Multi-robot path planning method and device in lunar surface environment, medium and equipment

Abstract

The invention provides a multi-robot path planning method in a lunar surface environment, a multi-robot path planning device in the lunar surface environment, a computer readable storage medium and electronic equipment, and relates to the technical field of computers. The path planning method comprises the following steps: acquiring a three-dimensional grid map of a lunar surface; determining a node with the minimum cost value for each robot based on the three-dimensional grid map by using a bidirectional A * algorithm, and expanding the node until the path planning is completed; wherein a cost function of the bidirectional A * algorithm comprises constraint conditions constructed based on topographic data and / or illumination data of the lunar surface; if the paths of the nodes have conflicts, conflict resolution is carried out according to the priorities of the robots, and node expansion is carried out by combining the bidirectional A * algorithm again. According to the invention, the efficiency of lunar surface path planning and the environmental fitness can be improved.

Description

Background Art

[0002] With the development of aerospace technology, humans have the ability to explore planets outside the Earth. As the closest celestial body to the Earth, the moon has become the first target of human exploration since the last century. Through preliminary exploration of the moon, it was discovered that the moon has rich mineral and energy resources. Humans intend to build a lunar base on the moon to further develop lunar resources.

[0003] Both the lunar exploration mission and base construction mission require lunar surface (referred to as lunar surface) robots to complete. The quality of the lunar surface robot's path planning determines whether the lunar surface mission can be successful and the efficiency of its completion.

[0004] At present, the path planning of lunar robots has problems of low efficiency and insufficient environmental adaptability.

[0005] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0006] The purpose of the present disclosure is to provide a multi-robot path planning method in a lunar environment, a multi-robot path planning device in a lunar environment, a computer-readable storage medium and an electronic device, so as to at least to some extent overcome the problems of low efficiency and insufficient environmental adaptability of lunar robot path planning.

[0007] According to a first aspect of the present disclosure, a multi-robot path planning method in a lunar environment is provided, comprising: obtaining a three-dimensional grid map of the lunar surface; determining a node with the smallest cost for each robot based on the three-dimensional grid map and using a bidirectional A* algorithm for expansion until path planning is completed; wherein the cost function of the bidirectional A* algorithm includes constraints constructed based on terrain data and / or illumination data of the lunar surface; if there is a conflict in the path of the node, the conflict is resolved according to the priority of the robot, and the node is expanded by recombining the bidirectional A* algorithm.

[0008] Optionally, obtaining a three-dimensional grid map of the lunar surface includes: obtaining three-dimensional representations of multiple discrete points on the lunar surface; rasterizing the three-dimensional representations of the multiple discrete points to generate an initial grid map; determining terrain features of the lunar surface; wherein the terrain features include rock features and crater features; and fusing the terrain features of the lunar surface with the initial grid map to generate a three-dimensional grid map of the lunar surface.

[0009] Optionally, when the cost function of the bidirectional A* algorithm includes constraints constructed based on the terrain data and lighting data of the lunar surface, the cost function of the bidirectional A* algorithm includes the path cost function of the robot from the initial node to the current node and the heuristic function of the bidirectional A* algorithm, and the path planning method also includes: determining the terrain data and lighting data of the lunar surface; establishing a path cost function based on the terrain data and lighting data of the lunar surface.

[0010] Optionally, the path cost function includes a path movement cost function and a path illumination cost function; wherein, establishing the path cost function based on the terrain data and illumination data of the lunar surface includes: determining the path movement cost function in combination with the slope of the adjacent grid area and the movement distance of the robot in the adjacent grid area; and determining the path illumination cost function by utilizing the output power of the solar panels of the robot in the adjacent grid area and the time taken for the robot to move from one area to another in the adjacent grid area.

[0011] Optionally, the bidirectional A* algorithm adopts an 8-neighborhood search method; wherein the path planning method also includes: determining the current node coordinates of the robot and the target node coordinates of the path; using the current node coordinates and the target node coordinates of the robot to determine the diagonal distance and the straight-line distance; and optimizing the heuristic function of the bidirectional A* algorithm in combination with the diagonal distance and the straight-line distance.

[0012] Optionally, the path planning method also includes: determining the optimal path of the robot; determining the maximum energy consumption and the maximum path length of the optimal path; and determining the priority of the robot in combination with the maximum energy consumption and the maximum path length of the optimal path.

[0013] Optionally, if there is a conflict in the path of the node, the conflict is resolved according to the priority of the robot, and the bidirectional A* algorithm is recombined to expand the node, including: if there is a conflict in the path of the node, the node is used as the parent node based on the binary tree to generate two child nodes, the child nodes inherit all the constraints of the parent node and add constraints corresponding to the conflict; for the two child nodes, the bidirectional A* algorithm is recombined to expand the node.

[0014] According to a second aspect of the present disclosure, a multi-robot path planning device in a lunar environment is provided, comprising: a map acquisition module, used to acquire a three-dimensional grid map of the lunar surface; a path planning module, used to determine, based on the three-dimensional grid map and using a bidirectional A* algorithm, a node with the smallest cost for each robot to expand until the path planning is completed; wherein the cost function of the bidirectional A* algorithm includes constraints constructed based on the terrain data and / or illumination data of the lunar surface; and a conflict handling module, used to resolve the conflict according to the priority of the robot if there is a conflict in the path of the node, and re-combine the bidirectional A* algorithm to perform node expansion.

