Multi-robot path planning method and terminal device

Abstract

The application discloses a kind of multi-robot path planning method and terminal equipment, the method includes: obtaining the starting position and target position of multiple robots respectively;Each robot is path planned to obtain corresponding first planning path;According to the node in the first planning path corresponding to all robots, obtain the open list;From the open list, obtain low layer focus list is extracted;According to the conflict number between each node in low layer focus list of the robot, obtain the low layer planning path of each robot;According to the low layer planning path of all robots, high layer focus list is extracted from the open list;According to the conflict number between each node in high layer focus list of the robot, obtain the second planning path of each robot;When the second planning path of each robot does not exist conflict, the second planning path of each robot is output as the path planning result of itself.The application can realize more efficient multi-robot path planning.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a multi-robot path planning method and terminal device. Background Technology

[0002] In existing technologies, when performing multi-robot path planning, a conflict search-based method is usually adopted. First, the corresponding single-robot paths are planned based on the starting and target positions of all robots. Then, conflict nodes between the single-robot paths are randomly selected to expand the planning to obtain a low-level multi-robot path. Next, it is determined whether there are still conflicts between different robots on the path in the low-level multi-robot path. If there are still conflicts, the next conflict node is traversed. If there are no conflicts, the low-level multi-robot path is taken as the result of multi-robot path planning.

[0003] As can be seen, in the existing technology, when performing multi-robot path planning, the planning is expanded by randomly selecting a conflict node to complete the multi-robot path planning. This requires randomly traversing each conflict node and performing subsequent path planning. Therefore, it usually needs to deal with too many conflict nodes and cannot efficiently complete multi-robot path planning. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes a multi-robot path planning method and terminal device. By further classifying conflicting nodes in both low-level and high-level node selection, prioritizing nodes with fewer conflicts, and specifically prioritizing conflicting nodes with the primary conflict type in the high-level focus list for path planning, the method reduces the number of nodes selected during multi-robot path planning. This reduces the computational complexity of multi-robot path planning for different nodes, achieving more efficient multi-robot path planning.

[0005] To achieve the above objectives, one embodiment of the present invention provides a multi-robot path planning method, comprising:

[0006] Obtain the starting and target positions of multiple robots;

[0007] For each robot, path planning is performed based on the starting position and the target position to obtain the corresponding first planned path;

[0008] Obtain the activation list based on the nodes in the first planning path corresponding to all robots;

[0009] Extract several nodes that meet the preset first filtering conditions from the open list to obtain a low-level focus list;

[0010] Based on the number of conflicts between the robots in the low-level focus list, the starting position and the target position of each robot, the low-level planned path of each robot is obtained.

[0011] Based on the low-level planning paths of all robots, a number of nodes that meet the preset second filtering conditions are extracted from the open list to obtain the high-level focus list;

[0012] Based on the number of conflicts between the robots and each node in the high-level focus list, the starting position and the target position of each robot, the second planned path of each robot is obtained;

[0013] When there is no conflict between the second planned paths of the robots, the second planned paths of each robot are output as their respective path planning results.

[0014] Furthermore, the step of extracting several nodes that meet the preset first filtering conditions from the open list to obtain a low-level focus list includes: for each robot, using the number of all nodes traversed from the starting position to the target position in the first planning path as the corresponding first planning path cost; selecting the minimum first planning path cost from the first planning path costs of all robots as the minimum search path cost; and extracting several nodes that meet the preset first filtering conditions from the open list to obtain a low-level focus list; wherein, the first filtering condition is shown in the following formula (1):

[0015] f1(n)≤ω·f (1)

[0016] Where f1(n) is the cost of the first planned path corresponding to the extracted node n, ω is the preset focus constant, and f is the minimum search path cost.

[0017] Furthermore, the step of obtaining the low-level planning path of each robot based on the number of conflicts between each node in the low-level focus list, the starting position and the target position of each robot, includes: obtaining the number of conflicts between each node in the low-level focus list, arranging each node in ascending order of the number of conflicts; taking the first node as the first root node, and performing path planning based on the starting position and the target position of each robot, using the first root node as the common node of each robot, and determining whether a low-level planning path for each robot has been planned; if a low-level planning path for each robot has not been planned, re-planning the path using the next node as the first root node, until a low-level planning path for each robot is planned.

