Full-coverage path planning method for leveling robot

Abstract

The invention discloses a full-coverage path planning method for a leveling robot. The full-coverage path planning method comprises the following steps: acquiring an environment grid map containing an impassable area; calling a priority starting point moving local path planning algorithm in the current sub-region, and guiding the robot to complete full-coverage operation in the region to reach a dead point of the current sub-region; performing state judgment on adjacent grids in the advancing process of the robot, and if edge coverage conditions are met, adding a target point list; unreasonable or redundant target points are removed according to a preset screening criterion; selecting a target point closest to the current dead point in Euclidean distance as an entry point of the next sub-region, and calling a region connection path planning algorithm to improve an A * algorithm to generate a connection path; moving to a target point along the planned path, updating the current position to the point, restarting the local coverage process, and performing loop execution; if the current target point list is empty, all reachable areas are accessed, and all coverage is completed.

Description

Technical Field

[0001] The present invention belongs to the technical field of robot path planning, and in particular relates to a full-coverage path planning method for a leveling robot. Background Art

[0002] In recent years, with the rapid development of artificial intelligence algorithms and application technologies, service-oriented mobile robots have gradually been widely used in various scenarios. Leveling robots, as a typical example, have demonstrated significant advantages in areas such as ground construction and floor paving due to their automated and intelligent features. Especially in indoor working environments, leveling robots can not only replace manual labor to complete the heavy and high-precision leveling tasks, but also effectively improve construction efficiency and quality. However, due to the lack of stable Global Navigation Satellite System (GNSS) signals in indoor environments, the autonomous navigation and path planning of leveling robots still face many challenges. Therefore, the research on efficient, stable, and highly adaptable path planning algorithms has become one of the key technologies to promote the practical application and intelligent development of leveling robots.

[0003] Mobile robot path planning can be broadly categorized into two types: First, the robot autonomously plans an optimal path from a starting point to a destination, ensuring the shortest path, the shortest time, and safety and collision-free travel. Second, the robot performs comprehensive traversal path planning in two-dimensional space, aiming to maximize operational efficiency, minimize path duplication, and effectively avoid obstacles. Numerous researchers, both domestically and internationally, have conducted in-depth research on mobile robot path planning, proposing and optimizing a large number of algorithms. These algorithms include traditional path planning methods such as A*, Dijkstra, rapidly expanding random trees (RRT), probabilistic roadmaps (PRM), and artificial potential fields (APF), as well as intelligent optimization strategies such as genetic algorithms (GAs), artificial fish swarm algorithms (AFS), particle swarm optimization (PSO), and ant colony optimization (ACO). Among these path planning algorithms, the A* algorithm, a classic heuristic search method, holds a prominent position in mobile robot path planning research due to its high computational efficiency, strong path search capabilities, and guaranteed optimal solution. The A* algorithm uses a comprehensive evaluation combining actual and estimated costs to effectively guide the search process, reduce unnecessary node expansion, and improve search efficiency. Due to its clear algorithmic structure, simple implementation, and ease of integration with technologies such as grid maps and graph search, the A* algorithm has been widely used in various path planning tasks, demonstrating exceptional stability and reliability in static environments, making it one of the most widely used algorithms in autonomous robot navigation systems. However, existing environmental coverage algorithms suffer from path redundancy, inescapable dead spots, and missed sub-region coverage in complex scenarios. Summary of the Invention

[0004] The purpose of the present invention is to solve the above problems and provide a full coverage path planning method for a leveling robot that combines a multi-directional priority control strategy with dynamic A* path planning optimization to improve the operating efficiency and coverage integrity of the mobile robot.

[0005] To solve the above technical problems, the technical solution of the present invention is: a full coverage path planning method for a leveling robot, comprising the following steps:

[0006] S1. Obtain an environmental grid map containing impassable areas;

[0007] S2: Invoke the priority starting point moving local path planning algorithm (PMTS algorithm) in the current sub-area to guide the robot to complete the full coverage operation in the area until it reaches the dead point of the current sub-area;

[0008] S3. During the robot's movement, the robot determines the status of the adjacent grids. If the grids meet the coverage edge condition, they are added to the target point list.

