A multi-map path planning method and apparatus

Abstract

The application provides a multi-map path planning method and device, the method comprising: S1, obtaining information of a comprehensive map, a starting point and a target point position of path planning, wherein the comprehensive map is a map set integrated from information of multiple maps; the information of the comprehensive map comprises a predetermined weight, obstacle distribution and an obstacle type of each node in each map in the comprehensive map; the obstacle type comprises random obstacles and fixed obstacles; the multiple maps comprise maps of different scenes or different constraint conditions; the different constraint conditions comprise different environments, different time levels, different obstacle distributions and / or different preset scene weights; S2, calculating an optimal path from the starting point to the target point according to the information of the comprehensive map and a predetermined path planning algorithm. By using the above technical scheme, the accuracy and efficiency of path planning in a complex and variable environment can be improved, and the high stability and adaptability of path planning are ensured.

Description

Technical Field

[0001] This invention relates to the field of route planning, and in particular to a multi-map route planning method and apparatus. Background Technology

[0002] Path planning, a core technology in automated control and intelligent systems, primarily aims to solve the problem of finding the optimal path from start to finish in complex environments. The traditional A* algorithm, with its graph search technique based on heuristic and cost functions, performs well in path finding within a single map environment. However, it typically only performs path search on a single static map, ignoring the environmental differences and dynamic obstacles in multi-map scenarios. This limits the efficiency and accuracy of path planning when facing various complex situations, such as changing environmental conditions, obstacle distribution, and dynamic changes. Furthermore, when dealing with multi-map scenarios, existing algorithms often lack effective information integration mechanisms and cannot fully consider the interactions between different maps. This makes it difficult for the algorithm to make timely and effective adjustments when faced with path congestion caused by random obstacles. Therefore, developing a path planning method that can effectively combine information from multiple maps, obstacle distribution, and dynamic changes has become a key challenge in improving the accuracy and efficiency of path planning in complex and changing environments. Summary of the Invention

[0003] The embodiments of the present invention provide a multi-map path planning method and apparatus to improve the accuracy and efficiency of path planning in complex and ever-changing environments, and to ensure the high stability and adaptability of path planning.

[0004] To achieve the above objectives, a multi-map path planning method is provided, which includes:

[0005] S1, obtain information from the integrated map, the starting point and target point location for route planning, where:

[0006] The integrated map is a map set composed of information from multiple maps; the information of the integrated map includes: the predetermined weight of each map in the integrated map, the distribution of obstacles, and the type of obstacles at each node in the integrated map;

[0007] The obstacle types include: random obstacles and fixed obstacles; the random obstacles are those that exist in at least one of the multiple maps in the composite map; the fixed obstacles are those that exist in all of the multiple maps in the composite map.

[0008] The multiple maps include maps with different scenes or different constraints; the different constraints include different environments, different time levels, different obstacle distributions and / or different preset scene weights.

[0009] S2, calculating the optimal path from the starting point to the target point based on the information from the integrated map and a predetermined path planning algorithm, including:

[0010] Calculate the path cost and heuristic function value of the neighbor nodes of the selected node in the integrated map for each map in the integrated map;

[0011] The path cost and heuristic function value of the neighboring nodes are weighted according to the predetermined weight of each map in the comprehensive map to obtain the total path cost, total heuristic function value and total cost of the neighboring nodes, and the selected node is taken as the parent node of the node with the smallest total path cost among the neighboring nodes.

[0012] The neighbor node with the lowest total value is selected as the next selected node. When the selected node is the target point, the path is backtracked according to the parent-child relationship between the nodes to obtain the optimal path from the starting point to the target point.

[0013] Preferably, in the multi-map path planning method, step S2 includes:

[0014] The neighboring node is: the node that is adjacent to the selected node in the integrated map;

[0015] The heuristic function value is obtained by calculating the Euclidean distance.

[0016] Preferably, in the multi-map path planning method, step S2, selecting the neighbor node with the lowest total value as the next selected node, and when the selected node is the target point, backtracking the path according to the parent-child relationship between nodes to obtain the optimal path from the starting point to the target point, includes:

[0017] S21, initialize the set of nodes to be calculated and the set of visited nodes, and add the starting point to the set of nodes to be calculated;

[0018] S22, Select the node with the smallest total generation value from the set of nodes to be calculated as the current parent node, remove the current parent node from the set of nodes to be calculated, and add the current parent node to the set of visited nodes;

[0019] S23, determine whether the current parent node is the target point. If so, trace back the path through the parent-child relationship between each node in the visited node set to obtain the planned path and end the process.

