## 1 Introduction  
With the swift advancement of robotics, multi-robot systems (MRS) are increasingly being employed in complex and dynamic settings [1]. In environments such as chemical laboratories, the conventional task of transporting reagents is typically carried out manually, which is not only labor-intensive but also poses safety risks, especially when dealing with hazardous chemicals. With the proliferation of automation technology, the utilization of robots to undertake the transportation of reagents and materials instead of manual labor not only enhances efficiency but also effectively mitigates the occurrence of human operational errors and safety incidents. Chemical laboratories frequently encounter complex environmental circumstances of confined spaces, dynamic obstacles, and fixed obstacles, which pose challenges for robot path planning. To ensure that the robots can complete the transportation tasks safely and promptly, how to strike an effective balance between global path planning and local obstacle avoidance becomes a key issue in multi-robot path planning. Common robot path planning algorithms encompass global path planning [2] and local path planning [3]. Global path planning algorithms include the Dijkstra algorithm [4], the A* algorithm [5; 6], and the ant colony algorithm [7; 8], et cetera, and local paths incorporate the dynamic window method [9; 10], the artificial potential field approach [11; 12], and the time elastic band (TEB) method [13; 14]. In the domain of path planning, numerous scholars have carried out profound and extensive research. The A* algorithm is the prevalent global path planning algorithm; however, the paths it generates exhibit poor smoothness and might engender a considerable number of redundant computations. [PERSON] [15] et al. guaranteed the feasibility of the path by incorporating a turning factor into the heuristic function and optimizing the search neighborhood. [PERSON] [16] broadened the search scope and established a dual-layer position-guided information evaluation function, thereby achieving a smoother path. Through integrating the obstacle rate coefficient into the evaluation function of the A* algorithm, an adaptive cost function is constructed, and by enhancing the search approach, the search efficiency and path safety are enhanced [17]. When a robot applies the dynamic window approach for path planning in navigation tasks, it may become stuck in a local optimum solution and be incapable of finding the optimal path. [PERSON] [17] integrated A* with DWA, leveraging the global path planning capabilities of the A* algorithm and the local obstacle avoidance capacity of DWA, thereby overcoming the limitations of a single approach and improving the robot's ability to navigate through complex environments. A flexible dynamic window technique employing a fuzzy controller for optimizing the dynamic obstacle avoidance of mobile robots was put forward, which allows the robot to move towards the target as rapidly as possible [18]. Through the introduction of a designed risk function into the evaluation function of the standard DWA (dynamic window approach), the probability of a collision between dynamic obstacles and the robot can be assessed, allowing the robot to effectively evade faster obstacles [19]. Regarding multi-robot path planning. [PERSON] [20] initially employed the RRT* algorithm to acquire the shortest path and subsequently utilized an advanced particle swarm optimization algorithm to obtain the time schedules for multiple robots, effectively resolving conflicts. [PERSON] [21] proposed a hierarchical distributed multi-AGV path planning algorithm, improving the combination of the A* and DWA algorithms and introducing a cooperative planning strategy to reduce the probability of conflicts between mobile robots. A multi-robot dual-layer planning algorithm featuring an improved A* algorithm and a conflict resolution strategy was put forward. In the first layer, the improved A* algorithm was used to obtain the initial path for a single robot.