[0009] S4. Eliminate unreasonable or redundant target points based on preset screening criteria to improve the efficiency of subsequent path generation;

[0010] S5. Select the target point with the shortest Euclidean distance to the current dead point as the entry point of the next sub-region, and call the improved A* algorithm of the regional connection path planning algorithm to generate a connection path;

[0011] S6. Move along the planned path to the target point, update the current position to that point, and start the local coverage process again, executing the cycle in a loop;

[0012] S7. If the current target point list is empty, it means that all reachable areas have been visited, and all coverage is completed.

[0013] Furthermore, the PMTS algorithm in S2 involves eight movement directions: east, south, west, north, southeast, northeast, southwest and northwest, and sets three direction priority rules. The first rule indicates that the priority of east, south, west and north is higher than that of southeast, northeast, southwest and northwest; the second rule points out that in the path planning process, if there are multiple feasible directions, the direction towards the starting position should be given priority to effectively reduce the redundancy of the path; the third rule further clarifies that in the determination of specific directions, the horizontal direction has a higher priority than the vertical direction, which helps to improve the overall efficiency and consistency of path planning.

[0014] Furthermore, during the local path planning process in S2, the robot scans the grid status in eight directions around it in real time, identifies uncovered and passable grid cells, and adds them to the target point candidate list; wherein, the candidate target point must be located at the edge or boundary grid of the uncovered area and meet the predefined conditions of the edge grid; if the grids around a candidate point are all covered areas or obstacles, it is deemed that it does not have edge attributes and is eliminated to avoid redundant coverage and path breakage; among the retained candidate target points, the target point with the smallest Euclidean distance to the current dead point is selected as the target position of the next connecting path, taking into account both path optimization and computational efficiency.

[0015] Furthermore, the regional connection path planning algorithm in S5 is a weighted average heuristic function method that combines Manhattan distance and Euclidean distance; the traditional A* algorithm will expand some redundant nodes when performing path search. This phenomenon is due to the fact that the heuristic function h^*(n) cannot be flexibly adjusted according to the real-time search process; when the estimated cost value is lower than the actual cost, the number of nodes expanded by the algorithm increases significantly, resulting in a decrease in search efficiency. At this time, the weight value of h^*(n) needs to be increased to speed up the calculation speed; when the estimated cost value h^*(n) is equal to the actual cost value, only necessary nodes are traversed; if the estimated cost value is higher than the actual cost, although the search speed is improved, the optimal path is easily missed, so the weight should be reduced to balance path quality and efficiency.

[0016] Furthermore, the PMTS algorithm in S2 includes the following sub-steps:

[0017] S21. Initialize the environment and parameters: The leveling robot loads the environment space model M, initializes the current position current_pos, grid size l, step size ε, and local coverage path Local Coverage Path;

[0018] S22, environmental information acquisition: Use sensors to obtain current environmental data, update the grid status and synchronously update the environmental space model M to ensure a true reflection of the environment and avoid path deviation or coverage omission caused by delayed environmental information;

[0019] S23. Determine the movable direction: Determine whether the movement is possible based on the grid status; free and uncovered grids are considered feasible directions, while covered or obstructed grids are considered infeasible directions; all movable directions of the current position are determined based on the information obtained in step S22;

[0020] S24, detect feasible path: check whether there is a direction in which movement is possible; if not, jump to step S27; if there is a feasible direction, proceed to the next step;

[0021] S25, priority direction selection: According to the priority rule of the PMTS algorithm, the optimal direction is selected from all feasible directions, with priority given to the direction toward the starting point and the horizontal direction;

[0022] S26, execute movement: the leveling robot moves once in the selected direction and step length, and returns to step S22 after completion, and continues to update the environment information and make judgments;

[0023] S27. Local coverage completed: When the leveling robot reaches the end of the current area or a position where it cannot move forward, the local coverage task ends and the path result Local Coverage Path is output.