[0020] If not, select multiple nodes from the current parent node's neighbor nodes that are within the effective range of the integrated map, are not fixed obstacles, and are not in the set of visited nodes, as nodes to be visited;

[0021] In step S2, the path cost and heuristic function value of the neighbor nodes of the selected node in the integrated map for each map in the integrated map are calculated, including:

[0022] S24, iteratively determine whether each node to be visited is an obstacle in each map of the integrated map. If so, select a replacement node from the surrounding nodes of the current node to be visited through a predetermined obstacle replacement strategy, and calculate the path cost and heuristic function value of the replacement node in the current map as the path cost and heuristic function value of the current node to be visited in the current map.

[0023] If not, calculate the path cost and heuristic function value of the currently visited node in the current map;

[0024] And, in step S2, the path cost value and heuristic function value of the neighboring nodes are weighted according to the predetermined weights of each map in the integrated map to obtain the total path cost value, total heuristic function value, and total cost of the neighboring nodes, and the selected node is designated as the parent node of the node with the smallest total path cost value among the neighboring nodes, including:

[0025] S25, calculate the total path cost, total heuristic function value and total cost of each node to be visited;

[0026] S26, determine whether each of the nodes to be visited is in the set of nodes to be calculated. If yes, select the node with the smallest total path cost in the set of nodes to be calculated, set the current parent node as the parent node of the node, and update the total path cost and total cost of the node to the total path cost and total cost calculated based on the current parent node. If no, add the node to be visited to the set of nodes to be calculated.

[0027] S27, return to step S22.

[0028] Preferably, in the multi-map path planning method, in step S24, the predetermined obstacle replacement strategy for selecting replacement nodes from the surrounding nodes of the currently visited node is as follows:

[0029] Get the surrounding nodes in eight directions centered on the node to be visited. The eight directions are: up, down, left, right and four diagonal positions. The surrounding nodes are: nodes that are located within the effective range of the current map, are not fixed obstacles or random obstacles, are not in the set of visited nodes and are not neighbors of the current parent node.

[0030] The node with the smallest heuristic function value among the surrounding nodes is selected as the replacement node.

[0031] Preferably, in the multi-map path planning method, if there are no surrounding nodes that meet the conditions around the node to be visited, a "not passable" flag is returned, and the path cost of the node to be visited is set to infinity.

[0032] Preferably, in the multi-map path planning method, step S23, when the current parent node is the target point, involves backtracking the path through the parent-child relationships between nodes in the visited node set to obtain the planned path, including:

[0033] The target point is taken as the current node. The parent node of the current node is found in the set of visited nodes, and the current node is updated to the parent node. The planned path is obtained when the current node is the starting point.

[0034] Preferably, in the multi-map path planning method, step S25 involves calculating the total path cost, total heuristic function value, and total cost for each node to be visited, wherein:

[0035] The total path cost is calculated using the following formula:

[0036]

[0037] g represents the total path cost, g(i) represents the path cost of the node to be visited on the i-th map, num_maps represents the number of all maps, i represents the i-th map, and w(i) represents the predetermined weight of the i-th map.

[0038] The total heuristic function value is calculated using the following formula:

[0039]

[0040] h represents the total heuristic function value, and h(i) represents the heuristic function value of the node to be visited on the i-th map;

[0041] The total cost is calculated using the following formula:

[0042] f = g + h

[0043] f represents the total cost.

[0044] Preferably, in the multi-map path planning method, the predetermined path planning algorithm is the A* search algorithm.

[0045] On the other hand, embodiments of the present invention provide a multi-map path planning apparatus, comprising a memory and a processor, wherein the memory stores at least one program, the at least one program being executed by the processor to implement the multi-map path planning method as described above.

[0046] In another aspect, embodiments of the present invention provide a computer program product, including a computer program, characterized in that, when the computer program is executed by a processor, it implements the multi-map path planning method as described above.

