polygon modules to the starting point and the target point. Then, expand the module block by block starting from the starting point. Record the non-parent adjacent modules of the starting block set ;like At that time, with The probability is used to select the block with the minimum path cost for expansion; otherwise, the probability is used to select the block with the minimum path cost for expansion. The probability is randomly selected, and blocks on the path are continuously recorded; S23, Set of Non-Parent Adjacency Modules Besides not including the parent block, the block also cannot include ancestor blocks to prevent wraparound paths; if When the middle is an empty set, that is, the path is not working, and it is necessary to return to the starting block to search. When the target block is reached, the path block sequence is found and the path block sequence is recorded as a path homotopy class. S24. Set weight coefficients probability parameters ; Iterate through all cases, calculate the homotopy class of the path and store it, then delete the homotopy class of duplicate paths; S25. If no path homotopy class is found, a breadth-first search algorithm is used to find at least one path homotopy class. Finally, all unique path homotopy classes are recorded as follows. .

[0011] In further step S22, the cost function for each path module is calculated according to the heuristic function as follows:

[0012] in, This is the cost function for the driving path module; This is the distance from the geometric center of the current convex polygon module to the starting point; This is the distance from the geometric center of the current convex polygon module to the target point; for The weight parameters, The larger the value, the faster the convergence to the target point; Let be the probability parameters of the heuristic function. The larger the value, the more certain the choice; otherwise, the more random the choice.

[0013] Further step S3 includes: S31, Settings Record the cumulative path distance from all vertices to the starting point. The initial values ​​are all set to Extract the set of shared edges between adjacent convex polygons from each path homotopy class. ; S32. Set the priority queue set Initially, there is only a set of starting points and path endpoints. Father-son relationship Temporary assembly First, solar selection is performed, based on... Cumulative path distance from all points in the set to the starting point Sort them and select them one by one according to their cost from smallest to largest; S33. Based on the principle of simulating sunlight, the illumination adopts a block-by-block approach, starting from the starting block and moving the starting point from... Delete the statement that uses the starting point as the point where the sun emits rays into the homotopy class along that path, and determine... Is it less than Record the minimum distance and determine the starting point. Is it possible to traverse all shared edges of the homotopy class along this path? Thus illuminating the target point At this point, we need to discuss two scenarios: like Less than The minimum distance recorded is obviously true. The initial value is And starting point It is possible to traverse all shared edges of the homotopy class of this path. Illuminate the target point If the condition is met (visibility is satisfied), then the starting point is recorded. Its index block and total computation path cost They are all included in set P; like Less than The minimum distance recorded is obviously true. The initial value is And starting point It is not possible to directly traverse all shared edges of the homotopy class of this path. Illuminate the target point If the visibility condition is not met, then iterate through all vertices of the convex polygons in front of the farthest block that can be reached by the ray. ; Calculation starting point Distance to all vertices ,like Smaller than the vertex The minimum distance and starting point recorded Meeting the visibility condition allows light to pass through to the vertex. All shared edges Then update And add these vertices to set S; traverse all vertices in set S, record their parent-child relationships and add them to set E, and then sort all vertices in set S according to their parent-child relationships. Push size into priority queue set ; S34, from the priority queue set Select all vertices other than the origin as ordinary suns Conduct simulated sunlight exposure to determine Is it less than Minimum recorded distance and sun Is it possible to traverse subsequent shared edges? Illuminate the target point At this point, we need to discuss three scenarios: like Greater than The recorded solar point The minimum distance proves that the sun point is... Since the sun point in point O has already been illuminated by less costly light while queuing, it is discarded. Select a new sun point from the priority queue set O; like Less than The recorded solar point Minimum distance, and sun point Able to cross subsequent shared edges Illuminate the target point Then record the sun point. Its index block and total computation path cost They are all included in set P; like Less than The recorded solar point Minimum distance, and sun point Cannot cross subsequent shared edges Illuminate the target point Then, iterate through all the vertices of the convex polygons before the farthest block that the ray can reach. ; Calculate the solar point Distance to all vertices ,like Smaller than the vertex Minimum recorded distance and sun point Able to pass through the illumination to the vertex All shared edges Then update Then add these vertices to set S; iterate through all vertices in set S, record their new parent-child relationships, update set E, and then add all vertices in set S according to their parent-child relationships. Push size into priority queue set ; S35. Repeat step S34, when... When the set is empty, traverse Given the path costs in the set, find the path with the shortest cost, based on... Backtracking through parent-child relationships yields the intra-class shortest path for each homotopy class. .

