CN-121409268-B - Multi-agent path planning method and related equipment

CN121409268BCN 121409268 BCN121409268 BCN 121409268BCN-121409268-B

Abstract

The application discloses a multi-agent path planning method and related equipment, which relate to the technical field of artificial intelligence and comprise the following steps: and determining a plurality of agents of a path to be planned according to the multi-agent path planning instruction, determining the priority of each agent of the current planning node, determining a first grid map corresponding to the current planning node based on the priority, and planning the grid of each multi-agent at the next planning node based on the unidirectional traffic constraint state of each loop in the first grid map and the priority. According to the method, the unidirectional passing constraint state of each loop corresponding to the current planning node is determined based on the priority, so that the motion direction of each intelligent agent in each loop corresponding to the current planning node is ensured to be uniform, further, narrow-channel conflict is avoided, and the task completion efficiency of multiple intelligent agents is improved.

Inventors

QIAN MING
HOU JIFU
CUI WENFENG
ZHENG RONGHAO
YE MIN
SONG ZHENYU
MAO YUANQI

Assignees

浙江飞航智能科技有限公司

Dates

Publication Date: 20260505
Application Date: 20251015

Claims (9)

1. The multi-agent path planning method is characterized by comprising the following steps of: Responding to a multi-agent path planning instruction, and determining a plurality of agents of a path to be planned; Determining the priority of each agent of a current planning node, and determining a first grid map corresponding to the current planning node based on the priority, wherein the first grid map comprises a plurality of loops with unidirectional traffic constraint states, the loops are composed of a plurality of grids, the loops are of a narrow channel structure, and the priority is determined based on time steps; Planning grids of each multi-agent at the next planning node based on the unidirectional traffic constraint state and the priority of each loop in the first grid map; the step of determining the first grid map corresponding to the current planning node based on the priority further includes: acquiring a second grid map corresponding to a previous planning node, wherein the previous planning node is the previous planning node corresponding to the current planning node; determining whether a target loop meeting a reset constraint condition exists in the second grid map; Resetting the one-way traffic constraint corresponding to the target loop if the one-way traffic constraint exists, and resetting the one-way traffic constraint of the target loop based on the reset target loop and the priority; determining a first grid map corresponding to the current planning node based on the reset target loop; And if the first grid map is not present, setting the second grid map as the first grid map.
2. The multi-agent path planning method of claim 1 wherein the step of determining the priority of each agent for the current planning node further comprises: acquiring a unique identifier disturbance value corresponding to each intelligent agent, and determining a first planning node corresponding to the current task received by each intelligent agent; Determining the number of time steps corresponding to each agent based on the first planning node and the current planning node; and determining a priority of each agent based on the number of time steps and the unique identification disturbance value, wherein the priority is reset when the agent completes the current task.
3. The multi-agent path planning method of claim 1 wherein the step of resetting the one-way traffic constraint of the target loop based on the reset completed target loop and the priority further comprises: determining the optimal path corresponding to each intelligent agent in turn based on the order of priority from high to low; determining an optimal path which first appears and comprises a target loop, and obtaining a target path; and determining the movement direction of the intelligent body corresponding to the target path in the target loop, and resetting the unidirectional traffic constraint of the target loop based on the movement direction.
4. The multi-agent path planning method of claim 1 wherein the reset constraint is that the loop is occupied by no more than one agent.
5. The multi-agent path planning method of claim 1, wherein prior to the step of determining the first grid map corresponding to the current planning node, further comprising: acquiring the size of the intelligent agent and acquiring a target environment in which the intelligent agent is located; And carrying out rasterization modeling on the target environment based on the size to obtain an initial raster map, wherein the raster map is a double-communication undirected graph, the raster map comprises a loop, the loop is formed by constructing a plurality of grids, and the size of the grids is smaller than or equal to that of the intelligent agent.
6. A multi-agent path planning apparatus, the multi-agent path planning apparatus comprising: the response module is used for responding to the multi-agent path planning instruction and determining a plurality of agents of the path to be planned; The system comprises a determining module, a first planning module and a second planning module, wherein the determining module is used for determining the priority of each agent of a current planning node, and determining a first grid map corresponding to the current planning node based on the priority, wherein the first grid map comprises a plurality of loops with unidirectional traffic constraint states, the loops are composed of a plurality of grids, the loops are of a narrow channel structure, and the priority is determined based on time steps; The planning module is used for planning grids of each multi-agent at the next planning node based on the unidirectional traffic constraint state of each loop in the first grid map and the priority; the multi-agent path planning device is further configured to implement: acquiring a second grid map corresponding to a previous planning node, wherein the previous planning node is the previous planning node corresponding to the current planning node; determining whether a target loop meeting a reset constraint condition exists in the second grid map; Resetting the one-way traffic constraint corresponding to the target loop if the one-way traffic constraint exists, and resetting the one-way traffic constraint of the target loop based on the reset target loop and the priority; determining a first grid map corresponding to the current planning node based on the reset target loop; And if the first grid map is not present, setting the second grid map as the first grid map.
7. A multi-agent path planning apparatus comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the multi-agent path planning method of any one of claims 1 to 5.
8. A storage medium, characterized in that the storage medium is a computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, realizes the steps of the multi-agent path planning method according to any one of claims 1 to 5.
9. A computer program product, characterized in that the computer program product comprises a computer program which, when executed by a processor, implements the steps of the multi-agent path planning method according to any one of claims 1 to 5.