[0047] The above technical solution has the following technical effects:

[0048] The technical solution of this invention integrates information from multiple maps to form a comprehensive map. This comprehensive map includes the predetermined weights, obstacle distribution, and obstacle types (random and fixed obstacles) for each map. Each map represents a different scenario or constraint, such as different environments, time levels, obstacle distributions, and preset scenario weights. Based on the information from the comprehensive map, the path cost and heuristic function value of each node's neighboring nodes in each map are calculated, and weighted by combining the weights of each map to obtain the total path cost, total heuristic function value, and total cost of the neighboring nodes. The neighboring node with the lowest total cost is selected as the next selected node, and its parent node is set as the current selected node, until the selected node becomes the target point. The path is then backtracked based on the parent-child relationships between nodes to obtain the optimal path from the starting point to the target point. This approach adapts to complex and changing environments, effectively handles random and fixed obstacles, and ensures that the most efficient and safest path is found based on the integrated information from multiple maps.

[0049] In a further embodiment, the technical solution of this invention initializes the set of nodes to be calculated and the set of visited nodes, and adds the starting point to the set of nodes to be calculated; iteratively selects the node with the minimum total cost from the set of nodes to be calculated as the current parent node, and moves it to the set of visited nodes; if the current parent node is the target point, the planned path is obtained by backtracking through the set of visited nodes; otherwise, the neighboring nodes of the current parent node are expanded as nodes to be visited; for random obstacles in the nodes to be visited, the algorithm uses a predetermined obstacle replacement mechanism to select replacement nodes and calculate their path costs; for non-obstacle nodes, the path cost and heuristic function value are directly calculated; subsequently, the total path cost, total heuristic function value, and total cost of each node to be visited in all maps are calculated, and the nodes to be visited are updated or added to the set of nodes to be calculated based on these values. This process is repeated until the optimal path is found. Through this method, the algorithm can adapt to different scenarios and constraints, intelligently handle fixed and random obstacles, comprehensively consider the weights and obstacle distribution of multiple maps, and thus calculate the optimal path from the starting point to the target point, improving the accuracy and adaptability of path planning.

[0050] In a further embodiment, when the node to be visited is determined to be a random obstacle, the technical solution of this embodiment obtains the neighboring nodes in eight directions around the node to be visited, including the top, bottom, left, right and four diagonal positions; filters out surrounding nodes that meet the conditions. These surrounding nodes must be within the effective range of the current map, and not be fixed or random obstacles, nor are they in the set of visited nodes, nor are they neighbors of the current parent node; calculates the heuristic function value of each surrounding node, and selects the node with the smallest heuristic function value as the replacement node. This ensures that when encountering random obstacles, the algorithm can flexibly select the optimal alternative path, thereby improving the flexibility and adaptability of path planning and ensuring that an effective path can be found in complex environments. Attached Figure Description

[0051] Figure 1 This is a flowchart illustrating a multi-map path planning method according to an embodiment of the present invention;

[0052] Figure 2 This is a flowchart of a multi-map path planning method according to an embodiment of the present invention;

[0053] Figure 3 This is a flowchart of an obstacle replacement strategy in a multi-map path planning method according to an embodiment of the present invention;

[0054] Figure 4 This is a schematic diagram of the optimal path on a comprehensive map in a multi-map path planning method according to an embodiment of the present invention.

[0055] Figure 5 This is a schematic diagram of the structure of a multi-map path planning device according to an embodiment of the present invention. Detailed Implementation

[0056] To further illustrate the various embodiments, the present invention provides accompanying drawings. These drawings are part of the disclosure of the present invention, primarily used to illustrate the embodiments and to explain the operating principles of the embodiments in conjunction with the relevant descriptions in the specification. With reference to these drawings, those skilled in the art should be able to understand other possible implementations and the advantages of the present invention. Components in the drawings are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0057] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments.

[0058] Example 1:

[0059] To improve the accuracy and efficiency of path planning in complex and ever-changing environments, and to ensure the high stability and adaptability of path planning, embodiments of the present invention provide a multi-map path planning method. Figure 1 This is a flowchart illustrating a multi-map path planning method according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0060] S1, obtain information from the integrated map, the starting point and target point location for route planning, where:

[0061] An integrated map is a map set composed of information from multiple maps. The information in an integrated map includes: the predefined weights of each map in the integrated map, the distribution of obstacles, and the types of obstacles at each node in the integrated map.