[0014] Further step S33 includes: calculating the relationship between the node rays and the ordered sequence of shared edges using a two-dimensional cross product algorithm. The relative geometric position; if and only if light can pass through the ordered sequence of shared edges in sequence. When a line segment at the corresponding level does not intersect with the non-common boundary of a convex polygon, it is determined to meet the visibility condition; during this process, there is no need to perform collision detection on obstacles in the global environment.

[0015] Compared with the prior art, the present invention has the following advantages: 1. This invention constructs the path of a planar mobile robot into multiple non-repeating path homotopy classes by topologicalizing a two-dimensional planar map. Utilizing the principle of sunlight illumination, a new pathfinding method is designed, thereby achieving intra-class shortest path planning while significantly improving the convergence speed of the algorithm, which has obvious advantages over traditional planning methods.

[0016] 2. This invention can not only quickly and accurately find the intra-class shortest travel path required by the robot in two-dimensional space, but also ensure the diversity of feasible paths. The robot can find the intra-class shortest path of multiple path homotopy classes when facing complex geographical environments. It also optimizes the calculation time while ensuring the smoothness of the travel path, effectively solving the problem of the robot finding the shortest travel path during automatic search. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a homotopy-based sunlight irradiation sampling path planning method for a planar mobile robot proposed in this invention. Figure 2 This is a schematic diagram illustrating the convex decomposition and topology of a two-dimensional planar map in this invention; Figure 3 This is a schematic diagram of non-repeating homotopy-class topology path pipelines in this invention (only three are shown). Figure 4This is a schematic diagram illustrating the intra-class optimal path finding process based on simulated sunlight illumination in this invention. Figure 5 This is a schematic diagram of multiple feasible optimal paths within a class in this invention (only five are shown). Detailed Implementation

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] To ensure the normal operation of the algorithm, this invention divides the search space into an obstacle space. and free space This invention utilizes constrained triangulation to divide the free space into a finite number of triangular free region modules. Then, using a greedy merging method, these triangular free region modules are merged into convex polygon free region modules, and the region index of each convex polygon free region module is recorded. To ensure the speed of free region search, this invention innovatively expands the heuristic function from the starting block, based on... Calculate the non-parent adjacent blocks of the starting block The method continuously records the blocks traversed by the path and excludes its parent and ancestor blocks to prevent path blocks from looping. When the target block is reached, the path homotopy class is found and the sequence is recorded. This invention refers to this homotopy class path sequence as a path homotopy class, characterized by the absence of obstacles except for the boundary in each path homotopy class. Subsequent illumination is performed in independent path homotopy classes. Finally, for each path homotopy class, sun selection and illumination sampling are performed, and the intra-class shortest path of each path homotopy class is calculated. To ensure the superiority of this method, this invention transforms obstacle collision detection into calculating the node rays and the ordered shared edge sequence using a two-dimensional cross product algorithm. The relative geometric position of the light rays is such that the light rays can pass through the ordered sequence of shared edges in sequence if and only if the light rays can pass through the sequence in sequence. When a line segment of the corresponding level in the class does not intersect with the non-common boundary of the convex polygon, it is determined to meet the visibility condition, and the shortest path within the class is finally obtained after screening and calculation.

[0020] See appendix Figure 1 The present invention proposes a homotopy-based sunlight irradiation sampling path planning method for planar mobile robots, the specific implementation process of which includes the following steps: S1. Divide the space to be searched into obstacle spaces. and free space The free space is divided into a finite number of triangular free region modules using constrained triangulation. Then, a greedy merging method is used to merge these triangular free region modules into convex polygon free region modules, and the region index of each convex polygon free region module is recorded. The specific steps are as follows: S11. Divide the entire search space into obstacle modules. and free space module Setting the starting point for the robot's travel path and target point ; S12, Using constrained triangulation ( The method will use the free space module The union of modules divided into countably many triangular free regions; specifically including: Extract the coordinates of all vertices and the positions of obstacle modules in the search space, as well as the spatial constraints, and use constrained Delaunay triangulation. The obstacle-clearing module performs triangulation, dividing the space into countably many triangular free spaces. Constrained Delaunay triangulation is employed. The purpose is to divide free space into convex polygon modules. According to the properties of convex polygons, a ray connecting two points inside a convex polygon will not touch any obstacles.