[0015] Optionally, the map acquisition module is configured to perform: acquiring three-dimensional representations of multiple discrete points on the lunar surface; rasterizing the three-dimensional representations of the multiple discrete points to generate an initial raster map; determining terrain features of the lunar surface; wherein the terrain features include rock features and crater features; and fusing the terrain features of the lunar surface with the initial raster map to generate a three-dimensional raster map of the lunar surface.

[0016] Optionally, when the cost function of the bidirectional A* algorithm includes constraints constructed based on the terrain data and lighting data of the lunar surface, the cost function of the bidirectional A* algorithm includes the path cost function of the robot from the initial node to the current node and the heuristic function of the bidirectional A* algorithm, and the path planning module is also configured to execute: determining the terrain data and lighting data of the lunar surface; establishing the path cost function based on the terrain data and lighting data of the lunar surface.

[0017] Optionally, the path cost function includes a path movement cost function and a path illumination cost function. In this case, the path planning module is configured to perform: determining the path movement cost function in combination with the slope of the adjacent grid area and the movement distance of the robot in the adjacent grid area; and determining the path illumination cost function using the output power of the solar panel of the robot in the adjacent grid area and the time taken by the robot to move from one area to another in the adjacent grid area.

[0018] Optionally, the bidirectional A* algorithm adopts an 8-neighborhood search method. In this case, the path planning module is further configured to perform: determining the current node coordinates of the robot and the target node coordinates of the path; determining the diagonal distance and the straight-line distance using the current node coordinates and the target node coordinates of the robot; and optimizing the heuristic function of the bidirectional A* algorithm in combination with the diagonal distance and the straight-line distance.

[0019] Optionally, the conflict handling module is further configured to execute: determining an optimal path for the robot; determining a maximum energy consumption and a maximum path length for the optimal path; and determining a priority of the robot in combination with the maximum energy consumption and the maximum path length for the optimal path.

[0020] Optionally, the conflict handling module is configured to execute: if there is a conflict in the path of the node, the node is used as a parent node based on the binary tree to generate two child nodes, the child nodes inherit all constraints of the parent node and add constraints corresponding to the conflict; for the two child nodes, the bidirectional A* algorithm is recombined to perform node expansion.

[0021] According to a third aspect of the present disclosure, there is provided a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-mentioned multi-robot path planning methods in a lunar environment.

[0022] According to a fourth aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; the processor is configured to implement any of the above-mentioned multi-robot path planning methods in a lunar environment by executing the executable instructions.

[0023] In the technical solutions provided in some embodiments of the present disclosure, on the one hand, the bidirectional A* algorithm of multiple robots is used to realize path planning, which can significantly improve the efficiency of lunar path planning; on the other hand, in the bidirectional A* algorithm, the terrain and lighting are considered to construct the cost function, which matches the complex environment of the lunar surface, and helps to improve the environmental adaptability of path planning; on the other hand, in response to possible conflict problems, the present disclosure resolves conflicts according to the priority of the robots, so that the path planning of each robot is more reasonable, which helps to complete various tasks in the lunar scene as quickly as possible.

[0024] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0026] Figure 1 A flowchart of a multi-robot path planning method in a lunar environment according to an exemplary embodiment of the present disclosure is schematically shown.

[0027] Figure 2 The simulated cross-section of a lunar rock according to an embodiment of the present disclosure is schematically shown.

[0028] Figure 3 A simulated cross-sectional view of a lunar impact crater according to an embodiment of the present disclosure is schematically shown.

[0029] Figure 4 A schematic diagram showing the robot travel states of adjacent grids according to an embodiment of the present disclosure.

[0030] Figure 5 A schematic diagram showing conflict types according to an embodiment of the present disclosure.

[0031] Figure 6 A schematic diagram showing types of vertex conflicts according to an embodiment of the present disclosure.

[0032] Figure 7 A pseudo code diagram of a conflict-based search algorithm according to an embodiment of the present disclosure is shown.

[0033] Figure 8 A block diagram of a multi-robot path planning device in a lunar environment according to an exemplary embodiment of the present disclosure is schematically shown.

[0034] Fig. 9 A block diagram of an electronic device according to an exemplary embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0035] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as being limited to the examples set forth herein; on the contrary, these embodiments are provided so that the present disclosure will be more comprehensive and complete, and the concepts of the example embodiments are fully conveyed to those skilled in the art. The described features, structures, or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known technical solutions are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0036] In addition, the accompanying drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0037] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the steps. For example, some steps may be decomposed, while some steps may be combined or partially combined, so the actual execution order may change according to the actual situation.

[0038] Lunar path planning is a prerequisite for performing various lunar tasks. When tasks are diversified, a single robot cannot meet the task requirements well. The multi-robot system can make up for the shortcomings of a single robot through the advantages of group collaboration.

[0039] The lunar surface is covered with loose weathering layers, with rugged terrain and many craters. The terrain is complex. In order to ensure that the robot avoids dangerous areas and efficiently completes lunar exploration and base construction tasks, reasonable path planning is very important. Multi-robot path planning requires planning safe and efficient paths from the starting point to the target point for multiple robots, avoiding collisions between robots, and achieving the optimal or suboptimal total path cost.

[0040] Lunar robots are usually powered by solar energy. Only the polar regions on the moon experience the phenomenon of polar day. Lunar robots cannot be charged 24 hours a day. Lunar path planning needs to consider the robot's energy constraints as well as the lunar terrain and lighting constraints. At present, most of the research on lunar robot path planning is based on a two-dimensional plane environment, which only considers the obstacle information of the map, and does not consider the external environment such as terrain and lighting, as well as the robot's own capabilities. Multi-robot path planning uses a collaborative strategy based on the single robot path planning algorithm to solve the collision problem between robots.

