关键词: 矿山巡检机器人　, A-star　, 遗传算法　, 避障　, 路径规划　, 任务分配

Abstract: In recent years,mine inspection robots have gradually replaced humans to perform inspection tasks in danger ous and harsh mining environments.However,the existing inspection robots have problems such as low efficiency and long paths in path planning and task allocation.It is necessary to develop more efficient methods for path planning and task alloca tion.By combining Genetic Algorithm (GA) with A-star algorithm,a new path planning method for mine inspection robots was proposed.By integrating the search strategies of four-neighborhood and eight-neighborhood in the A-star algorithm;introducing weights into the evaluation function to optimize the search process;and improving the crossover and mutation processes of the genetic algorithm by introducing heuristic crossover and cloning mutation methods,the time and length of the robot′s task exe cution were reduced.The experimental results show:① The path planning scheme using the improved A-star algorithm has fe wer path bends,does not collide with obstacles,and has a shorter path length.The path planning time and length of this algo rithm are only 7.2 s and 29.8 m respectively.② When the inspection task is 18,the average moving distance of the improved genetic algorithm is only 109.4 m.The research reflects that the proposed improved path planning and task allocation method effectively optimizes the path planning quality and task execution efficiency of mine inspection robots in complex mining envi ronments,and has certain practical application value.

Key words: mine inspection robot,A-star,genetic algorithm,obstacle avoidance,path planning,task allocation