An Improved Obstacle Avoidance Path Planning Method for Multiple Firefighting Robots in Complex Environments
Wencheng Liu, Tong Yang, Qingxiang Wu, Ming Li, Ning Sun · 2025
Firefighting robots play a crucial role in fire rescue operations, significantly enhancing the safety and efficiency of rescue missions in dynamic and hazardous environments. However, in complex obstacle environments, it is still difficult to realize optimal path planning for multiple firefighting robots and improve firefighting efficiency. Also, many planning methods cannot find feasible paths when dealing with intricate obstacles and dead corners in indoor spaces. In this paper, this problem is addressed by improving the evaluation function of the Dynamic Window Approach algorithm to enhance path planning efficiency and by using an improved Rapidly-exploring Random Tree algorithm to guide robots to find suitable paths in complex obstacle environments. By utilizing the proposed integrated algorithm, one can make multiple firefighting robots move along with the planned optimal paths more effectively in complex environments. Additionally, simulation results demonstrate that the proposed algorithm improves the autonomy, safety, and efficiency of firefighting robots.