[0018] Furthermore, the step of extracting a number of nodes that meet the preset second filtering conditions from the open list based on the low-level planning paths of all robots to obtain a high-level focus list includes: for each robot, using the number of all nodes traversed from the starting position to the target position in the low-level planning path as the corresponding low-level planning path cost; calculating the total low-level planning path cost corresponding to the first root node based on the low-level planning path costs of all robots; and extracting a number of nodes that meet the preset second filtering conditions from the open list based on the total low-level planning path cost to obtain a high-level focus list; wherein, the second filtering condition is shown in the following formula (2):

[0019] f2(m)≤ω·LL (2)

[0020] Where f2() is the total cost of the low-level planning path corresponding to the extracted node m, ω is the preset focus constant, and LL is the total cost of the low-level planning path corresponding to the first root node.

[0021] Furthermore, the step of obtaining the second planned path for each robot based on the number of conflicts between each node in the high-level focus list, the starting position and the target position of each robot, includes: obtaining the number of conflicts between each node in the high-level focus list, arranging each node in ascending order of the number of conflicts, and taking the first node as the second root node; and performing path planning based on the starting position and the target position of each robot, using the second root node as the common node of each robot, to obtain the second planned path for each robot.

[0022] Furthermore, when there is no conflict between the second planned paths of each robot, outputting the second planned path of each robot as its respective path planning result includes: determining whether there is a conflict between the second planned paths of each robot; if not, outputting the second planned path of each robot as its respective path planning result; if so, expanding the open list according to the second root node, re-determining the first root node for path planning, until there is no conflict between the second planned paths of each robot.

[0023] Furthermore, the step of expanding the open list based on the second root node and re-determining the first root node for path planning until there is no conflict between the second planned paths of the robots includes: when there is a conflict between the second planned paths of the robots: expanding the list based on the second root node to obtain two corresponding child nodes; inserting the two child nodes into the open list to obtain an expanded open list; and re-determining the first root node for path planning based on the expanded open list until there is no conflict between the second planned paths of the robots.

[0024] Furthermore, after expanding to obtain two corresponding child nodes based on the second root node, the method further includes: for each robot, using the number of all nodes traversed from the starting position to the target position in the second planned path as the cost of the second planned path; calculating the total cost of the second planned path corresponding to the second root node based on the second planned path costs of all robots; obtaining the total costs of two low-level planned paths corresponding one-to-one with the two child nodes as the total cost of the two child nodes; obtaining the conflict type of the second root node based on the total cost of the second planned path and the total cost of the two child nodes; when the conflict type is determined to be a major conflict, performing path planning based on the child nodes to obtain the child node paths of each robot as the new second planned paths of each robot.

[0025] Furthermore, determining the conflict type of the second root node based on the total cost of the second planned path and the total cost of the two child nodes includes: when the total cost of both child nodes is greater than the total cost of the second planned path, determining the conflict type of the second root node as a primary conflict; when the total cost of any child node is greater than the total cost of the second planned path, and the total cost of the other child node is equal to the total cost of the second planned path, determining the conflict type of the second root node as a semi-primary conflict; and when the total cost of both child nodes is equal to the total cost of the second planned path, determining the conflict type of the second root node as a non-primary conflict.

[0026] Another embodiment of the present invention provides a multi-robot path planning terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the multi-robot path planning method described in the above-described embodiment of the invention.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] In this embodiment of the invention, the starting positions and target positions of multiple robots are obtained. For each robot, path planning is performed based on the starting and target positions to obtain a corresponding first planned path. An open list is obtained based on the nodes in the first planned paths of all robots. Several nodes that meet a preset first filtering condition are extracted from the open list to obtain a low-level focus list. The low-level planned path of each robot is obtained based on the number of conflicts between nodes in the low-level focus list, the starting and target positions of each robot. Based on the low-level planned paths of all robots, several nodes that meet a preset second filtering condition are extracted from the open list to obtain a high-level focus list. The second planned path of each robot is obtained based on the number of conflicts between nodes in the high-level focus list, the starting and target positions of each robot. When there are no conflicts between the second planned paths of each robot, the second planned path of each robot is output as its respective path planning result. In this embodiment of the invention, when selecting nodes in both the low and high layers, nodes with conflicts are further divided in more detail, prioritizing nodes with fewer conflicts. In particular, different conflict types are specifically classified for nodes with conflicts in the high-level focus list. Then, based on the conflict type, it is selected whether the corresponding child nodes need path planning. This allows for targeted selection of nodes with major conflicts and path planning based on the generated child nodes. This enables the fastest possible acquisition of target nodes that satisfy all constraints and do not conflict between robots, completing multi-robot path planning without traversing every node. Therefore, it reduces the number of nodes selected during multi-robot path planning, thereby reducing the computation required for multi-robot path planning for different nodes and achieving more efficient multi-robot path planning. Attached Figure Description

