关键词: 移动机器人, 蚁群算法, 路径规划, 概率型算法, 最佳路径, 信息素挥发系数, 信息素更新标准, 参数空间

Abstract:

In view of the problem that it is difficult to reasonably plan the path after the mobile robot completes its self-positioning and map construction, which leads to the disordered movement of the mobile robot and the waste of resources, ant colony algorithm is adopted to realize the path planning of mobile robot in this study. Ant colony algorithm is a probabilistic algorithm to solve the optimal path in a problem. However, in the general ant colony algorithm, all parameters of the ant colony algorithm are unchanged, resulting in the result of the ant colony algorithm dependent on the pheromone parameters set in the algorithm. In order to solve the above problems, the parameters of ant colony algorithm and pheromone allocation are improved, and the pheromone update standard is improved by changing the pheromone volatility coefficient and pheromone update standard in each iteration and combining with heuristic factors. Setting the adjustable pheromone volatile factor increases the adaptability of the algorithm. According to the meaningful parameter space, the path planning results of the traditional ant colony algorithm and the improved ant colony algorithm are compared under different environments. The path length of the improved ant colony algorithm is reduced by 4.48% and 8.54%, respectively, and no path crossover nodes are generated, which achieves the expected effect of reasonable path planning for mobile robots.

Key words: mobile robot, ant colony algorithm, path planning, probabilistic algorithm, optimal path, pheromone volatilization coefficient, pheromone renewal standard, parameter space