[0041] Aiming at the multi-robot path planning problem in the complex lunar environment, this paper proposes a complete and optimal conflict-based hierarchical search algorithm, aiming to improve the efficiency and environmental adaptability of multi-robot path planning in the complex lunar environment.

[0042] The multi-robot path planning scheme under the lunar environment disclosed in the present invention can be implemented by an electronic device, that is, the electronic device can execute each step of the multi-robot path planning method under the lunar environment described below, and the multi-robot path planning device under the lunar environment described below can be configured in the electronic device. The present disclosure does not limit the type of electronic device, which can be a server, a personal computer, a smart mobile terminal, etc.

[0043] Figure 1 The flowchart of the multi-robot path planning method in the lunar environment according to an exemplary embodiment of the present disclosure is schematically shown. Figure 1 The multi-robot path planning method in the lunar environment may include the following steps:

[0044] S12. Obtain a three-dimensional grid map of the lunar surface.

[0045] According to some embodiments of the present disclosure, an image of the lunar surface may be collected. After acquiring the image, the electronic device may determine a map of the lunar surface based on the image, and then perform rasterization processing to generate a three-dimensional grid map of the lunar surface.

[0046] According to other embodiments of the present disclosure, a three-dimensional grid map of the lunar surface may be determined by computer simulation.

[0047] First, the electronics can obtain a three-dimensional representation of multiple discrete points on the lunar surface.

[0048] Specifically, the digital elevation model (DEM) can be used to represent the position information of a series of discrete points in three-dimensional space in digital form. Each discrete point can be represented by a three-dimensional vector, such as formula 1:

[0049] R i =(x i ,y i ,z i ),i=1,2,…,N (Formula 1)

[0050] Among them, (x i ,y i ) is the plane position coordinate of the discrete point, z i is the corresponding elevation value.

[0051] Since the discrete points cannot describe the terrain features, the electronic device may then perform rasterization processing on the three-dimensional representations of the multiple discrete points to generate an initial raster map.

[0052] Specifically, it can be divided into multiple grid areas according to actual needs, and each grid area contains a certain number of discrete points. It is understandable that if the grid size is too large, the map information will be less and the path planning result cannot be accurately obtained. If the grid size is too small, too many computing resources will be consumed. Therefore, the grid size can be set to be consistent with the projection area of ​​the robot or slightly larger than the projection area of ​​the robot.

[0053] In addition, the electronic equipment can also determine the terrain features of the lunar surface, which may include rock features and impact crater features.

[0054] In order to better simulate the complex environment of the lunar surface, we can use the distribution patterns of lunar terrain features to generate regular distribution of rocks and lunar meteorite impact craters (referred to as impact craters) and determine the diameters of rocks and impact craters.

[0055] The topographic features of the lunar surface mainly include rocks and impact craters. The distribution of rocks and impact craters on the lunar surface can be described by a formula. The number of rocks or impact craters exceeding a certain diameter per unit area can be expressed by formula 2:

[0056] lgN(D)=b×lgD+lg(a) (Formula 2)

[0057] Where a and b are coefficients, usually b < -2. N(D) represents the number of rocks or impact craters with a diameter greater than D.

[0058] Set the diameter array of rocks and craters, represented by D i (i=1,2…n),Dj (j=1,2…m), the above formula can be used to obtain the number array n corresponding to rocks and impact craters of different diameters i (i=1,2…n),n j (j=1,2…m). In the map area, for each rock and impact crater, the center position can be randomly generated to obtain the two-dimensional coordinates of the rock and impact crater.

[0059] As for rocks, since the lunar rocks are usually large in size, in order to simplify the processing, a suitable parabolic model can be used to simulate the shape of lunar rocks. Figure 2 , d represents the diameter of the stone, h represents the height of the stone, and the height of the stone can be further simplified to half of the stone diameter, that is, h = 0.5 × d. The parabola model used is shown in Formula 3:

[0060]

[0061] Regarding impact craters, lunar impact craters can be composed of a crater bottom and a crater lip. The simplified crater slope is shown in the figure below: Figure 3 The mathematical model of lunar impact craters is established by using double arc fitting and rotating around the Z axis. Figure 3 The parabolic equations of the pit bottom and pit lip are respectively given by

[0062] Formula 4 and Formula 5 show:

[0063]

[0064]

[0065] Among them, z(x,y) is the elevation value of the elevation point in the crater, (x,y) is the two-dimensional coordinate of the elevation point, D is the diameter of the bottom of the crater, d is the width of the lip of the crater, h is the depth of the crater, H is the height of the lip of the crater, and a is the parabola coefficient of the lip of the crater.

[0066] The impact area of ​​the stone and the impact crater is determined according to the diameter and two-dimensional coordinates of the stone and the impact crater, and the terrain elevation value in the stone and the impact crater area is calculated by the mathematical model of the stone and the impact crater to obtain the terrain features described in the embodiments of the present disclosure.

[0067] After determining the initial grid map and the terrain features of the lunar surface, the electronic device can fuse the two, that is, add the terrain features of the lunar surface to the initial grid map or modify the existing data to be consistent with the terrain features to generate a three-dimensional grid map of the lunar surface.

[0068] S14. Based on the three-dimensional grid map and using the bidirectional A* algorithm, determine the node with the smallest cost for each robot to expand until the path planning is completed; wherein the cost function of the bidirectional A* algorithm includes constraints constructed based on the terrain data and / or lighting data of the lunar surface.