[0024] Furthermore, the selection principle of the target point selection mechanism of the screening criteria in S4 is as follows: a target point list is established, and while performing local area coverage, the robot will scan the grid status in the eight surrounding directions, screen out the grid units that have not been covered and are in a passable state, and record them in the target point list as candidate points for subsequent path planning; if a target point has been covered by the robot, it should be immediately removed from the list to ensure the accuracy of the data; at the same time, the target points selected in the regional connection path planning should also be removed from the list to optimize the distribution of the target points.

[0025] Furthermore, the elimination of unreasonable or redundant target points in S4 is the screening of target points. The target points should be selected from the edge positions of the uncovered area. If the left, right, front, back, or diagonal adjacent grids of a node are free and uncovered at the same time, the node can be considered as not having the corner feature.

[0026]

[0027] ρ(m)=μ(d1,d5)+μ(d2,c6)+μ(d3,d7)+μ(d4,d8)

[0028] When ρ(m) ≥ 1, it means that the node is located in a continuous free area and has no boundary features. Therefore, it is judged as an invalid target point and should be removed from the candidate set. When ρ(m) = 0, it means that there are no two free and uncovered grids in its horizontal, vertical, or diagonal direction. In this case, the grid is at a corner position and can be retained as a reference target point for subsequent path planning.

[0029] Furthermore, the specific steps of the A* algorithm in S5 are as follows:

[0030] S51. Set the starting point to S and the end point to G. Initialize a list called setopenlist to store candidate nodes that have not yet been expanded, and create setclosedlist to record nodes that have been visited. The starting node S will be added to setopenlist first, and setclosedlist is still empty at this time, making preliminary preparations for the path search process.

[0031] S52, expand the current parent node, add all its reachable adjacent child nodes to setopenlist, and move the parent node itself to setclosedlist, indicating that the node has been visited;

[0032] S53, check whether setopenlist is empty. If setopenlist is empty, it means that no feasible path can be found during the search process, and the program terminates; otherwise, proceed to the next step;

[0033] S54, calculating the cost value of the current expandable child node according to the A* evaluation function, and selecting the child node with the smallest evaluation value as the new parent node;

[0034] S55. Determine whether the currently selected parent node is the target point G. If so, backtrack from the target point back to the starting point to generate the final path, and the search ends. If not, return to step S52 to continue expanding.

[0035] S56. Mark the current parent node as part of the path and add it to setclosedlist;

[0036] S57, backtracking the nodes in setclosedlist, integrating to obtain the planned path, and completing the path generation process.

[0037] The beneficial effects of the present invention are:

[0038] 1. The present invention provides a full-coverage path planning method for a leveling robot and an optimization algorithm for local area coverage: the PMTS (Prioritize Moving Towards Starting) algorithm is introduced to define eight moving directions (N, S, E, W, NE, NW, SE, SW) and set three-level priority rules to guide the robot to move preferentially toward the starting point and horizontally, thereby efficiently bypassing obstacles and reducing missed areas.

[0039] 2. During the local coverage process, the present invention dynamically generates a list of uncovered edge grid target points, and uses a structural screening mechanism to determine the state of the neighboring grids and eliminate redundant points. The optimal connection target point is selected in combination with the Euclidean distance.

[0040] 3. The present invention adopts an improved A* algorithm to guide the robot's joint movement between sub-areas, optimizes the heuristic function calculation method, combines Manhattan and Euclidean distances, and introduces a dynamic weight adjustment strategy to control the search depth and path smoothness.

[0041] 4. By cyclically calling the local coverage and regional connection algorithms, the robot can start from the starting point and completely cover all areas, and output the complete path trajectory.