[0062] Obstacle types include: random obstacles and fixed obstacles; random obstacles are obstacles that exist in at least one of the multiple maps in the composite map; fixed obstacles are obstacles that exist in multiple maps in the composite map.

[0063] Multiple maps include maps with different scenes or different constraints; different constraints include different environments, different time levels, different obstacle distributions, and / or different preset scene weights.

[0064] S2, based on the information from the integrated map and the predetermined path planning algorithm, calculates the optimal path from the starting point to the target point, including:

[0065] Calculate the path cost and heuristic function value of the neighboring nodes of the selected node in the integrated map for each map in the integrated map;

[0066] The path cost and heuristic function value of neighboring nodes are weighted according to the predetermined weight of each map in the integrated map to obtain the total path cost, total heuristic function value and total cost of neighboring nodes, and the selected node is selected as the parent node of the node with the smallest total path cost among the neighboring nodes.

[0067] The neighbor node with the lowest total value is selected as the next selected node. When the selected node is the target point, the path is backtracked according to the parent-child relationship between the nodes to obtain the optimal path from the starting point to the target point.

[0068] Preferably, the predetermined path planning algorithm is the A* search algorithm.

[0069] Example 2:

[0070] Traditional path planning typically relies on a single map for optimal path search. However, embodiments of this invention consider the combined influence of multiple maps. Specifically, each map represents a different scenario or constraint, such as varying obstacle distributions or scenario weights. During path planning, the path cost is calculated separately for each map, including the cumulative path length and heuristic distance from the starting point to the destination. Subsequently, the path costs of each map are multiplied by their respective weights and weighted to arrive at a total path cost, which is then used to select the optimal path. This multi-map weighted mechanism effectively addresses complex decision-making needs across multiple scenarios, enabling rapid path planning while also considering multi-dimensional map limitations and optimizing the path planning process.

[0071] Furthermore, in real-world environments, obstacles often exhibit randomness or dynamic changes. Traditional algorithms handle these unpredictable obstacles in a simplistic way or simply discard them. This invention proposes an obstacle substitution strategy. When a random obstacle affects path searching, the algorithm searches for an empty node near the obstacle with the smallest heuristic distance (h value), calculates the path cost of that node, and uses it as a reference value for the current obstacle node. Although the path cost calculation is based on the substitution node, the algorithm does not change the actual selection of the obstacle node but retains its position in the planned path. This ensures that the impact of obstacles on path planning is fully considered and effectively improves the flexibility and adaptability of path planning. This substitution mechanism is particularly suitable for application in dynamic environments, such as robot navigation or intelligent transportation scenarios, and can significantly improve the reliability and execution efficiency of planning. Therefore, Figure 2 This is a flowchart of a multi-map path planning method according to an embodiment of the present invention. Figure 2 As shown, the method includes:

[0072] Step 1: Obtain information from the integrated map, the starting point and target point location for route planning.

[0073] An integrated map is a map set composed of information from multiple maps. The information in an integrated map includes: the predefined weights of each map in the integrated map, the distribution of obstacles, and the types of obstacles at each node in the integrated map.

[0074] Obstacle types include: random obstacles and fixed obstacles; random obstacles are obstacles that exist in at least one of the multiple maps in the composite map; fixed obstacles are obstacles that exist in multiple maps in the composite map.

[0075] Multiple maps include maps with different scenarios or different constraints; different constraints include different environments, different time levels, different obstacle distributions, and / or different preset scenario weights.

[0076] In one specific embodiment, the integrated map comprises three maps (e.g., map_1, map_2, and map_3), with the following weights: map_1 = 0.4, map_2 = 0.3, and map_3 = 0.3.

[0077] Step 2: Initialize the set of nodes to be computed (openlist) and the set of visited nodes (closelist), and add the starting point to the set of nodes to be computed.

[0078] In one specific embodiment, when the set of nodes to be calculated is empty, an empty path is output and the parameter disTraj is set to -1 before the process ends.

[0079] Step 3: Select the node with the smallest total generation value (f value) from the set of nodes to be calculated as the current parent node, remove the current parent node from the set of nodes to be calculated, and add the current parent node to the set of visited nodes.

