A Two-Stage Adaptive Global Path Planning Method for Complex Terrains

Abstract

The present invention discloses a two-stage adaptive global path planning method for complex terrains. Taking the grid cells corresponding to the finest resolution as units, it calculates the movement cost by integrating slope, surface cover, roughness, and terrain undulation; determines the passage cost of the area; uses the path planned in the first stage as the reference path for the second stage, comprehensively considering the issues of efficiency and accuracy in the second path planning stage, adopts an adaptive resolution mechanism, and uses adaptive path end point judgment for each intermediate segment in the second stage, enhancing the flexibility and scalability of the path and achieving accurate and rapid path planning for complex terrains.

Description

Technical Field

[0001] The present invention relates to the field of path planning, and particularly to a two-stage adaptive global path planning method for complex terrains. Background Art

[0002] With the continuous expansion of the application fields of mobile robots, their working environments are no longer limited to simple indoor places, and gradually begin to expand to complex outdoor terrains. When mobile robots work in complex terrain environments, they can replace humans to complete dangerous or heavy tasks, which has significant benefits. In complex environments such as mountains, deserts or disaster areas, robots can perform exploration, rescue or material transportation tasks, avoiding humans from being exposed to risks such as cliffs and landslides and ensuring safety. At the same time, they can continuously operate without being limited by fatigue, replacing humans to complete long-term patrols or data collection to improve efficiency. In addition, robots can also work in harsh climates or toxic environments, such as volcanic monitoring or chemical leak detection, replacing humans to avoid health risks, thereby improving the success rate of tasks and reducing labor costs. Path planning in complex terrain environments refers to finding a feasible and optimal path from the starting point to the ending point under the condition of no road network. It is particularly difficult to conduct path planning in complex terrain environments. Traditional path planning methods mostly focus on path design in structured environments, while in complex natural environments, these methods often cannot effectively cope with variable ground features. Therefore, the path planning problem for complex terrain environments has become a difficult problem that researchers urgently need to solve. Currently, path planning algorithms face the following problems in complex terrains: (1) Low computational efficiency: Single-resolution search leads to a large number of nodes, especially in large-scale complex terrains, which is time-consuming significantly; (2) Poor terrain adaptability, resulting in low path feasibility; (3) Single cost evaluation, traditional evaluation functions only consider distance and ignore the impact of terrain on movement costs; (4) Insufficient dynamic adjustment: Existing two-stage path planning algorithms lack a multi-stage resolution adaptive mechanism and are difficult to balance accuracy and efficiency. Summary of the Invention

[0003] Object of the Invention: The present invention aims to provide a two-stage adaptive global path planning method for complex terrains that integrates terrain parameters.

[0004] Technical Solution: The two-stage adaptive global path planning method described in the present invention includes the following steps:

[0005] (1) Match the elevation data map and the surface cover type data map of the path planning area, define the resolution of the matched map as the finest resolution, and calculate the slope, roughness and terrain undulation degree of the grid cells at the finest resolution;

[0006] (2)Determine the slope cost, roughness cost, and terrain undulation cost of the grid cells corresponding to the finest resolution based on the slope, roughness, and terrain undulation of the grid cells obtained in step (1), and set the surface cover cost according to the region type;

[0007] (3)In the first stage, use the bidirectional A* algorithm for rapid path planning; determine the first-stage retrieval resolution according to the path planning area and the preset rapid retrieval space, calculate the traversal cost of the grid area corresponding to the first-stage retrieval resolution based on the slope cost, roughness cost, terrain undulation cost of the grid cells corresponding to the finest resolution obtained in step (2), and the set surface cover cost, and obtain the optimal path set of the first stage using the bidirectional A* algorithm;

[0008] (4)In the second stage, use the adaptive A* algorithm for path planning; construct an adaptive path resolution for the optimal path obtained in step (3), calculate the traversal cost of the grid area corresponding to the adaptive path resolution, use the adaptive A* algorithm, introduce a path adaptive buffer attenuation function, and perform adaptive path end point judgment on each intermediate path segment to complete the path planning.