[0029] Figure 1 This is a flowchart illustrating an embodiment of a multi-robot path planning method provided by the present invention. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] See Figure 1 This is a flowchart illustrating an embodiment of the multi-robot path planning method provided by the present invention. The method includes steps S1 to S8, as detailed below:

[0032] S1, obtain the starting position and target position of each of the multiple robots;

[0033] S2, for each robot, perform path planning based on the starting position and the target position to obtain the corresponding first planned path;

[0034] Specifically, for each robot, based on the starting position and the target position, the spatiotemporal A* (A Star) algorithm is used to perform path planning for the individual robot to obtain the corresponding first planned path, where the first planned path represents the planned path obtained after each robot performs path planning individually.

[0035] S3, obtain the list of robots to be activated based on the nodes in the first planned path for all robots;

[0036] Specifically, an open list is constructed from all the nodes traversed by each robot in its first planned path from the starting position to the target position. If the same node is traversed repeatedly in the first planned paths of different robots, it is only recorded as one node in the open list.

[0037] S4, extract several nodes that meet the preset first filtering conditions from the open list to obtain a low-level focus list;

[0038] Preferably, the step of extracting a number of nodes that meet the preset first filtering conditions from the open list to obtain a low-level focus list includes: for each robot, using the number of all nodes traversed from the starting position to the target position in the first planning path as the corresponding first planning path cost; selecting the minimum first planning path cost from the first planning path costs of all robots as the minimum search path cost; and extracting a number of nodes that meet the preset first filtering conditions from the open list to obtain a low-level focus list; wherein, the first filtering condition is shown in the following formula (1):

[0039] f1(n)≤ω·f (1)

[0040] Where f1(n) is the cost of the first planned path corresponding to the extracted node n, ω is the preset focus constant, and f is the minimum search path cost.

[0041] Specifically, ω is a preset focus constant, where ω>1. The constraint condition of equation (1) makes the cost of the first planning path corresponding to each node in the lower-level focus list have an upper limit, so that the cost of the first planning path corresponding to each node in the lower-level focus list is controlled within a reasonable range.

[0042] S5. Based on the number of conflicts between the robots in the low-level focus list, the starting position and the target position of each robot, the low-level planning path of each robot is obtained.

[0043] Preferably, obtaining the low-level planned path for each robot based on the number of conflicts between each node in the low-level focus list, the starting position of each robot, and the target position of each robot includes: obtaining the number of conflicts between each node in the low-level focus list, arranging each node in ascending order of the number of conflicts; taking the first node as the first root node, and performing path planning based on the starting position and target position of each robot, using the first root node as the common node of each robot, and determining whether a low-level planned path for each robot has been planned; if a low-level planned path for each robot has not been planned, re-planning the path using the next node as the first root node, until a low-level planned path for each robot is planned.

[0044] Specifically, the node with the fewest conflicts is selected as the common node for all robots, and path planning is performed based on the common node. This reduces the number of nodes selected during multi-robot path planning, thereby speeding up the process of obtaining the low-level planning path for each robot and improving the efficiency of multi-robot path planning.

[0045] S6. Based on the low-level planning paths of all robots, extract several nodes that meet the preset second filtering conditions from the open list to obtain the high-level focus list.