[0080] Step 4: Determine if the current parent node is the target point. If so, trace back the path through the parent-child relationships between the nodes in the visited node set to obtain the planned path and end the process.

[0081] If not, select multiple nodes from the current parent node's neighbor nodes that are within the effective range of the integrated map, are not fixed obstacles, and are not in the set of visited nodes, as nodes to be visited.

[0082] Preferably, the neighbor node is a node that is adjacent to the selected node in the integrated map.

[0083] Preferably, the planned path is obtained by tracing back the path through the parent-child relationship between each node in the visited node set, including: taking the target point as the current node, finding the parent node of the current node in the visited node set, and updating the current node to the parent node, until the current node is the starting point, thus obtaining the planned path.

[0084] Step 5: Iteratively determine whether each node to be visited is an obstacle on each map in the integrated map. If so, select a replacement node from the surrounding nodes of the current node to be visited using a predetermined obstacle replacement strategy, and calculate the path cost (g(i) value) and heuristic function value (h(i) value) of the replacement node in the current map as the path cost and heuristic function value of the current node to be visited in the current map.

[0085] If not, calculate the path cost and heuristic function value of the currently visited node in the current map.

[0086] Preferably, the heuristic function value is obtained by calculating the Euclidean distance, specifically using the following formula:

[0087]

[0088] h(n) represents the h value from point n to the target point, x1 and y1 represent the coordinates of point n, and x2 and y2 represent the coordinates of the target point.

[0089] In one specific embodiment, the predetermined obstacle replacement strategy for selecting replacement nodes from the surrounding nodes of the currently visited node is as follows:

[0090] Get the surrounding nodes in eight directions centered on the node to be visited. The eight directions are: up, down, left, right and four diagonal positions. Preferably, the surrounding nodes are: nodes that are within the effective range of the current map, are not fixed obstacles or random obstacles, are not in the set of visited nodes and are not the neighbor nodes of the current parent node.

[0091] Select the node with the smallest heuristic function value from the surrounding nodes as the replacement node.

[0092] Preferably, if there are no matching surrounding nodes around the node to be visited, a "not passable" flag is returned, and the path cost of the node to be visited is set to infinity.

[0093] Step 6: Calculate the total path cost (g value), total heuristic function value (h value), and total cost for each node to be visited.

[0094] Preferably, the total path cost is calculated using the following formula:

[0095]

[0096] g represents the total path cost, g(i) represents the path cost of the node to be visited on the i-th map, mum_maps represents the number of all maps, i represents the i-th map, and w(i) represents the predefined weight of the i-th map.

[0097] The total heuristic function value is calculated using the following formula:

[0098]

[0099] h represents the total heuristic function value, and h(i) represents the heuristic function value of the node to be visited on the i-th map;

[0100] The total cost is calculated using the following formula:

[0101] f = g + h

[0102] f represents the total cost.

[0103] Step 7: Determine whether each node to be visited is in the set of nodes to be calculated. If so, select the node with the smallest total path cost in the set of nodes to be calculated, set the current parent node as the parent node of the node, and update the total path cost and total cost of the node to the total path cost and total cost calculated based on the current parent node. If not, add the node to be visited to the set of nodes to be calculated.

[0104] Step 8, return to step 3.

[0105] In one specific embodiment, Figure 3 This is a flowchart illustrating an obstacle replacement strategy in a multi-map path planning method according to an embodiment of the present invention. Figure 3 As shown, the obstacle replacement strategy includes:

[0106] (1) Obtain the surrounding nodes in eight directions with the obstacle node as the center. The surrounding nodes in the eight directions are: the surrounding nodes in the top, bottom, left, right and four diagonal positions.

[0107] (2) Add the neighboring nodes of the surrounding nodes that are within the valid range of the current map, are not fixed obstacles or random obstacles, are not in the set of visited nodes, and are not the current parent node to the possible_neighbors set.

[0108] (3) Determine whether the possible_neighbors set is empty. If it is, set the g value to infinity. If not, calculate the h value of all surrounding nodes, i.e., replacement nodes, in the possible_neighbors set, and select the surrounding node with the smallest h value as the final replacement node. This means that the straight-line distance from the surrounding node to the target point is the shortest (distance between two points), and calculate the g value of the surrounding node.

