Research on path planning for coal mine rescue robots

This paper proposes a path planning method for coal mine rescue robots based on a Hierarchical Smooth Optimization Bidirectional A* guided dynamic window approach (HSTA*-G-DWA) algorithm. The method addresses several limitations of the traditional bidirectional A* algorithm, including low search efficiency, poor path safety, and inadequate smoothness, as well as low real-time pathfinding efficiency when integrating the DWA with global path planning algorithms. Firstly, an adjustment mechanism of collision constraint function is incorporated into the bidirectional A* algorithm to improve path safety. Next, a correction factor is incorporated into the cost function of the Bidirectional A* algorithm to ensure that the forward and backward search paths intersect, preventing them from diverging. Additionally, a dynamic weighting factor is added to the estimated cost function to eliminate irrelevant expanded nodes during pathfinding, thus improving search efficiency. A hierarchical smoothing optimization strategy is employed to remove redundant points and sharp turns, reducing both the number of waypoints and the overall path length, while enhancing smoothness. Finally, if the robot detects unknown obstacles while traveling along the global path, the DWA, guided by the global path, enables dynamic local obstacle avoidance. Simulation results show that: ① In static environments, the path search time using the HSTA*-G-DWA algorithm is reduced by 81.82% and 64.63% on average compared to the traditional A* and bidirectional A* algorithms, respectively, with improved path safety and smoothness. ② In unknown environments, the HSTA*-G-DWA algorithm can avoid unknown obstacles in real time, reducing the path length by 10.34%, 14.28%, and 2.45% compared to the rapidly-exploring random tree (RRT) algorithm, the improved A* algorithm, and existing integrated algorithms, respectively. The average path search time is reduced by 70.48% compared to existing integrated algorithms. In laboratory environments, experimental results show: ① In static environments, the HSTA*-G-DWA algorithm reduces the path search time by 58.75% on average compared to the traditional A* algorithm, and the minimum distance between the robot's edge and obstacles increases by 0.71 m on average. ② In unknown environments, compared to the traditional A* algorithm, the HSTA*-G-DWA algorithm can avoid unknown obstacles in real time, resulting in smoother paths.