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In order to improve the operational efficiency, search precision, and obstacle avoidance flexibility of the path planning algorithm for coal mine foot robot, a path planning method for coal mine foot robots is proposed, which integrates the improved A* algorithm and the dynamic window approach (DWA). Firstly, the A* algorithm is improved by reducing the length of the planned path through a redundant node removal strategy. The method improves the neighborhood search method and cost function to increase the speed of path planning, and uses segmented second-order Bessel curves for path smoothing. The path nodes planned by the improved A* algorithm are sequentially used as local target points for local path planning DWA for algorithmic fusion. The method filters neighboring obstacle nodes to shorten the path length again, and improves obstacle avoidance performance by adjusting the weight ratio in the DWA cost function. In response to the problem of robots falling into a 'feigned death' state when encountering unavoidable obstacles, the method starts from the current initial point, the fusion algorithm is called up again. The global path planning is carried out again, and the new nodes obtained replace the original local target points, and the subsequent work is carried out according to the new route. The simulation results show that, while ensuring the safety and stability of robot walking, the improved A* algorithm reduces the calculation time by 65%, the path length by 24.1%, and the number of path nodes by 27.65% compared to the traditional A* algorithm, resulting in a smoother path. The fusion algorithm further enhances the global path planning capability, enabling it to bypass newly added dynamic and static obstacles in multi obstacle environments. When the robot encounters an L-shaped obstacle and enters a 'feigned death' state, it reconducts global path planning at the 'feigned death' position, updates its walking path, and successfully reaches the final target point. The experimental results of path planning for JetHexa hexapod robot based on fusion algorithm have verified the effectiveness and superiority of the fusion algorithm.

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