[0009] Further, in step (1), convert the data of the elevation data map and the surface cover type data map to the same coordinate system. If the resolution of the elevation data map is lower than that of the surface cover type data map, resample the elevation data map using bilinear interpolation; otherwise, resample the surface cover type data map using the nearest neighbor method.

[0010] Further, in step (1), expand to obtain a 3×3 nine-grid area centered on the corresponding grid cell to be solved, and the elevation value matrix corresponding to the nine-grid is ;

[0011] The slope of the grid cell corresponding to the finest resolution is

[0012] ;

[0013] Among them, represents the slope in the row direction, represents the slope in the column direction, is the finest resolution in the row direction after map matching, is the finest resolution in the column direction after map matching, and S represents the slope value of the grid cell;

[0014] The roughness R of the grid cell corresponding to the finest resolution is

[0015] ;

[0016] Among them, is thei The elevation value represented by a grid is the average elevation value of the nine-square grid area;

[0017] The terrain undulation degree of the grid cell corresponding to the finest resolution U is

[0018] ;

[0019] Among them, is the maximum elevation value within the nine-square grid area, is the minimum elevation value within the nine-square grid area.

[0020] Furthermore, in step (2), the slope cost of the grid cell corresponding to the finest resolution is

[0021] ;

[0022] Among them, is the slope of the current driving grid, is the maximum allowable passing slope of the mobile robot;

[0023] The roughness cost is

[0024] ;

[0025] Among them, R is the roughness of the nine-square grid area;

[0026] The terrain undulation degree cost is

[0027] ;

[0028] Among them, and respectively represent the maximum elevation value and the minimum elevation value within the nine-square grid area, is the maximum elevation value of the elevation within the map area, is the minimum elevation value of the elevation within the map area.

[0029] Furthermore, in step (2), the surface cover cost is

[0030] ;

[0031] Among them, if the land cover type of the grid cell corresponding to the finest resolution is an obstacle area, a water area, or a tree area, it is classified as an impassable area; if the land cover type of the grid cell corresponding to the finest resolution is a crop or building area, it is classified as a difficult-to-pass area; if the land cover type of the grid cell corresponding to the finest resolution is bare ground or grassland, it is classified as a passable area.

[0032] Further, in step (3), the first-stage retrieval resolution is

[0033] ;

[0034] ;

[0035] Among them, Re c- x represents the resolution in the row direction of the first-stage retrieval, Re c- y represents the resolution in the column direction of the first-stage retrieval, m represents the of the search space when the search space is reduced to the finest resolution, indicating rounding up;

[0036] The traversal cost of the grid area corresponding to the first-stage retrieval resolution is

[0037] ;

[0038] Among them, , , , are weight coefficients, and ; is the slope cost of the area, is the land cover cost of the area, is the roughness cost of the area, is the terrain undulation cost of the area.

[0039] Further, the slope cost of the area is

[0040] ;

[0041] ;

[0042] Among them, , are weight coefficients, ; N is the number of grid cells corresponding to the finest resolution contained in the grid region corresponding to the retrieval resolution in the first stage, which is the average slope cost of the grid cells corresponding to the finest resolution in this region, is the slope cost of the

[0043] grid cell corresponding to the th

[0044] finest resolution;

[0045] ;

[0046] Among them, , are weight coefficients, ; is the average surface cover cost of the grid cells corresponding to the finest resolution in this region, is the surface cover cost of the

[0047] grid cell corresponding to the th

[0048] finest resolution;

[0049] ;

[0050] Among them, , are weight coefficients, ; is the average roughness cost of the grid cells corresponding to the finest resolution in this region, is the roughness cost of the

[0051] grid cell corresponding to the th

[0052] finest resolution;

[0053] ;

[0054] Among them, , are weight coefficients, ; is the average terrain undulation cost of the grid cells corresponding to the finest resolution in this region, is the The terrain undulation cost of the grid cell corresponding to the finest resolution.

[0055] Further, in step (4), an adaptive path resolution is constructed for the optimal path obtained in step (3) as

[0056] ;

[0057] ;

[0058] Among them, is the adaptive resolution in the row direction, is the adaptive resolution in the column direction, is the maximum passing cost at the first-stage retrieval resolution; is the passing cost of the path at the first-stage retrieval resolution, represents the maximum number of grid cells corresponding to the finest resolution in the row direction or column direction of the grid area corresponding to a second-stage adaptive resolution; represents rounding down.