[0069] In an exemplary embodiment of the present disclosure, the bidirectional A* algorithm can be improved by introducing a cost function that comprehensively considers the constraints of the lunar surface terrain and / or lighting, so that the improved bidirectional A* algorithm is more suitable for the complex environment of the lunar surface.

[0070] It should be noted that some embodiments of the present disclosure may use terrain data to optimize the bidirectional A* algorithm, may use illumination data to optimize the bidirectional A* algorithm, or may use terrain data and illumination data to optimize the bidirectional A* algorithm. The illumination data may include data obtained in response to illumination, which may also be referred to as illumination response data.

[0071] The cost function of the bidirectional A* algorithm can be expressed as Formula 6:

[0072] f(n)=g(n)+h(n) (Formula 6)

[0073] Wherein, f(n) is the cost function, which guides the search direction of the algorithm. n represents the current node. g(n) represents the actual cost consumed by the robot from the initial node to the current node, which is called the path cost function in the disclosed embodiment and can be calculated using the actual cost value of its parent node. h(n) is the heuristic function of the bidirectional A* algorithm, which represents the estimated cost of the robot from the current node to the target node.

[0074] The present disclosure improves the traditional bidirectional A* algorithm and can establish a path cost function based on the terrain data and illumination data of the lunar surface.

[0075] Specifically, the terrain data and the illumination data can be unified in the form of energy consumption cost to influence the path planning result. At the same time, a linear weighting method can be used to determine the influence of the terrain factors and the illumination factors on the path planning result.

[0076] Assume that the moving path of the lunar robot from the initial grid to the target grid is path, and the grid area traversed in the path is numbered R. i , i = 1, 2…n, then the improved path cost function g(n) can include the path movement cost function And the path lighting cost function It can be expressed as formula 7:

[0077]

[0078] in, Represents the grid R that takes into account the lunar surface terrain and lighting factors i To the adjacent grid R i+1 energy consumption cost. is the path movement cost function, which means the robot moves from grid R i To the adjacent grid R i+1 movement to do work. is the path illumination cost function, which indicates that the robot moves from the grid R i To the adjacent grid R i+1 w1 and w2 are the weight factors of the path movement cost and path illumination cost respectively, satisfying w1+w2=1.

[0079] The path movement cost function is as follows The determination process is described below.

[0080] The path movement cost function can be determined by combining the slope of the adjacent grid area and the movement distance of the robot in the adjacent grid area, wherein the slope corresponds to whether the robot is in an uphill or downhill motion state.

[0081] Assuming that the side length of the unit grid is L, the horizontal movement distance between adjacent grids can be expressed as Formula 8:

[0082]

[0083] Assume that the grid area R i and R i+1 The two-dimensional coordinates are (x i ,y i ) and (x i+1 ,y i+1 ), the normal vectors of the fitted plane are n i and n i+1 , then the robot consists of grid R i To grid R i+1 The horizontal direction vector of travel can be expressed as Formula 9:

[0084]

[0085] Grid area R i The plane normal vector n i and the robot's horizontal direction vector The angle φ i It can be expressed as formula 10:

[0086]

[0087] Similarly, the normal vector n of the fitting plane can be obtained i+1 and direction vector The angle φi+1 .

[0088] refer to Figure 4 , when the condition is met When the robot is in grid R i The interior is in a downhill state; when the conditions are met When the robot is in grid R i The internal is in an uphill state; when the conditions are met When the robot is in grid R i Similarly, it can be confirmed that the robot is in a horizontal state within the grid R. i+1 The state within.

[0089] The factors that affect the driving efficiency of the lunar robot on soft soil have a great impact on the power consumption of the robot's mobile system, and the driving efficiency of the lunar robot generally does not exceed 0.4. Assuming that the driving power of the lunar robot is q, the grid R i Move to the adjacent grid R i+1 The path movement cost function It can be obtained from formula 11 and formula 12:

[0090]

[0091]

[0092] Among them, w(R i ) indicates that the robot is in grid R i The path movement cost within; d i Indicates that the robot is in grid R i Moving distance within θ i Represents the grid R i The slope of the robot in grid R is: μ is the friction coefficient; G is the gravity of the robot. In addition, based on formula 12, the robot can be obtained i+1 The path movement cost w(R i+1 ).

[0093] The path lighting cost function is as follows The determination process is described below.

[0094] The path illumination cost function can be determined using the output power of the solar panels of the robot in the adjacent grid areas and the time taken by the robot to move from one area to another in the adjacent grid areas.

[0095] Assume that the area of ​​the solar panel on the lunar robot is s, and the grid area R i The light intensity is ε i, the solar energy conversion rate is λ, the inverter conversion efficiency in the solar panel is u, considering the charging efficiency and the loss during the charging process, the charging conversion rate is v, then the rated power of the robot's solar panel is P rated It can be expressed as formula 13:

[0096] P rated =ε i ·λ·s (Formula 13)

[0097] The output power of the solar panel can be expressed as Equation 14:

[0098] P output =P rated ·u·v (Formula 14)

[0099] The robot's solar panels can be found in grid R i and grid R i+1 The output power is P i and P i+1 .

[0100] As the robot moves along the path, the charging capacity of the solar panel changes with the light intensity of the grid area. Therefore, the path illumination cost function It can be expressed as formula 15:

[0101]

[0102] Among them, t i,i+1 Represented by the grid R i Move to the adjacent grid R i+1 time.