[0109] (4) Output the g value of the final replacement node.

[0110] In one specific embodiment, Figure 4 This is a schematic diagram of the optimal path on a comprehensive map in a multi-map path planning method according to an embodiment of the present invention. Figure 4 As shown, the black part represents fixed obstacles, the gray part represents random obstacles, the red dot represents the starting point, and the blue dot represents the target point. By combining multiple maps of the integrated map and their corresponding weights, the A* search algorithm is used in combination with multi-map weighting and obstacle replacement strategy to generate an optimal path from the starting point to the target point. The optimal path passes through nodes of random obstacles through the obstacle replacement strategy.

[0111] The embodiments of this invention improve the accuracy and efficiency of path planning by integrating the distribution and weights of obstacles in multiple maps through an improved path planning algorithm. Specifically, by introducing mechanisms such as multi-map weighting and obstacle replacement strategies, the path planning can maintain high stability and flexibility in complex and ever-changing environments, ensuring that the optimal path can be found on the map and that it can react promptly when encountering dynamic obstacles.

[0112] Example 3:

[0113] The present invention also provides a multi-map path planning device, such as... Figure 5 As shown, the device includes a processor 501, a memory 502, a bus 503, and a computer program stored in the memory 502 and executable on the processor 501. The processor 501 includes one or more processing cores. The memory 502 is connected to the processor 501 via the bus 503. The memory 502 is used to store program instructions. When the processor 501 executes the computer program, it implements the steps in the above-described method embodiment of Embodiment 1 of the present invention.

[0114] Furthermore, as an executable solution, the multi-map path planning device can be a computer unit, which can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer unit may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above-described structure of the computer unit is merely an example and does not constitute a limitation on the computer unit. It may include more or fewer components, or combine certain components, or use different components. For example, the computer unit may also include input / output devices, network access devices, buses, etc., and this embodiment of the invention does not limit this.

[0115] Furthermore, as an executable solution, the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc. The processor is the control center of the computer unit, connecting various parts of the entire computer unit via various interfaces and lines.

[0116] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the computer unit by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0117] Example 4:

[0118] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps described above.

[0119] Although the invention has been specifically shown and described in conjunction with preferred embodiments, those skilled in the art should understand that various changes in form and detail may be made to the invention without departing from the spirit and scope of the invention as defined in the appended claims, all of which shall be within the scope of protection of the invention.

