关键词: 矿山巡检机器人, 遗传算法, 变步长蚁群算法, 路径规划

Abstract: As an important industrial production base,mine plays an important role in ensuring production safety and improving production efficiency. Traditional mine inspection mainly relies on manual inspection,which has some shortcomings such as high labor cost,low inspection efficiency and safety risk. Therefore,it is of great significance to study path planning algorithm of autonomous inspection robot for mine inspection to improve the technical level of mine inspection. In this paper,a road stiffness planning method of mine inspection robot is proposed,which combines optimal genetic algorithm and variable step size ant colony algorithm. Firstly,genetic algorithm is used to optimize the robot path,and then the optimized path is adopted as the initial path for ant search. In the process of ant search,the step size of ants is adjusted according to the change of current path length to adapt to the search of different path lengths. At the same time,the heuristic information and pheromone updating strategy are introduced to enhance the local and global search ability of ant search. The study results show that the proposed method can find the optimal solution faster,and has better robustness and stability than a single algorithm. By combining the advantages of variable step size ant colony algorithm and genetic algorithm,this method can quickly converge to the global optimal solution,and at the same time improve the search accuracy and convergence speed,which has certain significance for improving the path rule efficiency of mine inspection robots and ensuring the safe and efficient production of mines.

Key words: mine inspection robot,genetic algorithm,variable step size ant colony algorithm,path planning