[0042] 5. The present invention has a small number of sub-areas, a smoother coverage path, a low number of turns, and significantly improved path search efficiency. At the same time, the coverage completeness rate is significantly improved, and the first coverage ratio is increased. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is a flow chart of a full coverage path planning method for a leveling robot according to the present invention;

[0044] Figure 2 It is the flow chart of PMTS algorithm of the present invention;

[0045] Figure 3 This is a simulation comparison diagram of the PMTS algorithm and the BCD algorithm in the present invention;

[0046] Figure 4 This is the target point screening principle in the present invention;

[0047] Figure 5 Schematic diagram of grid marking in the present invention;

[0048] Figure 6 This is a flow chart of the A* algorithm in the present invention;

[0049] Figure 7 A diagram showing the calculation method for optimizing the heuristic function distance in the A* algorithm of the present invention;

[0050] Figure 8 A comparison chart of simulation results of the A* algorithm and the improved A* algorithm in the present invention;

[0051] Figure 9 This is the simulation result of the full coverage path planning method. DETAILED DESCRIPTION

[0052] The present invention will be further described below with reference to the accompanying drawings and specific embodiments:

[0053] like Figures 1 to 8 As shown, the present invention provides a full coverage path planning method for a leveling robot, comprising the following steps:

[0054] S1. Obtain an environmental grid map containing inaccessible areas.

[0055] In this example, the spatial grid model M of the current working environment is loaded and relevant variables are initialized. These variables include the starting point q_start, the leveling robot step length ε, the grid size l, the target point list target_points, and the Full Coverage Path used to store the final trajectory. The starting point is set to the current position current_pos, preparing to begin the coverage task.

[0056] S2. Call the priority starting point moving local path planning algorithm, namely the PMTS algorithm, in the current sub-area to guide the robot to complete the full coverage operation in the area until it reaches the dead point of the current sub-area.

[0057] The dead point in step S2 refers to the eight grids around the robot's position being covered grids, obstacle grids, or boundaries, and can be determined as the dead point position.

[0058] The PMTS algorithm in step S2 involves eight directions of movement: east, south, west, north, southeast, northeast, southwest and northwest, and sets three direction priority rules. The first rule indicates that the priority of east, south, west and north is higher than that of southeast, northeast, southwest and northwest. The second rule points out that in the process of path planning, if there are multiple feasible directions, the direction towards the starting position should be given priority to effectively reduce the redundancy of the path. The third rule further clarifies that in the determination of specific directions, the horizontal direction has a higher priority than the vertical direction, which helps to improve the overall efficiency and consistency of path planning. In this embodiment, the horizontal direction is east or west, and the vertical direction is north or south.

[0059] During the local path planning process in step S2, the robot scans the grid status in eight directions around it in real time, identifies the grid cells that are not covered and are in a passable state, and adds them to the target point candidate list. Among them, the candidate target point must be located at the edge or boundary grid of the uncovered area and meet the predefined conditions of the edge grid. The predefined conditions in this step S2 refer to: if the left, right, front, back and diagonal adjacent grids of a node are all free and uncovered at the same time, then the node can be regarded as not having corner characteristics. Among the retained candidate target points, the target point with the smallest Euclidean distance to the current dead point is selected as the target position of the next connecting path, taking into account both path optimization and computational efficiency.

[0060] The PMTS algorithm in step S2 includes the following sub-steps:

[0061] S21. Initialize the environment and parameters: The leveling robot loads the environment space model M, initializes the current position current_pos, grid size l, step size ε, and local area coverage path Local Coverage Path.

[0062] S22. Environmental information acquisition: Use sensors to obtain current environmental data, update the grid status and synchronously update the environmental space model M to ensure a true reflection of the environment and avoid path deviation or coverage omissions caused by delayed environmental information.

[0063] S23. Determine the possible directions of movement: Determine whether movement is possible based on the grid status. Free, uncovered grids are considered possible directions, while covered or obstructed grids are considered infeasible directions. Using the information obtained in step S22, all possible directions of movement at the current position are determined.