[0103] It is understandable that when the condition P is met i -P i+1 >0, it means that the robot's moving energy consumption increases; when the condition P is met i -P i+1 When <0, it means that the robot's movement energy consumption is reduced.

[0104] The optimization of the heuristic function of the bidirectional A* algorithm is explained below.

[0105] It is understandable that the closer the heuristic function h(n) is to the actual value h * (n), the fewer the extended nodes, the higher the search efficiency. The Manhattan distance used in the traditional two-way A* algorithm is suitable for 4-neighborhood search. In order to improve the search efficiency, the embodiment of the present disclosure adopts an 8-neighborhood search method.

[0106] First, the current node coordinates of the robot and the target node coordinates of the path can be determined; next, the diagonal distance and the straight-line distance can be determined using the current node coordinates of the robot and the target node coordinates; then, the diagonal distance and the straight-line distance can be combined to optimize the heuristic function of the bidirectional A* algorithm.

[0107] Specifically, based on the 8-neighborhood search, the distance can be constructed, see Formula 16, Formula 17, and Formula 18.

[0108] h diagonal =min(|x n -x goal |,|y n -y goal |) (Formula 16)

[0109] h straight =(|x n -x goal |+|y n -y goal |) (Formula 17)

[0110] h(n)=D 2 *h diagonal +D*(h straight -2*h diagonal ) (Formula 18)

[0111] Where D represents the direct penetration cost; D 2 Indicates the cost of diagonal penetration; x n ,y n is the current node coordinate; x goal ,y goal is the target node coordinate.

[0112] Since the illumination cost of the robot fluctuates rather than accumulates during its movement, the heuristic function h(n) of the embodiment of the present disclosure takes into account the path movement cost. The improved heuristic function h(n) can be expressed as Formula 19:

[0113]

[0114] Among them, h(n) is the estimated energy consumption cost from the current node to the target node; μ is the friction coefficient; G is the gravity of the robot; w1 is the weight factor of the path movement cost.

[0115] Based on the above process, the optimization of the bidirectional A* algorithm is realized. When the three-dimensional grid map is determined in step S12, the optimized bidirectional A* algorithm can be used to determine the node with the smallest cost for each robot for expansion until the path planning task from the initial node to the target node is completed.

[0116] S16. If there is a conflict in the path of the node, the conflict is resolved according to the priority of the robot, and the bidirectional A* algorithm is recombined to expand the node.

[0117] According to some embodiments of the present disclosure, each robot is assigned a unique priority, and then the movement trajectory is planned in sequence from the robot with the highest priority to the robot with the lowest priority.

[0118] This priority rule can effectively reduce the running time of the algorithm. However, the priority order of the robots is determined a priori, which may lead to poor solution quality and fail to meet the algorithm optimization requirements.

[0119] In order to at least solve the above problems, according to other embodiments of the present disclosure, a priority is dynamically set for each robot. First, the optimal path of the robot is determined, and the maximum energy consumption and the maximum path length of the optimal path are determined. Then, the priority of the robot is determined in combination with the maximum energy consumption and the maximum path length of the optimal path.

[0120] Specifically, the priority of each robot can be dynamically set by considering the robot's path energy consumption and task completion time. When a path conflict occurs, the conflict is eliminated according to the priority.

[0121] In the multi-robot path planning algorithm disclosed in the present invention, each robot obtains the optimal path without considering conflict coordination through the improved bidirectional A* algorithm. Assume that the maximum energy consumption of the robot in the optimal path is cost m , the maximum path length is dist m , then the robot priority can be expressed as formula 20:

[0122]

[0123] Among them, R icost represents the path energy consumption of the robot; R idist represents the path length of the robot. The path length reflects the robot's task completion time. It can be understood that P i The larger it is, the higher the priority of the robot.

[0124] The conflict coordination of the embodiment of the present disclosure is described below.

[0125] In the multi-robot path planning problem, as the number of robots increases, the multi-robot operating space environment will become narrower, which is likely to cause conflicts such as collisions or deadlocks between robots. When using the conflict-based search algorithm (CBS), it is necessary to determine the type of conflict and its corresponding solution.

[0126] The path planning algorithm used in the embodiment of the present disclosure uses an 8-neighborhood search. The robot may encounter vertex conflicts, boundary conflicts, and diagonal conflicts during movement. The specific forms are as follows: Figure 5 shown.

[0127] When dealing with vertex conflicts, one of the robots can be made to wait in place to avoid the occurrence of vertex conflicts. However, this method may form a new type of conflict after dealing with vertex conflicts, resulting in a decrease in the search efficiency of the algorithm. Figure 6 As shown in Figure 2, vertex conflicts can be divided into opposite vertex conflicts and cross vertex conflicts according to the robot's travel direction. Figure 5 and Figure 6 Use a 1 and a 2 Conflicting examples are provided and the present disclosure is not limited thereto.

[0128] For vertex conflicts, it is expressed as (a i ,a j ,v,t), robot a i and a j If the robot waits in place to deal with the conflict, the other robot will move to grid v at time t, and the two robots will form a new boundary conflict and exchange grid positions at time t+1.

[0129] Cross-collision is different from opposite vertex conflict. i and a j The direction of travel will change, and any robot will not form a boundary conflict with another robot after moving to grid v at time t.

[0130] The path planning algorithm compares the current positions of the two robots in a time series order to detect whether there is a conflict between the robots. The present disclosure defines conflict as follows:

[0131] Vertex conflict (a i ,a j ,u,v,w,t,type):robot a i At time t-1, robot a is at grid u. j At time t-1, the robot is at grid v. At time t, robot a i and a j They will all move to grid w, and type indicates the conflict type (including opposite vertex conflicts and cross vertex conflicts).

