## 2 Related Work  
Path planning algorithms are widely used in indoor and outdoor path planning problems, and so far, scholars have conducted a lot of research on path planning problems, and various methods have been proposed to better solve path planning problems [8]. Multi-robot path planning algorithms can be divided into three categories: classical algorithms, heuristic algorithms, and artificial intelligence algorithms [3]. Among the classical algorithms, [PERSON] et al. [20] proposed a collision avoidance strategy and risk assessment based on improved APF and fuzzy inference systems for multi-robot path planning in completely unknown environments. [PERSON] [21] aimed at the optimal multi-robot path planning problem with computational complexity and used the floor plan to establish the complexity of the problem on the floor plan to reduce path sharing in the opposite direction. [PERSON] et al. [22] argued that for multi-robot path planning problems, the RRT algorithm outperforms the Bibox algorithm in optimizing solutions and exploring search spaces in urban environments. [PERSON] et al. [23] proposed a centralized algorithm framework for the multi-robot path planning problem in general two-dimensional continuous environment, which obtained a high level of effectiveness by discretizing the continuous environment and quickly solving the resulting discrete planning problem. [PERSON] et al. [24] aimed at the NP difficulty problem of multi-robot path planning under communication constraints, using continuous region division into Voronoi components to effectively divide initially unknown environments among robots according to newly discovered obstacles, thereby improving load balancing among robots. [PERSON] et al. [25] proposed a two-layer path planning algorithm--an improved A* algorithm and a dynamic fast exploration random tree (RRT) algorithm with kinematic constraints to optimize the path of multiple AGVs, and simulation experiments show that the proposed two-layer path planning algorithm performs well in search efficiency and significantly reduces the incidence of multiple AGV path conflicts. [PERSON] et al. [26] proposed the EA* algorithm, allocation technology, and fault detection algorithm using the circumferential division method for the target location-allocation problem in multi-robot path planning and area exploration and tested them in different environments. [PERSON] et al. [27] proposed a comprehensive framework of dynamic path planning algorithms for the real-time and concurrent movement of multiple robot cars on the ground of parking lots without driving lanes, and the simulation results show that the proposed design makes the robot car close to the optimal path and can process dozens of concurrent requests in real time, even in the case of fixed cars. [PERSON] et al. [28] proposed a conflict-based search method for the multi-agent path planning problem in the train route problem of multiple vehicles and train route setting paths, considering the complexity of the scene. [PERSON] et al. [29] aimed at the challenging problem of multi-agent cooperative motion planning for complex tasks, a timed waypoint-based method is proposed to apply the linear time logic formula to solve multi-robot path planning, and simulation experiments show that the proposed method supports multi-agent planning from complex specifications in a long planning cycle, and significantly outperforms the most advanced abstraction-based and MPC-based motion planning methods. A biomimetic algorithm is a kind of algorithm that simulates the random search method of natural biological evolution or group social behavior, and its application range is wide, such as [PERSON] et al. [30] designed whale optimization weighted fuzzy clustering head selection algorithm to promote successful communication of IoMT-based systems. [PERSON] et al. [31] adopted the PSO algorithm in the photovoltaic power generation system, which can quickly and accurately control the maximum power point in the event of sudden changes in lighting.