[0046] Preferably, the step of extracting a number of nodes that meet a preset second filtering condition from the open list based on the low-level planning paths of all robots to obtain a high-level focus list includes: for each robot, using the number of all nodes traversed from the starting position to the target position in the low-level planning path as the corresponding low-level planning path cost; calculating the total low-level planning path cost corresponding to the first root node based on the low-level planning path costs of all robots; and extracting a number of nodes that meet a preset second filtering condition from the open list based on the total low-level planning path cost to obtain a high-level focus list; wherein, the second filtering condition is shown in the following formula (2):

[0047] f2(m)≤ω·LL (2)

[0048] Where f2() is the total cost of the low-level planning path corresponding to the extracted node m, ω is the preset focus constant, and LL is the total cost of the low-level planning path corresponding to the first root node.

[0049] Specifically, ω is a preset focus constant, where ω>1. The constraint condition of equation (2) makes the total cost of the low-level planning path corresponding to each node in the high-level focus list have an upper limit, so that the total cost of the low-level planning path corresponding to each node in the high-level focus list is controlled within a reasonable range.

[0050] S7. Based on the number of conflicts between the robots in the high-level focus list, the starting position and the target position of each robot, the second planned path of each robot is obtained.

[0051] Preferably, the step of obtaining the second planned path for each robot based on the number of conflicts between each node in the high-level focus list, the starting position and the target position of each robot includes: obtaining the number of conflicts between each node in the high-level focus list, arranging each node in ascending order of the number of conflicts, and taking the first node as the second root node; and performing path planning based on the starting position and the target position of each robot, using the second root node as the common node of each robot, to obtain the second planned path for each robot.

[0052] Specifically, the node with the fewest conflicts is selected as the common node for all robots, and path planning is performed based on the common node. This reduces the number of nodes selected during multi-robot path planning, thereby speeding up the process of obtaining the second planned path for each robot and improving the efficiency of multi-robot path planning.

[0053] S8, when there is no conflict between the second planned paths of each robot, output the second planned path of each robot as its respective path planning result.

[0054] Preferably, when there is no conflict between the second planned paths of each robot, outputting the second planned path of each robot as its own path planning result includes: determining whether there is a conflict between the second planned paths of each robot; if not, outputting the second planned path of each robot as its own path planning result; if so, expanding the open list according to the second root node, re-determining the first root node for path planning, until there is no conflict between the second planned paths of each robot.

[0055] Preferably, the step of expanding the open list based on the second root node and re-determining the first root node for path planning until there is no conflict between the second planned paths of the robots includes: when there is a conflict between the second planned paths of the robots: expanding the list based on the second root node to obtain two corresponding child nodes; inserting the two child nodes into the open list to obtain an expanded open list; and re-determining the first root node for path planning based on the expanded open list until there is no conflict between the second planned paths of the robots.

[0056] Preferably, after expanding to obtain two corresponding child nodes based on the second root node, the method further includes: for each robot, using the number of all nodes traversed from the starting position to the target position in the second planned path as the cost of the second planned path; calculating the total cost of the second planned path corresponding to the second root node based on the second planned path costs of all robots; obtaining the total costs of two low-level planned paths corresponding one-to-one with the two child nodes as the total cost of the two child nodes; obtaining the conflict type of the second root node based on the total cost of the second planned path and the total cost of the two child nodes; when the conflict type is determined to be a major conflict, performing path planning based on the child nodes to obtain the child node paths of each robot as the new second planned paths for each robot.

[0057] Preferably, determining the conflict type of the second root node based on the total cost of the second planned path and the total cost of the two child nodes includes: when the total cost of both child nodes is greater than the total cost of the second planned path, determining the conflict type of the second root node as a primary conflict; when the total cost of any child node is greater than the total cost of the second planned path, and the total cost of the other child node is equal to the total cost of the second planned path, determining the conflict type of the second root node as a semi-primary conflict; and when the total cost of both child nodes is equal to the total cost of the second planned path, determining the conflict type of the second root node as a non-primary conflict.

[0058] Specifically, based on the above conditions, the conflicts between multiple robots occurring at different conflict nodes are divided into three categories. The conflict nodes where the main conflict occurs are selected first for path planning, thereby reducing the depth of the constraints used when selecting nodes, reducing the number of nodes selected in multi-robot path planning, and thus reducing the time consumption of multi-robot path planning.