Description

Multi-agent path planning method and related equipment Technical Field The application relates to the technical field of artificial intelligence, in particular to a multi-agent path planning method and related equipment. Background With the popularization of multi-agent systems in related scenes such as warehouse logistics, industrial automation and the like, it becomes important to efficiently complete work tasks in related scenes based on the multi-agent systems. In practice, there are generally narrow access areas in a map environment, such as aisles between warehouse racks or work areas between production lines, which are generally only accessible to a single agent. In the face of narrow channel collisions, it is often necessary for some agent to completely withdraw from the channel to let others go, resulting in a significant increase in task completion time. Therefore, how to improve the efficiency of the multi-agent to accomplish the task is an urgent issue to be resolved. The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present application and is not intended to represent an admission that the foregoing is prior art. Disclosure of Invention The application mainly aims to provide a multi-agent path planning method and related equipment, and aims to solve the technical problem of how to improve the efficiency of completing tasks by multi-agents. In order to achieve the above object, the present application provides a multi-agent path planning method, which includes: Responding to a multi-agent path planning instruction, and determining a plurality of agents of a path to be planned; Determining the priority of each agent of a current planning node, and determining a first grid map corresponding to the current planning node based on the priority, wherein the first grid map comprises, but is not limited to, a plurality of loops with unidirectional traffic constraint states, the loops consist of a plurality of grids, the loops are of a narrow channel structure, and the priority is determined based on time steps; and planning grids of each multi-agent at the next planning node based on the unidirectional traffic constraint state of each loop in the first grid map and the priority. In an embodiment, the step of determining the priority of each agent of the current planning node further includes: acquiring a unique identifier disturbance value corresponding to each intelligent agent, and determining a first planning node corresponding to the current task received by each intelligent agent; Determining the number of time steps corresponding to each agent based on the first planning node and the current planning node; and determining a priority of each agent based on the number of time steps and the unique identification disturbance value, wherein the priority is reset when the agent completes the current task. In an embodiment, the step of determining the first grid map corresponding to the current planning node based on the priority further includes: acquiring a second grid map corresponding to a previous planning node, wherein the previous planning node is the previous planning node corresponding to the current planning node; determining whether a target loop meeting a reset constraint condition exists in the second grid map; Resetting the one-way traffic constraint corresponding to the target loop if the one-way traffic constraint exists, and resetting the one-way traffic constraint of the target loop based on the reset target loop and the priority; determining a first grid map corresponding to the current planning node based on the reset target loop; And if the first grid map is not present, setting the second grid map as the first grid map. In one embodiment, the step of resetting the unidirectional traffic constraint of the target loop based on the reset completed target loop and the priority further comprises: determining the optimal path corresponding to each intelligent agent in turn based on the order of priority from high to low; determining an optimal path which first appears and comprises a target loop, and obtaining a target path; and determining the movement direction of the intelligent body corresponding to the target path in the target loop, and resetting the unidirectional traffic constraint of the target loop based on the movement direction. In one embodiment, the reset constraint is that the loop is occupied by no more than one agent. In an embodiment, before the step of determining the first grid map corresponding to the current planning node, the method further includes: acquiring the size of the intelligent agent and acquiring a target environment in which the intelligent agent is located; And carrying out rasterization modeling on the target environment based on the size to obtain an initial raster map, wherein the raster map is a double-communication undirected graph, the raster map comprises a loop, the loop is formed by