[0059] Further, in step (4), the path adaptive buffer decay function f(d) is

[0060] ;

[0061] Among them, c is a custom coefficient, D is the distance from the end point of the last path segment to the start point of the first path segment , d is the distance from the end point of the current path segment to the start point of the first path segment .

[0062] Beneficial effects: Compared with the prior art, the significant advantages of the present invention are as follows: 1. The present invention takes the grid cell corresponding to the finest resolution as the unit, and calculates the movement cost by integrating slope, surface cover, roughness, and terrain undulation; 2. The grid areas corresponding to the first-stage and second-stage resolutions of the present invention are both composed of grid cells corresponding to the finest resolution. The overall passing difficulty and local volatility of the terrain are comprehensively characterized by the statistical aggregation of the cost mean and standard deviation of the grid cells within the area, so as to calculate the passing cost of the area; 3. The present invention takes the path planned in the first stage as the reference path for the second stage, comprehensively considers the issues of efficiency and accuracy in the second path planning stage, adopts an adaptive resolution mechanism, and uses an adaptive path end point judgment for each intermediate path segment in the second stage, enhancing the flexibility and scalability of the path, and achieving accurate and fast path planning for complex terrain paths. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is the flow chart of the present invention;

[0064] Figure 2 This is the structural schematic diagram of the nine - grid. Detailed implementation manners

[0065] The present invention will be further described below with reference to the accompanying drawings.

[0066] The two - stage adaptive global path planning method for complex terrain of the present invention includes the following steps:

[0067] (1) Match the elevation data map and the surface cover type data map of the path planning area, define the resolution of the matched map as the finest resolution, and calculate the slope, roughness and terrain undulation degree of the grid cells at the finest resolution.

[0068] Convert the data of the elevation data map and the surface cover type data map into the same coordinate system, resample the low - resolution map to match the high - resolution map. Among them, bilinear interpolation is used for the elevation data, and the nearest - neighbor method is used for the surface cover type data. Then, crop the data according to the boundary range of the research area to ensure that the spatial ranges of the two maps are consistent and completely covered. Use rows to represent the east - west direction and columns to represent the north - south direction. The resolution of the processed map is the finest resolution, and the finest resolution in the row direction is and the finest resolution in the column direction is .

[0069] According to the coordinates of the path start point (srow, scol) and the end point (grow, gcol), determine the minimum row coordinate (minrow), minimum column coordinate (mincol), maximum row coordinate (maxrow), and maximum column coordinate (maxcol) of the target area, and determine the range to be rasterized. Among them:

[0070] ;

[0071] In the above formula, K is a user - defined expansion coefficient, is to take the absolute value. For example, is the absolute value of.

[0072] Generate a discrete sequence from the minimum row coordinate (minrow) to the maximum row coordinate (maxrow) with the row resolution as the step size; generate a discrete sequence from the minimum column (mincol) to the maximum column (maxcol) with the column resolution as the step size.

[0073] When a mobile robot is moving, it is mainly affected by several nearby areas centered on its own position. Therefore, the slope, roughness, and terrain undulation are calculated for a 3×3 nine-grid area that expands from a grid area with the finest resolution as the center to the eight surrounding grid areas. The nine-grid area is shown in Figure (2), where the letters in the nine-grid represent elevation values, and the elevation value matrix corresponding to the nine-grid is .

[0074] The slope of the grid cell corresponding to the finest resolution is

[0075] ;

[0076] Among them, represents the slope in the row direction, represents the slope in the column direction, is the finest resolution in the row direction after map matching, is the finest resolution in the column direction after map matching, and S represents the slope value of the grid cell;

[0077] The roughness R of the grid cell corresponding to the finest resolution is

[0078] ;

[0079] Among them, is the elevation value represented by the i th grid, is the average elevation value of the nine-grid area.

[0080] Terrain undulation refers to the difference between the maximum elevation and the minimum elevation of an area. The terrain undulation degree U of the grid cell corresponding to the finest resolution is

[0081] ;

[0082] Among them, is the maximum elevation value within the nine-grid area, is the minimum elevation value within the nine-grid area.

