Key technologies for path planning of coal mine wheeled robots integrating fast traversal random trees and Q-reinforcement learning

There are a lot of dangerous environment and large volume transportation tasks in the coal mining process. Improving the working quality of coal mine equipment is the key to the intelligent construction of coal mine. In order to improve the efficiency of coal mine production and the safety of transportation tasks, a path planning method of coal mine wheeled robot based on reinforcement learning is proposed. This method uses greedy strategy to guide the direction of random tree expansion, uses Markov decision to reduce the invalid nodes generated during expansion, smooths the path trajectory through third-order Bessel curve, and adds expert experience playback pool to improve the computational efficiency. The experimental results show that the global path length generated by the research method can be reduced by at least 10.71% compared with other algorithms in the planned path length test. In the multi-obstacle scenario planning time test, the planning time of the research method is only 0.452 s. In the analysis of obstacle avoidance effect, the research method can effectively avoid static obstacle and dynamic obstacle. The research method has faster path planning efficiency and can generate safer robot running paths.