[0021] S13. Based on the principle of greedy merging, merge triangular regions that share boundaries and remain convex polygons after merging, ultimately obtaining the free space of convex polygons. Specifically, it includes: Given the large number of free triangular regions, a greedy merging strategy is adopted to simplify the topological space. This involves traversing all free triangular regions and their shared edges, continuously merging free triangular regions that share edges and remain convex polygons after merging, until no more convex polygons in the topological map can be merged. The region index of each convex polygon module is recorded, thus obtaining the free space of the convex polygons. .

[0022] like Figure 2 The diagram shown illustrates the topological decomposition of a two-dimensional planar map proposed in this invention. Through convex decomposition, the obstacle module and the convex polygon free region module can be simplified, allowing subsequent exploration to proceed only within the free space of the convex polygon. Just proceed.

[0023] S2. Set a heuristic function to expand from the starting block, continuously recording the blocks traversed along the path and excluding parent and ancestor blocks to prevent looping. Stop when the target block is reached and record the sequence of path blocks traversed as a path homotopy class; specifically including: S21. Extract and record For each convex polygon, there is a set of coordinate relationships between its vertices. For each convex polygon, there is a set of relationships between adjacent convex polygons, and this set records the relationships between them. The starting block With inclusion target block ; S22. Set up a heuristic function to first calculate the distance from the geometric center of all convex polygon modules to the starting point and the target point. Then, expand the module block by block starting from the starting point. Record the non-parent adjacent modules of the starting block gather .like At that time, with The probability is used to select the block with the minimum path cost for expansion; otherwise, the probability is used to select the block with the minimum path cost for expansion. The probability is randomly selected, and blocks on the path are continuously recorded; The cost function for each path module, calculated using heuristics, is as follows:

[0024] in, This is the cost function for the driving path module; This is the distance from the geometric center of the current convex polygon module to the starting point; This is the distance from the geometric center of the current convex polygon module to the target point; for The weight parameters, The larger the value, the faster it converges to the target point.

[0025] S23, Set of Non-Parent Adjacency Modules Besides not including the parent block, the block also cannot include ancestor blocks to prevent wraparound paths. If When the middle is an empty set, the path is blocked, and the search needs to be restarted from the starting block. When the target block is reached, the path block sequence is found and recorded as a path homotopy class.

[0026] S24. Set weight coefficients probability parameters Iterate through all cases, calculate the homotopy class of the path, and store it. Delete homotopy classes of duplicate paths.

[0027] S25. If no path homotopy class is found, use a breadth-first search (BFS) algorithm to find at least one path homotopy class, and finally record all unique path homotopy classes as follows. .

[0028] like Figure 3The diagram shows a non-repeating path homotopy class proposed in this invention. Through step S2, multiple non-repeating homotopy path block sequences are obtained. This invention refers to this homotopy path block sequence as a path homotopy class, characterized in that each path homotopy class has no obstacles except for the boundary. Subsequent illumination can be performed in independent path homotopy classes. like Figure 4 The diagram shown illustrates the intra-class shortest path finding method based on simulated sunlight illumination proposed in this invention; via the sun point Rays are emitted towards the vertices of convex polygons in the path homotopy class, and the vertices with the optimal cost are continuously filtered to find the shortest path within the class. Considering the complexity and efficiency of collision detection, this invention transforms obstacle collision detection into calculating the intersection of node rays and the ordered sequence of shared edges using a two-dimensional cross product algorithm. The relative geometric position of the light rays is such that the light rays can pass through the ordered sequence of shared edges in sequence if and only if the light rays can pass through the sequence in sequence. A line segment at the corresponding level in the middle is considered to meet the visibility condition if it does not intersect with the non-common boundary of the convex polygon. like Figure 5 The diagram shows multiple intra-class shortest paths proposed in this invention (only five are shown). By repeating the above steps, the five shortest intra-class paths can be extracted for easy observation, while the remaining intra-class shortest paths can be saved.