[0132] Border conflict (a i ,a j ,u,v,t,type):robot a iAt time t-1, robot a is at grid u. j At time t-1, the robot is at grid v. At time t, robot a i and a j The grid positions are swapped.

[0133] Diagonal conflict (a i ,a j ,u,v,t,type):robot a i At time t-1, robot a is at grid u. j At time t-1, the robot is at grid v. At time t, robot a i and a j Will cross to the diagonal grid.

[0134] According to the characteristics of different conflict types, formulating appropriate resolution schemes can help improve the search efficiency of the algorithm. The conflict resolution scheme of the embodiment of the present disclosure is as follows:

[0135] Solution to conflict between opposite vertices: i ,a j ,u,v,w,t,type), respectively robot a i and a j Add constraints (a i ,u,w,t) and constraints (a j ,v,w,t). Constraint (a i ,u,w,t) represents robot a i Grids u and w cannot be occupied at time t. Constraint (a j ,v,w,t) represents robot a j Grids v and w cannot be occupied at time t. The low-level search algorithm is called to re-plan the global path, obtain the conflict-free path, and update the binary tree child nodes.

[0136] Solution to cross vertex conflicts: i ,a j ,u,v,w,t,type), in robot a i or a j Before moving to the grid w where the conflict will occur, choose the time to wait for the other robot to move one grid. In this way, the path of the waiting robot can be guaranteed to maintain the global optimality and avoid replanning to increase the cost. For example, determine the waiting time t wait , add constraints (a i ,u,t wait ,) and (a j ,v,t wait ,), constraint (a i ,u,twait ,) indicates robot a i Move to grid u and wait for time t wait .

[0137] Solutions to border conflicts: i ,a j ,u,v,t,type), the robot's waiting solution cannot solve the conflict and needs to replan the global path. i and a j Add constraints (a i ,u,v,t) and constraints (a j ,u,v,t). Constraint (a i ,u,v,t) represents robot a i Grids u and v cannot be occupied at time t. The low-level search algorithm is called to replan the global path, obtain the conflict-free path, and update the binary tree child nodes.

[0138] Solution to diagonal conflict: i ,a j ,u,v,t,type), there is no robot a at a certain time t i and a j Occupies the same grid. Robot a i or a j At the grid at time t-1, wait for another robot to move to the next grid. Add constraints (a i ,u,t wait ,) and (a j ,v,t wait ,), constraint (a i ,u,t wait ,) indicates robot a i Move to grid u and wait for time t wait .

[0139] In some embodiments of the present disclosure, if there is a conflict in the path of a node, the node is used as a parent node based on a binary tree to generate two child nodes. The child nodes inherit all constraints of the parent node and add constraints corresponding to the conflict. For the two child nodes, the bidirectional A* algorithm is recombined to perform node expansion.

[0140] In combination with the above description, the embodiment of the present disclosure adopts the CBS algorithm to solve the path planning problem. The CBS algorithm can be divided into two layers of structure, the low-level structure plans a constrained optimal path for each robot, and the high-level structure handles the path conflicts between robots.

[0141] Specifically, the optimized bidirectional A* algorithm is used as the low-level search algorithm, and conflicts are resolved according to the robot priority defined above, and finally the path planning result is obtained. In addition. In the high-level structure, a search constraint binary tree (Constraint Tree, CT) is constructed, and each node N in CT contains the constraint set N.constraints, the solution to the problem N.solution and the total cost of the solution N.cost. Among them, the constraint set contains the constraints of all robots in the MAPF (multi-robot path planning) problem; the solution to the problem may not be valid, but the paths of all robots in the solution satisfy the constraint set of the node; the total cost of the solution represents the sum of the path costs of all robots.

[0142] The algorithm flow of the embodiment of the present disclosure is schematically illustrated below.

[0143] First, in the high-level search of the CBS algorithm, the constraint tree starts from a root node with an empty set of constraints and an empty priority ordering.

[0144] In addition, create a priority queue OPEN CT , used to store the nodes to be tested in CT, and the list is arranged from small to large according to the N.cost value.

[0145] Next, a low-level search can be performed to independently find an optimal path for each robot, and the solution N.solution of the root node N is obtained. Based on the N.solution, the priority sequence of each robot is obtained and placed in the root node.

[0146] Continuously open from the priority queue CT Select the node N with the lowest cost N.cost to expand. Perform conflict detection on the path of the current node. If no conflict is detected, use node N as the target node and return the solution N.solution of the node as the solution to the problem. If a conflict is found on the path, resolve the conflict according to the robot priority, and node N generates two child nodes N. 1 and N 2 , the child node inherits all the constraints of the parent node, and also adds new constraints (a i ,v,t) and (a j ,v,t). The child node updates the constraint set and calls the lower-level search to obtain the optimal path N that satisfies the new constraint set. 1 .solution, N 2 .solution. And update the total cost of the node N 1 .cost, N 2 .cost and the priority N of the node 1 .priority, N2 .priority. In addition, the parent node detection is completed, and the parent node N is OPENed from the queue CT Remove and add new node N 1 and N 2 Insert queue OPEN CT middle.

[0147] Thus, the node detection and expansion process is repeated until a conflict-free path solution is found and the target node is determined.

[0148] Figure 7 A pseudo code diagram of a conflict-based search algorithm according to an embodiment of the present disclosure is shown.