[0064] S24, Detection of feasible paths: Check whether there is a direction in which movement is possible. If not, jump to step S27; if there is a feasible direction, proceed to the next step.

[0065] S25. Priority direction selection: According to the priority rules of the PMTS algorithm, the optimal direction is selected from all feasible directions, with priority given to the direction toward the starting point and the horizontal direction.

[0066] S26. Execute movement: The leveling robot moves once in the selected direction and step length, and after completion returns to step S22 to continue updating the environmental information and making judgments.

[0067] S27. Local coverage completed: When the leveling robot reaches the end of the current area or a position where it cannot move forward, the local coverage task ends and the path result Local Coverage Path is output.

[0068] The simulation results of PMTS algorithm and BCD algorithm are as follows Figure 3 As shown, Figure 3 (a) is the simulation result of PMTS algorithm, and (b) is the simulation result of BCD algorithm.

[0069] In this embodiment, the PMTS local path planning algorithm is started. In the process of guiding the robot to complete coverage in the current sub-area, the sensor continuously collects surrounding environment information and updates the spatial model.

[0070] S3. During the robot's movement, the robot judges the status of the adjacent grids. If the coverage edge condition is met, the grid is added to the target point list.

[0071] After the robot reaches the dead point of the area, the local path planning phase is completed.

[0072] S4. Eliminate unreasonable or redundant target points based on preset screening criteria to improve the efficiency of subsequent path generation.

[0073] The selection principle of the target point selection mechanism of the screening criterion in step S4 is:

[0074] A target point list is created. While covering a local area, the robot scans the grid in eight directions, identifying uncovered and traversable grid cells. These cells are then added to the target point list as candidate points for subsequent path planning. If a target point has already been covered by the robot, it should be removed from the list immediately to ensure data accuracy. Furthermore, target points selected during regional connection path planning should also be removed from the list to optimize target point distribution.

[0075] Eliminating unreasonable or redundant target points in step S4 is the screening of target points. The target points should be selected from the edge positions of the uncovered area. If the left, right, front, back, or diagonal adjacent grids of a node are free and uncovered at the same time, the node can be regarded as not having corner characteristics, such as Figure 4 shown.

[0076]

[0077] ρ(m)=μ(d1,d5)+μ(d2,c6)+μ(d3,d7)+μ(d4,d8)

[0078] In which, we define μ(d i ,d j ) describes the state relationship between these grids, where the value range of i and j is 1-8, m is the grid where the robot is currently located, and d i and d j Number the eight grids around the robot, such as Figure 5 As shown in Figure 2, the discriminant function ρ(m) is introduced to determine whether the grid m meets the selection conditions of the edge target point.

[0079] When ρ(m) ≥ 1, it indicates that the node is located in a continuous free area and does not have boundary features. Therefore, it is judged as an invalid target point and should be removed from the candidate set. When ρ(m) = 0, it means that there are no two free uncovered grids in its horizontal, vertical, or diagonal direction. In this case, the grid is at a corner position and can be retained as a reference target point for subsequent path planning.

[0080] The target point is selected by selecting the location with the smallest Euclidean distance from the current dead point as the end point of the next connecting path.

[0081] S5. Select the target point with the shortest Euclidean distance to the current dead point as the entry point of the next sub-area, and call the regional connection path planning algorithm to improve the A* algorithm to generate a connection path.

[0082] The regional connection path planning algorithm in step S5 is a weighted average heuristic function method that combines Manhattan distance and Euclidean distance. The traditional A* algorithm will expand some redundant nodes when performing path search. This phenomenon is due to the fact that the heuristic function h^*(n) cannot be flexibly adjusted according to the real-time search process. When the estimated cost value is lower than the actual cost, the number of nodes expanded by the algorithm increases significantly, resulting in a decrease in search efficiency. At this time, the weight value of h^*(n) needs to be increased to speed up the calculation. When the estimated cost value h^*(n) is equal to the actual cost value, only necessary nodes are traversed. If the estimated cost value is higher than the actual cost, although the search speed is improved, it is easy to miss the optimal path. Therefore, the weight should be reduced to balance the path quality and efficiency.