[0029] S3. For each homotopy class, perform solar selection and illumination sampling, calculate the intra-class shortest path for each homotopy class, and finally obtain the shortest path for all homotopy classes; specifically including: S31, Settings Record the cumulative path distance from all vertices to the starting point. The initial values ​​are all set to Extract the set of shared edges between adjacent convex polygons from each path homotopy class. ; S32. Set the priority queue set Initially, there is only a set of starting points and path endpoints. Father-son relationship Temporary assembly First, a solar selection is performed, based on... Cumulative path distance from all points in the set to the starting point Sort them and select them one by one according to their cost from smallest to largest; S33. Based on the principle of simulating sunlight, the illumination adopts a block-by-block approach, starting from the starting block and moving the starting point from... Delete the statement that uses the starting point as the point where the sun emits rays into the homotopy class along that path, and determine... Is it less than Record the minimum distance and determine the starting point. Is it possible to traverse all shared edges of the homotopy class along this path? Thus illuminating the target point At this point, we need to discuss two scenarios: like Less than The minimum distance recorded is obviously true. The initial value is And starting point Able to traverse all shared edges Illuminate the target point Then record the starting point. Its index block and total computation path cost They are all included in set P; like Less than The minimum distance recorded is obviously true. The initial value is And starting point It is not possible to directly traverse all shared edges of the homotopy class of this path. Thus illuminating the target point Then, iterate through all the vertices of the convex polygons before the farthest block that the ray can reach. Calculation starting point Distance to all vertices ,like Smaller than the vertex The minimum distance and starting point recorded Able to pass through and reach the top All shared edges Then update Then add these vertices to set S. Iterate through all vertices in set S, record their parent-child relationships, add them to set E, and then add all vertices in set S according to their parent-child relationships. Push size into priority queue set ; The two-dimensional cross product algorithm is used to calculate the relationship between the node rays and the ordered sequence of shared edges. The relative geometric position; if and only if light can pass through the ordered sequence of shared edges in sequence. When a line segment at the corresponding level does not intersect with the non-common boundary of a convex polygon, it is determined to meet the visibility condition; during this process, there is no need to perform collision detection on obstacles in the global environment.

[0030] S34, from the priority queue set Select all vertices other than the origin as ordinary suns Conduct simulated sunlight exposure to determine Is it less than Minimum recorded distance and sun Is it possible to traverse subsequent shared edges? Illuminate the target point At this point, we need to discuss three scenarios: like Greater than The recorded solar point The minimum distance proves that the sun point is... Since the sun point in point O has already been illuminated by less costly light while queuing, it is discarded. Select a new sun point from the priority queue set O; like Less than The recorded solar point Minimum distance, and sun point Able to cross subsequent shared edges Illuminate the target point Then record the sun point. Its index block and total computation path cost They are all included in set P; like Less than The recorded solar point Minimum distance, and sun point Cannot cross subsequent shared edges Illuminate the target point Then, iterate through all the vertices of the convex polygons before the farthest block that the ray can reach. Calculate the solar point. Distance to all vertices ,like Smaller than the vertex Minimum recorded distance and sun point Able to pass through the illumination to the vertex All shared edges Then update Then add these vertices to set S. Iterate through all vertices in set S, record their new parent-child relationships, update set E, and then add all vertices in set S according to their parent-child relationships. Push size into priority queue set ; S35. Repeat step S34, when... When the set is empty, traverse Given the path costs in the set, find the path with the shortest cost, based on... Backtracking using parent-child relationships yields the shortest path within the class. .

[0031] The core of this invention is based on Constrained Delaunay Triangulation (CDT): First, it utilizes CDT convex decomposition and greedy merging strategies to topologically transform a 2D free space map into a series of convex polygon regions, simplifying the map compared to traditional methods. Second, by setting a heuristic function, it can quickly enumerate and record each distinct path homotopy class on the resulting topological graph, overcoming the deficiency of traditional methods that only output a single topological path. Finally, simulating the principle of sunlight illumination, it performs discretized vertex sampling and visibility judgment within the path homotopy classes formed by the sequence of convex polygons, thereby efficiently and lightweightly solving for the intra-class strict shortest path of each path homotopy class. This method exhibits significant advantages in homotopy class partitioning and multi-track path planning efficiency, with high computational determinism and strong real-time performance, making it particularly suitable for global path planning of robots in complex static environments.