[0149] In addition, it can be understood that based on the improved two-way A* algorithm, the priority queue OPEN CT The choice of grid node n is determined by the cost function f(n)=g(n)+h(n). The state space of grid node n can be expressed as n=[n.node f(n)g(n)h(n)n parent .node], where n.node is the node serial number, indicating the coordinate position; n parent .node represents the parent node serial number. There is no time dimension in the state space of the grid node.

[0150] The constraints in the CBS algorithm are determined by the conflict node position and time. The state space of the improved grid node n can be n = [n.node f(n) g(n) h(n) n parent .node t], t represents the time taken by the robot to move from the starting point to the grid node n.

[0151] If the conflict resolution solution is that the robot waits for time t at grid node n wait , we can update t in the node state space to t = t + t wait .

[0152] If the conflict resolution solution is to call the bidirectional A* algorithm to re-plan the path, the conflict between the opposite vertices (a i ,a j ,u,v,w,t,type) as an example, for robot a i Add constraints (a i ,u,w,t). When the bidirectional A* algorithm is searching, the current node is expanded with 8 neighborhoods. If the state space of the child node n=[n.node t] exceeds the limit of the given constraint set, the child node can be abandoned and only the child nodes that meet the constraint set are placed in the priority queue OPEN. CT middle.

[0153] It should be noted that although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0154] Furthermore, this example implementation also provides a multi-robot path planning device in a lunar environment.

[0155] Figure 8 The block diagram of the multi-robot path planning device in the lunar environment according to an exemplary embodiment of the present disclosure is schematically shown. Figure 8 According to an exemplary embodiment of the present disclosure, a multi-robot path planning device 8 in a lunar environment may include a map acquisition module 81, a path planning module 83 and a conflict handling module 85.

[0156] Specifically, the map acquisition module 81 can be used to acquire a three-dimensional grid map of the lunar surface; the path planning module 83 can be used to determine the node with the smallest cost for each robot based on the three-dimensional grid map and using the bidirectional A* algorithm for expansion until the path planning is completed; wherein the cost function of the bidirectional A* algorithm includes constraints constructed based on the terrain data and / or lighting data of the lunar surface; the conflict handling module 85 can be used to resolve the conflict according to the robot's priority if there is a conflict in the node path, and re-combine the bidirectional A* algorithm to expand the node.

[0157] According to an exemplary embodiment of the present disclosure, the map acquisition module 81 can be configured to perform: acquiring a three-dimensional representation of multiple discrete points on the lunar surface; rasterizing the three-dimensional representation of the multiple discrete points to generate an initial grid map; determining the terrain features of the lunar surface; wherein the terrain features include rock features and crater features; and fusing the terrain features of the lunar surface with the initial grid map to generate a three-dimensional grid map of the lunar surface.

[0158] According to an exemplary embodiment of the present disclosure, when the cost function of the bidirectional A* algorithm includes constraints constructed based on the terrain data and lighting data of the lunar surface, the cost function of the bidirectional A* algorithm includes the path cost function of the robot from the initial node to the current node and the heuristic function of the bidirectional A* algorithm, and the path planning module 83 can also be configured to execute: determining the terrain data and lighting data of the lunar surface; establishing a path cost function based on the terrain data and lighting data of the lunar surface.

[0159] According to an exemplary embodiment of the present disclosure, the path cost function includes a path movement cost function and a path illumination cost function. In this case, the path planning module 83 can be configured to perform: determining the path movement cost function in combination with the slope of the adjacent grid area and the moving distance of the robot in the adjacent grid area; and determining the path illumination cost function using the output power of the solar panel of the robot in the adjacent grid area and the time taken by the robot to move from one area to another in the adjacent grid area.

[0160] According to an exemplary embodiment of the present disclosure, the bidirectional A* algorithm adopts an 8-neighborhood search method. In this case, the path planning module 83 can also be configured to perform: determining the current node coordinates of the robot and the target node coordinates of the path; determining the diagonal distance and the straight-line distance using the current node coordinates and the target node coordinates of the robot; and optimizing the heuristic function of the bidirectional A* algorithm in combination with the diagonal distance and the straight-line distance.

[0161] According to an exemplary embodiment of the present disclosure, the conflict handling module 85 can also be configured to perform: determining the optimal path of the robot; determining the maximum energy consumption and maximum path length of the optimal path; and determining the priority of the robot in combination with the maximum energy consumption and maximum path length of the optimal path.

[0162] According to an exemplary embodiment of the present disclosure, the conflict handling module 85 can be configured to perform: if there is a conflict in the path of a node, the node is used as a parent node based on a binary tree to generate two child nodes, and the child nodes inherit all constraints of the parent node and add constraints corresponding to the conflict; for the two child nodes, the bidirectional A* algorithm is recombined to perform node expansion.

[0163] Since the functional modules of the multi-robot path planning device in the lunar environment of the embodiment of the present disclosure are the same as those in the above-mentioned method implementation, they will not be described in detail here.

[0164] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementations, various aspects of the present disclosure may also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary implementations of the present disclosure described in the above "Exemplary Method" section of the present specification.

[0165] The program product for implementing the above method according to the embodiment of the present disclosure can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0166] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical disk, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0167] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0168] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0169] Program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0170] In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

[0171] Those skilled in the art will appreciate that various aspects of the present disclosure may be implemented as systems, methods or program products. Therefore, various aspects of the present disclosure may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as "circuits", "modules" or "systems".