[0083] The principle of the A* algorithm: The A* algorithm introduces two key cost values: one is the actual cost from the current point n to the starting point, and the other is the estimated cost from the current point n to the target point. The method of selecting the next child node is determined by summing these two costs. The A* algorithm evaluation function is calculated as follows:

[0084] f(n)=g(n)+h(n)

[0085] Where g(n) is the known shortest path from the start node to node n; h(n) is a heuristic estimate of the cost from node n to the target node.

[0086] like Figure 5 As shown, the specific steps of the A* algorithm in step S5 are as follows:

[0087] S51. Set the starting point to S and the end point to G; initialize a list called setopenlist to save the candidate nodes that have not yet been expanded, and create setclosedlist to record the nodes that have been visited; the starting node S will be added to setopenlist first, and setclosedlist is still empty at this time, making preliminary preparations for the path search process.

[0088] S52. Expand the current parent node, add all its reachable adjacent child nodes to setopenlist, and move the parent node itself to setclosedlist, indicating that the node has been visited.

[0089] S53. Check whether setopenlist is empty. If setopenlist is empty, it means that no feasible path can be found during the search process and the program terminates; otherwise, proceed to the next step.

[0090] S54 , calculating the cost of the current expandable child node according to the A* evaluation function, and selecting the child node with the smallest evaluation value as the new parent node.

[0091] S55. Determine whether the currently selected parent node is the target point G. If so, backtrack from the target point back to the starting point to generate the final path, and the search ends. If not, return to step S52 to continue expanding.

[0092] S56. Mark the current parent node as part of the path and add it to setclosedlist.

[0093] S57, backtracking the nodes in setclosedlist, integrating to obtain the planned path, and completing the path generation process.

[0094] like Figure 6 As shown in , the curved line in the middle of the triangle represents the improved heuristic function path. Figure 6 The intersection point of the angle bisectors of ∠BAC and ∠BCA is X. The lengths of the dashed segments OX, HX, and HI are equal. According to the Pythagorean theorem and the angle bisector theorem, we can obtain:

[0095]

[0096] Then the improved heuristic function calculation expression is:

[0097]

[0098] When the estimated cost is lower than the actual cost, the number of nodes expanded by the algorithm increases significantly, resulting in a decrease in search efficiency. In this case, it is necessary to increase h appropriately. * (n) to speed up the calculation. * When (n) is equal to the actual cost, the search path is optimal, traversing only necessary nodes. If the estimated value is higher than the actual cost, the search speed will increase, but the optimal path will be missed. Therefore, the weight should be appropriately reduced to balance path quality and efficiency. The improved evaluation function calculation method is as follows:

[0099]

[0100] Where: f(n) is the total cost from the starting point to the target point; g(n) is the actual cost from the current node to the starting point; h * (n) is the estimated cost from the current node to the target node; d is the distance between the current node and the target point; and D is the distance from the starting point to the target point.

[0101] The simulation results of A* and improved A* algorithms are shown in Figure 7 As shown, Figure 7(a) shows the simulation results of the A* algorithm, and (b) shows the simulation results of the improved A* algorithm. In this embodiment, the A* algorithm generates connection paths by using the improved regional connection path planning algorithm, which is a calculation method that optimizes the heuristic function. By introducing dynamic weight coefficients, the computational complexity and runtime are reduced, thereby improving the efficiency of the leveling robot operation.

[0102] S6. Move along the planned path to the target point, update the current position to the target point, and start the local coverage process again, and execute it in a loop.

[0103] S7. If the current target point list is empty, it means that all reachable areas have been visited, and all coverage is completed.