[0083] (2) According to the slope, roughness, and terrain undulation degree of the grid cell obtained in step (1), determine the slope cost, roughness cost, and terrain undulation degree cost of the grid cell corresponding to the finest resolution, and set the surface cover cost according to the area type.

[0084] When a mobile robot performs path planning in complex terrain, its driving cost is mainly affected by slope, surface cover, roughness, and terrain undulation. For each influencing factor, its cost is designed according to its passing possibility, and its value range is [0,1]. Among them, the smaller the cost, the greater the passing possibility.

[0085] The movement difficulty of the mobile robot increases rapidly with the increase of the slope. When the slope is greater than the movement slope threshold of the robot, the robot cannot pass through the grid cell area, and the slope cost of the grid cell corresponding to the finest resolution cost S is

[0086] ;

[0087] where is the slope of the current driving grid, is the maximum allowable passing slope of the mobile robot.

[0088] Roughness cost is

[0089] ;

[0090] where R is the roughness of the nine-grid area.

[0091] Terrain undulation cost is

[0092] ;

[0093] where and respectively represent the maximum elevation and the minimum elevation in the nine-grid area, is the maximum elevation in the map area, is the minimum elevation in the map area.

[0094] According to the surface cover type, an area is divided into seven areas: obstacle area, water area, trees, crops, building area, bare ground, and grassland. The obstacle area, water area, and tree area are regarded as non-passable areas; the crop and building areas are regarded as difficult-to-pass areas; the bare ground and grassland are regarded as passable areas. Define the surface cover cost as

[0095] ;

[0096] where, if the surface cover type of the grid cell corresponding to the finest resolution is the obstacle area, water area, or tree area, it is divided into a non-passable area; if the surface cover type of the grid cell corresponding to the finest resolution is the crop or building area, it is divided into a difficult-to-pass area; if the surface cover type of the grid cell corresponding to the finest resolution is the bare ground or grassland, it is divided into a passable area.

[0097] In the first stage, the bidirectional A* algorithm is used for rapid path planning. According to the path planning area and the preset rapid retrieval space, the retrieval resolution of the first stage is determined. Based on the slope cost, roughness cost, terrain undulation cost, and the set surface cover cost of the grid cells corresponding to the finest resolution obtained in step (2), the passage cost of the grid area corresponding to the retrieval resolution of the first stage is calculated, and the optimal path set of the first stage is obtained by using the bidirectional A* algorithm.

[0098] Let the resolution in the row direction of the retrieval resolution of the first stage be and the resolution in the column direction be .

[0099] The search space of the map at the finest resolution is

[0100] ;

[0101] where X is the distance in the row direction of the map and Y is the distance in the column direction of the map, represents the finest resolution in the row direction, represents the finest resolution in the column direction.

[0102] The search space of the map at the retrieval resolution of the first stage is

[0103] ;

[0104] where X is the distance in the row direction and Y is the distance in the column direction, represents the resolution in the row direction of the first stage retrieval, represents the resolution in the column direction of the first stage retrieval, and

[0105] ;

[0106] Then = To achieve the purpose of rapid path planning in the first stage, it is necessary to reduce the search space. Let the when the search space needs to be reduced to the search space of the finest resolution of the map, then

[0107] ;

[0108] Then , Since the grid area corresponding to the retrieval resolution of the first stage is composed of the grid cells corresponding to the finest resolution, the following operation is performed:

[0109] ;

[0110] ;

[0111] Indicates rounding up.

[0112] The passing cost of the grid area corresponding to the first-stage retrieval resolution is

[0113] ;

[0114] Wherein, , , , are weight coefficients, and ; is the slope cost of the area, is the surface cover cost of the area, is the roughness cost of the area, is the terrain undulation cost of the area.