[0032] This invention is more suitable for complex environments compared to existing path planning methods.

[0033] All matters not covered in this invention are common knowledge.

[0034] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for planning a homotopic sunlight irradiation sampling path for a planar mobile robot, characterized in that, The method includes the following steps: S1. Divide the space to be searched into obstacle spaces. and free space The free space is divided into a finite number of triangular free region modules by using the constrained triangulation method. Then, the triangular free region modules are merged into convex polygon free region modules by the greedy merging method, and the region index of each convex polygon free region module is recorded. S2. Set up a heuristic function to expand from the starting block, continuously record the blocks traversed by the path and exclude parent and ancestor blocks to prevent looping, stop when the target block is reached and record the sequence of path blocks traversed as a path homotopy class; S3. For each homotopy class, perform solar selection and illumination sampling, calculate the intra-class shortest path for each homotopy class, and finally obtain the shortest path for all homotopy classes.

2. The method for homotopy-type sunlight irradiation sampling path planning for a planar mobile robot according to claim 1, characterized in that: Step S1 includes: S11. Divide the entire search space into obstacle modules. and free space module Setting the starting point for the robot's travel path and target point ; S12. Using constrained triangulation to divide the free space module The union of modules divided into countable triangular free regions; S13. Based on the principle of greedy merging, merge triangular regions that share boundaries and remain convex polygons after merging, ultimately obtaining the free space of convex polygons. .

3. The method for homotopy-type sunlight irradiation sampling path planning for a planar mobile robot according to claim 2, characterized in that: Step S12 includes: extracting the coordinates of all vertices and the positions of obstacle modules in the search space, as well as the constraint relationships of the space boundary; using constrained Delaunay triangulation to hollow out the obstacle modules to perform triangulation, thereby dividing the space into a countable number of triangular free spaces.

4. The method for homotopy-type sunlight irradiation sampling path planning for a planar mobile robot according to claim 2, characterized in that: Step S13 includes: Given the large number of free triangular regions, to simplify the topological space, a greedy merging strategy is adopted. All free triangular regions and their shared edges are traversed. Free triangular regions with shared edges that remain convex polygons after merging are continuously merged until all convex polygons in the topological map can no longer be merged. The region index of each convex polygon module is recorded to obtain the free space of the convex polygons. .

5. The method for homotopy-type sunlight irradiation sampling path planning for a planar mobile robot according to claim 1, characterized in that: Step S2 includes: S21, Extract Records For each convex polygon, there is a set of coordinate relationships between its vertices. For each convex polygon, there is a set of relationships between adjacent convex polygons, and this set records the relationships between them. The starting block With inclusion target block ; S22. Set up a heuristic function to first calculate the distance from the geometric center of all convex polygon modules to the starting point and the target point. Then, expand the module block by block starting from the starting point. Record the non-parent adjacent modules of the starting block set ;like At that time, with The probability is used to select the block with the minimum path cost for expansion; otherwise, the probability is used to select the block with the minimum path cost for expansion. The probability is randomly selected, and blocks on the path are continuously recorded; S23, Set of Non-Parent Adjacency Modules Besides not including the parent block, the block also cannot include ancestor blocks to prevent wraparound paths; if When the middle is an empty set, that is, the path is not working, and it is necessary to return to the starting block to search. When the target block is reached, the path block sequence is found and the path block sequence is recorded as a path homotopy class. S24. Set weight coefficients probability parameters ; Iterate through all cases, calculate the homotopy class of the path and store it, then delete the homotopy class of duplicate paths; S25. If no path homotopy class is found, a breadth-first search algorithm is used to find at least one path homotopy class. Finally, all unique path homotopy classes are recorded as follows. .