[0059] Specifically, when the conflict between multiple robots at the conflict node is a non-primary conflict or a semi-primary conflict, path planning is not performed based on the aforementioned conflict node. However, if the path planning results for each robot are still not obtained after traversing all the conflict nodes according to all the above steps, path planning is performed based on the corresponding conflict nodes in the order of semi-primary conflict and non-primary conflict.

[0060] Accordingly, embodiments of the present invention also provide a multi-robot path planning terminal device, including: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a multi-robot path planning program. When the processor executes the computer program, it implements the steps described in the various multi-robot path planning method embodiments above, for example... Figure 1 Steps S1 to S8 are shown.

[0061] For example, the computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the multi-robot path planning terminal device.

[0062] The multi-robot path planning terminal device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The multi-robot path planning terminal device may include, but is not limited to, a processor and memory.

[0063] 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. The processor is the control center of the multi-robot path planning terminal device, connecting all parts of the device via various interfaces and lines.

[0064] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the multi-robot path planning terminal device by running or executing the computer programs and / or modules stored in the memory and calling the 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, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital card (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0065] Wherein, if the modules / units integrated in the multi-robot path planning terminal device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0066] Through the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus necessary hardware platforms, and of course, it can also be implemented entirely by hardware. Based on this understanding, all or part of the technical solution of the present invention that contributes to the background art can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present invention.

[0067] The present invention has the following beneficial effects:

[0068] In this embodiment of the invention, the starting positions and target positions of multiple robots are obtained. For each robot, path planning is performed based on the starting and target positions to obtain a corresponding first planned path. An open list is obtained based on the nodes in the first planned paths of all robots. Several nodes that meet a preset first filtering condition are extracted from the open list to obtain a low-level focus list. The low-level planned path of each robot is obtained based on the number of conflicts between nodes in the low-level focus list, the starting and target positions of each robot. Based on the low-level planned paths of all robots, several nodes that meet a preset second filtering condition are extracted from the open list to obtain a high-level focus list. The second planned path of each robot is obtained based on the number of conflicts between nodes in the high-level focus list, the starting and target positions of each robot. When there are no conflicts between the second planned paths of each robot, the second planned path of each robot is output as its respective path planning result. In this embodiment of the invention, when selecting nodes in both the low and high layers, nodes with conflicts are further divided in more detail, prioritizing nodes with fewer conflicts. In particular, different conflict types are specifically classified for nodes with conflicts in the high-level focus list. Then, based on the conflict type, it is selected whether the corresponding child nodes need path planning. This allows for targeted selection of nodes with major conflicts and path planning based on the generated child nodes. This enables the fastest possible acquisition of target nodes that satisfy all constraints and do not conflict between robots, completing multi-robot path planning without traversing every node. Therefore, it reduces the number of nodes selected during multi-robot path planning, thereby reducing the computation required for multi-robot path planning for different nodes and achieving more efficient multi-robot path planning.