[0115] The slope cost of the area is

[0116] ;

[0117] ;

[0118] Wherein, , are weight coefficients, ; N is the number of grid cells corresponding to the finest resolution included in the grid area corresponding to the first-stage retrieval resolution, is the average value of the slope costs of all grid cells corresponding to the finest resolution in this area, is the th slope cost of the grid cell corresponding to the finest resolution;

[0119] The surface cover cost of the area is

[0120] ;

[0121] ;

[0122] Wherein, , are weight coefficients, ; is the average value of the surface cover costs of all grid cells corresponding to the finest resolution in this area, is the th surface cover cost of the grid cell corresponding to the finest resolution;

[0123] Roughness cost of the area is

[0124] ;

[0125] ;

[0126] wherein and are weight coefficients, ; is the average roughness cost of the grid cells corresponding to all the finest resolutions in this area, is the roughness cost of the grid cell corresponding to the th finest resolution;

[0127] Terrain undulation cost of the area is

[0128] ;

[0129] ;

[0130] wherein and are weight coefficients, ; is the average terrain undulation cost of the grid cells corresponding to all the finest resolutions in this area, is the terrain undulation cost of the grid cell corresponding to the th finest resolution.

[0131] The evaluation function for the first-stage path planning is designed as follows:

[0132] ;

[0133] ;

[0134] ;

[0135] where n node is the current grid node, is the estimated cost from the current grid node to the target node, is the actual cost from the starting point to the previous grid node. is the distance from the previous grid node to the current grid node; is the distance from the current grid node to the end point; is the average cost of the unpassed grid area corresponding to the first-stage retrieval resolution.

[0136] Define the starting point S and the end point G, and initialize two lists and are used to store the nodes to be explored starting from the starting point and the ending point respectively. Initialize two lists S and

[0137] G, which are used to store the nodes that have been processed starting from their respective starting points. These two lists are empty during initialization. Forward search: End the search when OpenSet_S is empty. When OpenSet_S is not empty, select the node with the smallest value from OpenSet_S as the current node, set it as Current_S. Calculate the evaluation function of all adjacent path nodes of Current_S . If the adjacent path node is not in OpenSet_S, add the adjacent node to OpenSet_S and record the predecessor of this node as Current_S. Remove Current_S from OpenSet_S and add it to CloseSet_S.

[0138] Backward search: End the search when OpenSet_G is empty. When OpenSet_G is not empty, select the node with the smallest value from OpenSet_G as the current node, set it as Current_G. Calculate the evaluation function of all adjacent path nodes of Current_G . If the adjacent path node is not in OpenSet_G, add the adjacent node to OpenSet_G and record the predecessor of this node as Current_G. Remove Current_G from OpenSet_G and add it to CloseSet_G. .

[0139] During each search process, it is necessary to check whether there is a node that appears in both CloseSet_S and CloseSet_G lists. If it appears in both lists, it means that the forward search and the backward search have met, and the search stops at this time.

[0140] When the meeting point is found, form a complete path by backtracking the forward path and the backward path. Forward path: Start from the starting point and backtrack along the parent nodes to the meeting node. Backward path: Start from the ending point and backtrack along the parent nodes to the meeting node. When the final path is found, return the merged path as the final result.

[0141] The optimal path planned by the first-stage retrieval resolution map that can reflect the fine-scale geomorphic situation is used as the reference planned path for the second stage, and a path collection is created , where is the Segment path. is the first segment path of the first-stage retrieval resolution path planning, is the last segment path of the first-stage retrieval resolution path planning.

[0142] (4) In the second stage, the adaptive A-star algorithm is used for path planning; an adaptive path resolution is constructed for the optimal path obtained in step (3), the passing cost of the grid area corresponding to the adaptive path resolution is calculated, the adaptive A-star algorithm is adopted, a path adaptive buffer attenuation function is introduced, and the adaptive path end point is judged for each intermediate segment path to complete the path planning.

[0143] In order to balance the accuracy and efficiency of the second-stage path planning, it is necessary to design the resolution of the second stage according to the cost of the first-stage path planning. This paper proposes an adaptive fine-scale resolution mechanism. The specific design of the adaptive path resolution for each segment path is as follows: ;

[0144] ;

[0145] Among them, is the adaptive resolution in the row direction, is the adaptive resolution in the column direction, is the maximum passing cost under the first-stage retrieval resolution; is the passing cost of the path under the first-stage retrieval resolution, t represents the maximum number of grid cells corresponding to the finest resolution in the row direction or column direction of a grid area corresponding to the second-stage adaptive resolution; represents rounding down.