[0104] In this embodiment, if the current target point list target_points is empty, it means that all reachable areas have been visited, and the program jumps to the next step, which is to output the robot's entire movement trajectory as the Full Coverage Path, and the full coverage path planning is completed. Otherwise, it proceeds to step S5.

[0105] The principle of this invention is to use the PMTS algorithm to cover an obstructed map until a dead point is reached. Using a target point selection mechanism, the most suitable target point in the uncovered area is selected, and a modified A* algorithm is activated to determine the optimal path between the dead point and the target point. After reaching the uncovered area, the PMTS algorithm continues to cover the entire environment until it reaches full coverage.

[0106] The core goal of the full coverage path planning algorithm is to plan an optimal path for the robot, starting from the initial position, that covers all traversable areas in the environment while minimizing the total distance. Figure 8 shown.

[0107] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific descriptions and embodiments. Those skilled in the art can make various other specific variations and combinations based on the technical teachings disclosed in the present invention without departing from the essence of the present invention, and such variations and combinations are still within the scope of protection of the present invention.

Claims

1. A full coverage path planning method for a leveling robot, characterized in that: The following steps are involved: S1. Obtain an environmental grid map containing impassable areas; S2: Invoke the priority starting point moving local path planning algorithm (PMTS algorithm) in the current sub-area to guide the robot to complete the full coverage operation in the area until it reaches the dead point of the current sub-area; S3. During the robot's movement, the robot determines the status of the adjacent grids. If the grids meet the coverage edge condition, they are added to the target point list. S4. Eliminate unreasonable or redundant target points based on preset screening criteria to improve the efficiency of subsequent path generation; S5. Select the target point with the shortest Euclidean distance to the current dead point as the entry point of the next sub-region, and call the improved A* algorithm of the regional connection path planning algorithm to generate a connection path; S6. Move along the planned path to the target point, update the current position to that point, and start the local coverage process again, executing the cycle in a loop; S7. If the current target point list is empty, it means that all reachable areas have been visited, and all coverage is completed.

2. A full coverage path planning method for a leveling robot according to claim 1, characterized in that: The PMTS algorithm in S2 involves eight movement directions: east, south, west, north, southeast, northeast, southwest and northwest, and sets three direction priority rules. The first rule indicates that the priority of east, south, west and north is higher than that of southeast, northeast, southwest and northwest; the second rule points out that in the path planning process, if there are multiple feasible directions, the direction towards the starting position should be given priority to effectively reduce the redundancy of the path; the third rule further clarifies that in the determination of specific directions, the horizontal direction has a higher priority than the vertical direction.

3. The full coverage path planning method of a leveling robot according to claim 1, characterized in that: During the local path planning process in S2, the robot scans the grid status in eight directions around it in real time, identifies uncovered and passable grid cells, and adds them to the target point candidate list; wherein, the candidate target point must be located at the edge or boundary grid of the uncovered area and meet the predefined conditions of the edge grid; if the grids around a candidate point are all covered areas or obstacles, it is determined that it does not have edge attributes and is eliminated to avoid redundant coverage and path breakage; among the retained candidate target points, the target point with the smallest Euclidean distance to the current dead point is selected as the target position of the next connecting path.

4. The full coverage path planning method of a leveling robot according to claim 1, characterized in that: The regional connection path planning algorithm in S5 is a weighted average heuristic function method that combines Manhattan distance and Euclidean distance. The traditional A* algorithm will expand some redundant nodes when performing path search. This phenomenon is due to the fact that the heuristic function h^*(n) cannot be flexibly adjusted according to the real-time search process. When the estimated cost value is lower than the actual cost, the number of nodes expanded by the algorithm increases significantly, resulting in a decrease in search efficiency. At this time, the weight value of h^*(n) needs to be increased to speed up the calculation. When the estimated cost value h^*(n) is equal to the actual cost value, only necessary nodes are traversed. If the estimated cost value is higher than the actual cost, although the search speed is improved, the optimal path is easily missed.