[0172] Refer to the following Fig. 9 The electronic device 900 according to this embodiment of the present disclosure is described. Fig. 9 The electronic device 900 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0173] like Fig. 9 As shown, the electronic device 900 is in the form of a general computing device. The components of the electronic device 900 may include, but are not limited to: the at least one processing unit 910, the at least one storage unit 920, a bus 930 connecting different system components (including the storage unit 920 and the processing unit 910), and a display unit 940.

[0174] The storage unit stores a program code, which can be executed by the processing unit 910, so that the processing unit 910 executes the steps according to various exemplary embodiments of the present disclosure described in the above "Exemplary Method" section of this specification. For example, the processing unit 910 can execute various steps of the multi-robot path planning method in the lunar environment of the embodiment of the present disclosure.

[0175] The storage unit 920 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 9201 and / or a cache storage unit 9202 , and may further include a read-only storage unit (ROM) 9203 .

[0176] The storage unit 920 may also include a program / utility 9204 having a set (at least one) of program modules 9205, such program modules 9205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0177] Bus 930 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0178] The electronic device 900 may also communicate with one or more external devices 1000 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 900, and / or communicate with any device that enables the electronic device 900 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 950. Furthermore, the electronic device 900 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 960. As shown, the network adapter 960 communicates with other modules of the electronic device 900 via a bus 930. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 900, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0179] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0180] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present disclosure, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0181] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0182] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing what is disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not disclosed in the present disclosure. The specification and embodiments are to be considered merely as exemplary, and the true scope and spirit of the present disclosure are indicated by the claims.

[0183] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A multi-robot path planning method in lunar environment, It is characterized in that include: Get the three-dimensional grid map of the moon; Based on the three-dimensional grid map and using a bidirectional A* algorithm, a node with the smallest cost is determined for each robot for expansion until the path planning is completed; wherein the cost function of the bidirectional A* algorithm includes constraints constructed based on the terrain data and / or illumination data of the lunar surface; If there is a conflict in the path of the node, the conflict is resolved according to the priority of the robot, and the bidirectional A* algorithm is recombined to perform node expansion.

2. The path planning method according to claim 1, It is characterized in that Obtaining a 3D grid map of the lunar surface includes: Obtaining a three-dimensional representation of a plurality of discrete points on the lunar surface; rasterizing the three-dimensional representations of the plurality of discrete points to generate an initial raster map; Determining the terrain features of the lunar surface; wherein the terrain features include rock features and impact crater features; The terrain features of the lunar surface are merged with the initial grid map to generate a three-dimensional grid map of the lunar surface.

3. The path planning method according to claim 1, It is characterized in that In the case where the cost function of the bidirectional A* algorithm includes constraints constructed based on the terrain data and illumination data of the lunar surface, the cost function of the bidirectional A* algorithm includes a path cost function of the robot from an initial node to a current node and a heuristic function of the bidirectional A* algorithm, and the path planning method further includes: Determining topographic data and illumination data of the lunar surface; The path cost function is established based on the terrain data and illumination data of the lunar surface.

4. The path planning method according to claim 3, It is characterized in that The path cost function includes a path movement cost function and a path illumination cost function; wherein establishing the path cost function based on the terrain data and illumination data of the lunar surface includes: Determining the path movement cost function in combination with the slope of the adjacent grid area and the movement distance of the robot in the adjacent grid area; and The path illumination cost function is determined by using the output power of the solar panels of the robot in the adjacent grid areas and the time taken by the robot to move from one area to another area in the adjacent grid areas.

5. The path planning method according to claim 3, It is characterized in that The bidirectional A* algorithm adopts an 8-neighborhood search method; wherein the path planning method further includes: Determine the current node coordinates of the robot and the target node coordinates of the path; Determine the diagonal distance and the straight line distance using the current node coordinates of the robot and the target node coordinates; The heuristic function of the bidirectional A* algorithm is optimized by combining the diagonal distance and the straight-line distance.

6. The path planning method according to claim 1, It is characterized in that The path planning method further includes: determining an optimal path for the robot; Determining the maximum energy consumption and the maximum path length of the optimal path; The priority of the robot is determined in combination with the maximum energy consumption and the maximum path length of the optimal path.

7. The path planning method according to claim 1, It is characterized in that If there is a conflict in the path of the node, the conflict is resolved according to the priority of the robot, and the bidirectional A* algorithm is recombined to perform node expansion, including: If there is a conflict in the path of the node, the node is used as a parent node based on a binary tree to generate two child nodes, and the child nodes inherit all constraints of the parent node and add constraints corresponding to the conflict; For the two child nodes, the bidirectional A* algorithm is re-combined to perform node expansion.

8. A multi-robot path planning device in a lunar environment, It is characterized in that include: A map acquisition module is used to obtain a three-dimensional grid map of the lunar surface; A path planning module, used to determine the node with the minimum cost for each robot based on the three-dimensional grid map and using a bidirectional A* algorithm to expand until the path planning is completed; wherein the cost function of the bidirectional A* algorithm includes constraints constructed based on the terrain data and / or illumination data of the lunar surface; The conflict handling module is used to resolve the conflict according to the priority of the robot if there is a conflict in the path of the node, and to re-combine the bidirectional A* algorithm to perform node expansion.

9. A computer-readable storage medium having a computer program stored thereon, It is characterized in that When the computer program is executed by a processor, the multi-robot path planning method in a lunar environment according to any one of claims 1 to 7 is implemented.

10. An electronic device, It is characterized in that include: processor; as well as A memory, configured to store executable instructions of the processor; Wherein, the processor is configured to implement the multi-robot path planning method in a lunar environment according to any one of claims 1 to 7 by executing the executable instructions.

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