[0146] The passing cost of the grid area corresponding to the second-stage adaptive resolution is

[0147] ;

[0148] Among them, , , , are weight coefficients, and ; is the slope cost of the area, is the surface cover cost of the area, is the roughness cost of the area, is the terrain undulation cost of the area.

[0149] The design of the evaluation function of the adaptive A-star algorithm in the second stage is as follows:

[0150] ;

[0151] ;

[0152] ;

[0153] Among them, the n node is the current grid node, is the estimated cost from the current grid node to the target node, is the actual cost from the starting point to the previous grid node. is the distance from the previous grid node to the current grid node; is the distance from the current grid node to the end point. is the average cost of the corresponding grid area with the second-stage adaptive resolution where this section is not passable. Path planning is performed on the path sequentially starting from the first section of the path.

[0154] Define the starting point S and the end point G, and initialize the list for storing the nodes to be explored starting from the starting point. Initialize the list , for storing the nodes that have been processed starting from the starting point. When initializing, the CLOSE list is empty.

[0155] Forward search: End the search when OPEN is empty. When OPEN is not empty, select from OPEN . If the adjacent path node is not in CLOSE and not in OPEN, add the adjacent path node to OPEN, record the predecessor of this node as Current, remove Current from OPEN, and add it to CLOSE.

[0156] Adaptive end point judgment: For each intermediate section of the path , introduce a path adaptive buffer attenuation function, and use the adaptive A* algorithm for path planning. The buffer attenuation function is designed as follows:

[0157] ;

[0158] where c is a custom coefficient, D is the last section of the path the distance from the end point to the starting point of the first section of the path starting point, d is the distance from the end point of the current section of the path to the starting point of the first section of the path starting point, is the absolute value of, when the buffer attenuation function is satisfied, that is, the distance from the current path node to the end point of this section of the path is reached, then it is considered that the end point has been reached, and this path node is regarded as the end point of this section of the path.

[0159] Loop and termination: Treat the end point of the current path as the start point of the next path segment, and perform adaptive resolution path planning for each path segment in sequence until the planning of the last path segment is completed.

Claims

1. A two-stage adaptive global path planning method for complex terrain, characterized in that, Including the following steps: (1) Match the elevation data map and the surface cover type data map of the path planning area, define the resolution of the matched map as the finest resolution, and calculate the slope, roughness, and terrain undulation degree of the grid cells at the finest resolution; (2) Determine the slope cost, roughness cost, and terrain undulation degree cost of the grid cells corresponding to the finest resolution according to the slope, roughness, and terrain undulation degree of the grid cells obtained in step (1), and set the surface cover cost according to the area type; (3) In the first stage, use the bidirectional A-star algorithm for fast path planning; determine the first-stage retrieval resolution according to the path planning area and the preset fast retrieval space, calculate the passing cost of the grid area corresponding to the first-stage retrieval resolution according to the slope cost, roughness cost, terrain undulation degree cost of the grid cells corresponding to the finest resolution obtained in step (2) and the set surface cover cost, and use the bidirectional A-star algorithm to obtain the optimal path set in the first stage; (4) In the second stage, use the adaptive A-star algorithm for path planning; construct an adaptive path resolution for the optimal path obtained in step (3), calculate the passing cost of the grid area corresponding to the adaptive path resolution, use the adaptive A-star algorithm, introduce a path adaptive buffer attenuation function, and perform an adaptive path end point judgment on each intermediate path segment to complete the path planning.

2. The two-stage adaptive global path planning method for complex terrain according to claim 1, wherein In step (1), convert the data of the elevation data map and the surface cover type data map to the same coordinate system. If the resolution of the elevation data map is lower than that of the surface cover type data map, use bilinear interpolation to resample the elevation data map; otherwise, use the nearest neighbor method to resample the surface cover type data map.