Claims

1. A multi-map path planning method, characterized by, The method comprises the following steps: S1, obtaining information of a comprehensive map, a starting point and a target point of path planning, wherein: The comprehensive map is a map set integrated from information of multiple maps; the information of the comprehensive map comprises predetermined weights of each map in the comprehensive map, obstacle distribution, and obstacle types of each node in the comprehensive map; The obstacle types comprise random obstacles and fixed obstacles; the random obstacles are obstacles existing in at least one map of the multiple maps of the comprehensive map; the fixed obstacles are obstacles existing in all the multiple maps of the comprehensive map; The multiple maps comprise maps of different scenes or different constraint conditions; the different constraint conditions comprise different environments, different time levels, different obstacle distributions, and / or different preset scene weights; S2, calculating an optimal path from the starting point to the target point according to the information of the comprehensive map and a predetermined path planning algorithm, comprising: calculating path generation values and heuristic function values of neighbor nodes of a selected node in the comprehensive map in each map of the comprehensive map; performing weighted calculation on the path generation values and the heuristic function values of the neighbor nodes according to predetermined weights of each map of the comprehensive map, to obtain total path generation values, total heuristic function values, and total costs of the neighbor nodes, and taking the selected node as a parent node of a node with the minimum total path generation value among the neighbor nodes; taking the neighbor node with the minimum total cost as a next selected node, and when the selected node is the target point, backtracking a path according to parent-child relationships among nodes to obtain the optimal path from the starting point to the target point; In step S2, taking the neighbor node with the minimum total cost as the next selected node, and when the selected node is the target point, backtracking a path according to parent-child relationships among nodes to obtain the optimal path from the starting point to the target point, comprising: S21, initializing a set of nodes to be calculated and a set of visited nodes, and adding the starting point to the set of nodes to be calculated; S22, selecting a node with the minimum total cost from the set of nodes to be calculated as a current parent node, removing the current parent node from the set of nodes to be calculated, and adding the current parent node to the set of visited nodes; S23, judging whether the current parent node is the target point, if yes, backtracking a path according to parent-child relationships among nodes in the set of visited nodes to obtain a planning path, and ending the process; if not, selecting multiple nodes, which are located within a valid range of the comprehensive map, are not fixed obstacles, and are not in the set of visited nodes, from neighbor nodes of the current parent node as nodes to be visited; In step S2, calculating path generation values and heuristic function values of neighbor nodes of a selected node in the comprehensive map in each map of the comprehensive map, comprising: S24, judging whether each of the to-be-visited nodes in the comprehensive map is an obstacle on each of the maps in the comprehensive map, if yes, selecting a replacement node from surrounding nodes of the current to-be-visited node by a predetermined obstacle replacement strategy, and calculating a path cost value and a heuristic function value of the replacement node on the current map as the path cost value and the heuristic function value of the current to-be-visited node on the current map; if no, calculating the path cost value and the heuristic function value of the current to-be-visited node on the current map; and in step S2, the path cost value and the heuristic function value of the neighbor node are calculated by weighting according to a predetermined weight of each of the maps in the comprehensive map, to obtain a total path cost value, a total heuristic function value and a total cost of the neighbor node, and the selected node is set as a parent node of the neighbor node with the minimum total path cost value, comprising: S25, calculating the total path cost value, the total heuristic function value and the total cost of each of the to-be-visited nodes; S26, judging whether each of the to-be-visited nodes is in the to-be-calculated node set, if yes, selecting a node with the minimum total path cost value in the to-be-calculated node set, setting the current parent node as a parent node of the node, and updating the total path cost value and the total cost of the node as the total path cost value and the total cost calculated according to the current parent node; if no, adding the to-be-visited node to the to-be-calculated node set; S27, returning to step S22.

2. The multi-map path planning method of claim 1, wherein, In step S2: the neighbor node is a node adjacent to the selected node in the comprehensive map; the heuristic function value is obtained by calculating the Euclidean distance.

3. The multi-map path planning method of claim 1, wherein, In step S24, the predetermined obstacle replacement strategy in selecting the replacement node from the surrounding nodes of the current to-be-visited node is: obtaining eight surrounding nodes in eight directions around the to-be-visited node, the eight directions being up, down, left, right and four diagonal positions, the surrounding nodes being nodes in the effective range of the current map, not being fixed obstacles or random obstacles, not being in the visited node set and not being neighbor nodes of the current parent node; selecting a node with the minimum heuristic function value as the replacement node from the surrounding nodes.

4. The multi-map path planning method of claim 3, wherein, if there is no surrounding node meeting the conditions around the to-be-visited node, returning an impassable flag, and setting the path cost value of the to-be-visited node as infinity.

5. The multi-map path planning method of claim 1, wherein, In step S23, when the current parent node is the target point, the planning path is obtained by backtracking the parent-child relationship between nodes in the visited node set, comprising: taking the target point as a current node, finding a parent node of the current node in the visited node set, and updating the current node as the parent node, until the current node is the starting point, to obtain the planning path.

6. The multi-map path planning method of claim 1, wherein, In step S25, the total path cost value, the total heuristic function value and the total cost of each of the to-be-visited nodes are calculated, wherein: The total path cost value is calculated by the following formula: g represents the total path cost value, g(i) represents the path cost value of the node to be visited on the i-th map, num_maps represents the number of all maps, i represents the i-th map, and w(i) represents the predetermined weight of the i-th map; The total heuristic function value is calculated by the following formula: h represents the total heuristic function value, and h(i) represents the heuristic function value of the node to be visited on the i-th map; The total cost is calculated by the following formula: f = g + h f represents the total cost.

7. The multi-map path planning method of claim 1, wherein, The predetermined path planning algorithm is an A* search algorithm.

8. A multi-map path planning apparatus characterized by comprising: The computer program is executed by the processor to implement the multi-map path planning method according to any one of claims 1 to 7.

9. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the multi-map path planning method according to any one of claims 1 to 7.

Images