6. The method for homotopy-type sunlight irradiation sampling path planning for a planar mobile robot according to claim 5, characterized in that: In step S22, the cost function for each path module is calculated based on the heuristic function as follows: in, This is the cost function for the driving path module; This is the distance from the geometric center of the current convex polygon module to the starting point; This is the distance from the geometric center of the current convex polygon module to the target point; for The weight parameters, The larger the value, the faster the convergence to the target point; Let be the probability parameters of the heuristic function. The larger the value, the more certain the choice; otherwise, the more random the choice.

7. The method for homotopy-type sunlight irradiation sampling path planning for a planar mobile robot according to claim 1, characterized in that: Step S3 includes: S31, Settings Record the cumulative path distance from all vertices to the starting point. The initial values ​​are all set to Extract the set of shared edges between adjacent convex polygons from each path homotopy class. ; S32. Set the priority queue set Initially, there is only a set of starting points and path endpoints. Father-son relationship Temporary assembly First, solar selection is performed, based on... Cumulative path distance from all points in the set to the starting point Sort them and select them one by one according to their cost from smallest to largest; S33. Based on the principle of simulating sunlight, the illumination adopts a block-by-block approach, starting from the starting block and moving the starting point from... Delete the statement that uses the starting point as the point where the sun emits rays into the homotopy class along that path, and determine... Is it less than Record the minimum distance and determine the starting point. Is it possible to traverse all shared edges of the homotopy class along this path? Thus illuminating the target point At this point, we need to discuss two scenarios: like Less than The minimum distance recorded is obviously true. The initial value is And starting point It is possible to traverse all shared edges of the homotopy class of this path. Illuminate the target point If the condition is met (visibility is satisfied), then the starting point is recorded. Its index block and total computation path cost They are all included in set P; like Less than The minimum distance recorded is obviously true. The initial value is And starting point It is not possible to directly traverse all shared edges of the homotopy class of this path. Illuminate the target point If the visibility condition is not met, then iterate through all vertices of the convex polygons in front of the farthest block that can be reached by the ray. ; Calculation starting point Distance to all vertices ,like Smaller than the vertex The minimum distance and starting point recorded Meeting the visibility condition allows light to pass through to the vertex. All shared edges Then update And add these vertices to set S; traverse all vertices in set S, record their parent-child relationships and add them to set E, and then sort all vertices in set S according to their parent-child relationships. Push size into priority queue set ; S34, from the priority queue set Select all vertices other than the origin as ordinary suns Conduct simulated sunlight exposure to determine Is it less than Minimum recorded distance and sun Is it possible to traverse subsequent shared edges? Illuminate the target point At this point, we need to discuss three scenarios: like Greater than The recorded solar point The minimum distance proves that the sun point is... Since the sun point in point O has already been illuminated by less costly light while queuing, it is discarded. Select a new sun point from the priority queue set O; like Less than The recorded solar point Minimum distance, and sun point Able to cross subsequent shared edges Illuminate the target point Then record the sun point. Its index block and total computation path cost They are all included in set P; like Less than The recorded solar point Minimum distance, and sun point Cannot cross subsequent shared edges Illuminate the target point Then, iterate through all the vertices of the convex polygons before the farthest block that the ray can reach. ; Calculate the solar point Distance to all vertices ,like Smaller than the vertex Minimum recorded distance and sun point Able to pass through the illumination to the vertex All shared edges Then update Then add these vertices to set S; iterate through all vertices in set S, record their new parent-child relationships, update set E, and then add all vertices in set S according to their parent-child relationships. Push size into priority queue set ; S35. Repeat step S34, when... When the set is empty, traverse Given the path costs in the set, find the path with the shortest cost, based on... Backtracking through parent-child relationships yields the intra-class shortest path for each homotopy class. .

8. The method for homotopy-type sunlight irradiation sampling path planning for a planar mobile robot according to claim 7, characterized in that: Step S33 includes: using a two-dimensional cross product algorithm to calculate the relationship between the node rays and the ordered sequence of shared edges. The relative geometric position; if and only if light can pass through the ordered sequence of shared edges in sequence. When a line segment at the corresponding level does not intersect with the non-common boundary of a convex polygon, it is determined to meet the visibility condition; during this process, there is no need to perform collision detection on obstacles in the global environment.

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