3. The two-stage adaptive global path planning method for complex terrain according to claim 1, characterized in that In step (1), with the corresponding grid cell to be solved as the center, a 3×3 nine-grid area is expanded, and the elevation value matrix corresponding to the nine-grid is ; The slope of the grid cell corresponding to the finest resolution is ； Among them, represents the slope in the row direction, represents the slope in the column direction, is the finest resolution in the row direction after map matching, is the finest resolution in the column direction after map matching, and S represents the slope value of the grid cell; The roughness R of the grid cell corresponding to the finest resolution is ； Among them, is the elevation value represented by the k th grid, and is the average elevation value of the nine-square grid area; Terrain undulation degree of the grid cell corresponding to the finest resolution U is ； Among them, is the maximum elevation value within the nine-grid area, is the minimum elevation value within the nine-grid area.

4. The two-stage adaptive global path planning method for complex terrain according to claim 3, wherein In step (2), the slope cost of the grid cell corresponding to the finest resolution is ； Among them, is the slope of the current driving grid, is the maximum allowable passing slope of the mobile robot; Roughness cost For ； where R is the roughness of the nine-grid area; Terrain undulation cost is ； Among them, and respectively represent the maximum elevation and the minimum elevation within the nine-square grid area, is the maximum elevation within the map area, is the minimum elevation within the map area.

5. The two-stage adaptive global path planning method for complex terrain according to claim 4, characterized in that In step (2), the cost of surface coverings cost C is ； where, if the surface cover type of the grid cell corresponding to the finest resolution is an obstacle area, water area, or tree area, it is classified as an impassable area; if the surface cover type of the grid cell corresponding to the finest resolution is a crop or building area, it is classified as a difficult-to-pass area; if the surface cover type of the grid cell corresponding to the finest resolution is bare ground or grassland, it is classified as a passable area.

6. The two-stage adaptive global path planning method for complex terrain according to claim 5, wherein In step (3), the first-stage retrieval resolution is ； ； Among them, represents the resolution in the row direction of the first-stage search, represents the resolution in the column direction of the first-stage search, m represents the , when the search space is reduced to the finest resolution, which means rounding up; Retrieving the passing cost of the grid area corresponding to the first-stage retrieval resolution For ； Among them, , , , are weight coefficients, and ; is the slope cost of the area, is the surface cover cost of the area, is the roughness cost of the area, is the terrain undulation cost of the area.

7. The two-stage adaptive global path planning method for complex terrain according to claim 6, wherein Slope cost of the area is ； ； Among them, , are weight coefficients, ; N is the number of grid cells corresponding to the finest resolution contained in the grid area corresponding to the first-stage retrieval resolution, is the average of the slope costs of all grid cells corresponding to the finest resolution in this area, is the th slope cost of the grid cell corresponding to the finest resolution; Cost of surface cover in the area For ； ； Among them, , are weight coefficients, ; is the average cost of surface cover of grid cells corresponding to all the finest resolutions in this area, is the th surface cover cost of grid cells corresponding to the finest resolution; Roughness cost of the area is ； ； Among them, , are weight coefficients, ; is the average roughness cost of the grid cells corresponding to all the finest resolutions in this area, is the roughness cost of the grid cell corresponding to the th finest resolution; Terrain undulation cost of the area For ； ； Among them, , are weight coefficients, ; is the average cost of terrain undulation of all grid cells corresponding to the finest resolution in this area, is the th terrain undulation cost of the grid cell corresponding to the finest resolution.

8. The two-stage adaptive global path planning method for complex terrain according to claim 7, characterized in that In step (4), the adaptive path resolution constructed for the optimal path obtained in step (3) is ； ； Among them, is the adaptive resolution in the row direction, is the adaptive resolution in the column direction, is the maximum passing cost at the first-stage retrieval resolution; is the passing cost of the path at the first-stage retrieval resolution, represents the maximum number of grid cells corresponding to the finest resolution in the row direction or the column direction of the grid area corresponding to a second-stage adaptive resolution; represents rounding down.

9. The global path planning method for two-stage adaptive complex terrain according to claim 8, characterized in that In step (4), the path adaptive buffer attenuation function f(d) is ； Among them, c is a custom coefficient, and D is the distance from the end point of the last section of the path to the start point of the first section of the path . d is the distance from the end point of the current section of the path to the start point of the first section of the path .

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