[0069] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A multi-robot path planning method, characterized in that, include: Obtain the starting and target positions of multiple robots; For each robot, path planning is performed based on the starting position and the target position to obtain the corresponding first planned path; Obtain the activation list based on the nodes in the first planning path corresponding to all robots; Extract several nodes that meet the preset first filtering conditions from the open list to obtain a low-level focus list; Based on the number of conflicts between the robots in the low-level focus list, the starting position and the target position of each robot, the low-level planned path of each robot is obtained. Based on the low-level planning paths of all robots, a number of nodes that meet the preset second filtering conditions are extracted from the open list to obtain the high-level focus list; Based on the number of conflicts between the robots and each node in the high-level focus list, the starting position and the target position of each robot, the second planned path of each robot is obtained; When there is no conflict between the second planned paths of the robots, the second planned paths of each robot are output as their respective path planning results. The step of obtaining the second planned path for each robot based on the number of conflicts between each node in the high-level focus list, the starting position and the target position of each robot, includes: obtaining the number of conflicts between each node in the high-level focus list, arranging each node in ascending order of the number of conflicts, and taking the first node as the second root node; and performing path planning based on the starting position and the target position of each robot, using the second root node as the common node of each robot, to obtain the second planned path for each robot. The step of outputting the second planned path of each robot as its own path planning result when there is no conflict between the second planned paths of each robot includes: determining whether there is a conflict between the second planned paths of each robot; if not, outputting the second planned path of each robot as its own path planning result; if so, expanding the open list according to the second root node, re-determining the first root node for path planning, until there is no conflict between the second planned paths of each robot. The step of expanding the open list based on the second root node and re-determining the first root node for path planning until there is no conflict between the second planned paths of the robots includes: when there is a conflict between the second planned paths of the robots: expanding the list based on the second root node to obtain two corresponding child nodes; inserting the two child nodes into the open list to obtain an expanded open list; and re-determining the first root node for path planning based on the expanded open list until there is no conflict between the second planned paths of the robots. The step of expanding to obtain two corresponding child nodes based on the second root node further includes: for each robot, using the number of all nodes traversed from the starting position to the target position in the second planned path as the cost of the second planned path; calculating the total cost of the second planned path corresponding to the second root node based on the second planned path costs of all robots; obtaining the total costs of two low-level planned paths corresponding one-to-one with the two child nodes as the total cost of the two child nodes; determining the conflict type of the second root node based on the total cost of the second planned path and the total cost of the two child nodes; when the conflict type is determined to be a major conflict, performing path planning based on the child nodes to obtain the child node paths of each robot as the new second planned paths for each robot.

2. The multi-robot path planning method as described in claim 1, characterized in that, The step of extracting several nodes that meet the preset first filtering conditions from the open list to obtain a low-level focus list includes: For each robot, the number of all nodes traversed from the starting position to the target position in the first planned path is used as the corresponding first planned path cost; Select the minimum first-planned path cost from all the robots' first-planned path costs as the minimum search path cost; From the open list, a number of nodes that meet the preset first filtering condition are extracted to obtain the low-level focus list; wherein, the first filtering condition is as shown in the following formula (1): （1） in, The cost of the first planned path corresponding to the extracted node n, The preset focus constant, This represents the minimum search path cost.

3. The multi-robot path planning method as described in claim 2, characterized in that, The step of obtaining the low-level planned path for each robot based on the number of conflicts between each node in the low-level focus list, the starting position and the target position of each robot, includes: Obtain the number of conflicts between each node in the low-level focus list of the robot, and arrange each node in ascending order of the number of conflicts; The first node is taken as the first root node, and path planning is performed based on the starting position and target position of each robot, using the first root node as the common node of each robot, and it is determined whether a low-level planning path for each robot has been planned. If a low-level planning path for each robot is not found, the next node is used as the first root node to re-plan the path until a low-level planning path for each robot is found.

4. The multi-robot path planning method as described in claim 3, characterized in that, The step involves extracting several nodes that meet a preset second filtering condition from the open list based on the low-level planned paths of all robots to obtain a high-level focus list, including: For each robot, the number of all nodes traversed from the starting position to the target position in the low-level planning path is used as the corresponding low-level planning path cost. Based on the low-level planning path costs of all robots, the total low-level planning path cost corresponding to the first root node is calculated. Based on the total cost of the low-level planning path, several nodes that meet the preset second filtering conditions are extracted from the open list to obtain the high-level focus list; wherein, the second filtering conditions are as shown in the following formula (2): （2） in, The total cost of the low-level planning path corresponding to the extracted node m is... is a preset focus constant, and LL is the total cost of the low-level planning path corresponding to the first root node.

5. The multi-robot path planning method as described in claim 1, characterized in that, The step of determining the conflict type of the second root node based on the total cost of the second planned path and the total cost of the two child nodes includes: When the total cost of both child nodes is greater than the total cost of the second planned path, the conflict type of the second root node is determined to be a major conflict; When the total cost of any of the child nodes is greater than the total cost of the second planned path, and the total cost of the other child node is equal to the total cost of the second planned path, the conflict type of the second root node is determined to be a semi-primary conflict. When the total cost of both child nodes is equal to the total cost of the second planned path, the conflict type of the second root node is determined to be a non-major conflict.

6. A multi-robot path planning terminal device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the multi-robot path planning method as described in any one of claims 1 to 5.

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