Enhanced ant colony algorithm with obstacle avoidance strategy for multi-objective path planning of mobile robots

Abstract

Path planning with multiple evaluation metrics makes the motion of a robot realistic, but the contradiction between the metrics and the lack of global search and obstacle avoidance capabilities increases the difficulty of obtaining the optimization solution. To solve these problems, an enhanced ant colony algorithm (EACA) with an obstacle avoidance strategy is proposed in this article. First, the path planning model is constructed, and strict movement rules are designed. Secondly, the EACA with global search, balancing the contradiction between metrics, is designed. The dynamic regulation of pheromone concentration and the mechanism of fluctuating pheromone distribution are explored, heuristic information is optimized and the path planning effect is enhanced. Finally, a new mechanism of away-from-obstacles is proposed as the obstacle avoidance strategy, which ensures a reasonable safe distance. Comparative simulations on several different maps validate the performance of EACA with the obstacle avoidance strategy for planning robot movement paths.