Abstract: The path planning of the mining area inspection robot faces challenges such as complex environments,dynamic obstacles and uncertain factors. Therefore,a path planning idea integrating the improved Rapidly-exploring Random Tree algorithm (RRT) and the Artificial Potential Field (APF) method is proposed. This method is based on the RRT algorithm and introduces the Random Walk (SW) algorithm for optimization of the search strategy. On this basis,to further improve the adaptability and obstacle avoidance ability in dynamic obstacle environments,the calculation method of the gravitational and repulsive force parameters in the APF algorithm is optimized and improved. Finally,a path planning model for the mining area inspection robot integrating SW-RRT and IAPF (SW-RRT-IAPF model) is proposed. The experimental results show that the path planning success rate of the SW-RRT-IAPF model is the highest at 97. 13%,the path smoothness is the highest at 93. 61%,and the average path planning time is the shortest at 0. 66 seconds. Compared with traditional methods,the path planning time in complex mining environments is reduced by approximately 35%,and the path smoothness is improved by 22%. The proposed model can optimize the path planning efficiency in both daytime and nighttime environments,effectively avoid dynamic obstacles,and provide a new solution for the path planning of mining area inspection robots. 

Key words: minspection robot in mining area,path planning,obstacle avoidance,RRT,APF