## 1 Introduction
In the second layer, a time dimension was introduced to anticipate robot conflicts [22]. A safe A* algorithm was developed with a technique for identifying key path points to guarantee smooth trajectory following, and real-time obstacle avoidance was achieved through adaptive window-based motion planning [23]. The paper [24] presents a new path planning strategy combining an optimized A* algorithm for global path planning, adaptive navigation and obstacle avoidance for local planning, and a collision recognition and task prioritization strategy for coordinating multiple robots. Although the existing literature has extensively explored robot path planning, particularly focusing on obstacle avoidance strategies, our research emphasizes the issue of the \"path deadlock\" in dynamic environments. Specifically, it addresses how to re-plan the path in real time when unknown obstacles block the original route. While most studies focus on obstacle avoidance, research on path re-planning remains relatively limited. To tackle this challenge, we propose an innovative path re-planning method tailored to the specific application scenario of chemical laboratories. This method incorporates a parking-waiting strategy and a multi-robot priority scheduling mechanism, effectively resolving the issues of path blockage and motion conflicts within multi-robot systems while successfully handling path obstructions in dynamic environments. Regarding the improvement and analysis of the aforementioned algorithms, a novel multi-robot path planning method is proposed, combining the A* algorithm and the dynamic window approach (DWA). Within the A* algorithm, we adopted a dynamic evaluation function that adapts based on distance cost weighting. This modification enhanced the algorithm's efficiency in complex environments and enabled the dynamic adjustment of the weights in response to diverse environmental variations, thereby augmenting the flexibility and robustness of the path planning. Furthermore, a redundant node deletion strategy was introduced to further optimize the efficiency of path calculation, avoiding unnecessary computations and path redundancies and enhancing the overall planning performance. In the section of the dynamic window method, we expanded the evaluation function to incorporate an adaptive adjustment mechanism for the heading angle. This tactic dynamically modifies the robot's heading angle, enabling the path planning to adapt more precisely to various driving environments and minimizing the trajectory deviation caused by unreasonable heading, thereby enhancing the precision and motion efficiency of navigation. To further enhance the adaptability of the algorithm, this study also put forward a parking-and-waiting strategy, which can promptly halt and wait when encountering unforeseen obstacles, thereby evading unnecessary collision risks; meanwhile, the path replanning strategy guarantees that if an infeasible path emerges during the robot's execution, a new feasible path can be rapidly identified, preventing prolonged system stagnation. In the context of multi-robot path planning, we introduced a dynamic priority strategy for addressing the obstacle avoidance issue among robots. This strategy effectively precludes conflicts and deadlocks among robots and guarantees that multiple robots can collaborate in shared spaces without colliding or delaying tasks. By dynamically regulating the priorities between robots, we can ensuring that multiple robots can complete tasks efficiently and stably in complex and dynamically evolving environments. In conclusion, this approach significantly enhances the safety, efficiency, and adaptability of multi-robot path planning through innovative optimizations in various modules.