5. The full coverage path planning method of a leveling robot according to claim 1, characterized in that: The PMTS algorithm in S2 includes the following steps: S21. Initialize the environment and parameters: The leveling robot loads the environment space model M, initializes the current position current_pos, grid size l, step size ε, and local coverage path Local Coverage Path; S22, environmental information acquisition: Use sensors to obtain current environmental data, update the grid status and synchronously update the environmental space model M to ensure a true reflection of the environment and avoid path deviation or coverage omission caused by delayed environmental information; S23. Determine the movable direction: Determine whether the movement is possible based on the grid status; free and uncovered grids are considered feasible directions, while covered or obstructed grids are considered infeasible directions; all movable directions of the current position are determined based on the information obtained in step S22; S24, detect feasible path: check whether there is a direction in which movement is possible; if not, jump to step S27; If there is a feasible direction, proceed to the next step; S25, priority direction selection: According to the priority rule of the PMTS algorithm, the optimal direction is selected from all feasible directions, with priority given to the direction toward the starting point and the horizontal direction; S26, execute movement: the leveling robot moves once in the selected direction and step length, and returns to step S22 after completion, and continues to update the environment information and make judgments; S27. Local coverage completed: When the leveling robot reaches the end of the current area or a position where it cannot move forward, the local coverage task ends and the path result Local Coverage Path is output.

6. The full coverage path planning method of a leveling robot according to claim 1, characterized in that: The selection principle of the target point selection mechanism of the screening criterion in S4 is as follows: a target point list is established. While performing local area coverage, the robot scans the grid status in eight directions around it, screens out the grid cells that have not been covered and are in a passable state, and records them in the target point list as candidate points for subsequent path planning; If a target point has been covered by a robot, it should be removed from the list immediately to ensure data accuracy; at the same time, the target points selected in the regional connection path planning should also be removed from the list to optimize the distribution of target points.

7. The full coverage path planning method of a leveling robot according to claim 1, characterized in that: Eliminating unreasonable or redundant target points in S4 is the screening of target points. Target points should be selected from the edge of the uncovered area. If the left, right, front, and back, or diagonal adjacent grids of a node are free and uncovered at the same time, the node can be considered as not having corner characteristics. ρ(m)=μ(d1,d5)+μ(d2,c6)+μ(d3,d7)+μ(d4,d8) When ρ(m) ≥ 1, it means that the node is located in a continuous free area and has no boundary features. Therefore, it is judged as an invalid target point and should be removed from the candidate set. When ρ(m) = 0, it means that there are no two free and uncovered grids in its horizontal, vertical, or diagonal direction. In this case, the grid is at a corner position and can be retained as a reference target point for subsequent path planning.

8. The full coverage path planning method of a leveling robot according to claim 1, characterized in that: The specific steps of the A* algorithm in S5 are as follows: S51. Set the starting point to S and the end point to G. Initialize a list called setopenlist to store candidate nodes that have not yet been expanded, and create setclosedlist to record nodes that have been visited. The starting node S will be added to setopenlist first, and setclosedlist is still empty at this time, making preliminary preparations for the path search process. S52, expand the current parent node, add all its reachable adjacent child nodes to setopenlist, and move the parent node itself to setclosedlist, indicating that the node has been visited; S53, check whether setopenlist is empty. If setopenlist is empty, it means that no feasible path can be found during the search process, and the program terminates; otherwise, proceed to the next step; S54, calculating the cost value of the current expandable child node according to the A* evaluation function, and selecting the child node with the smallest evaluation value as the new parent node; S55. Determine whether the currently selected parent node is the target point G. If so, backtrack from the target point back to the starting point to generate the final path, and the search ends. If not, return to step S52 to continue expanding. S56. Mark the current parent node as part of the path and add it to setclosedlist; S57, backtracking the nodes in setclosedlist, integrating to obtain the planned path, and completing the path generation process.

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