An Enhanced Ant Colony Optimization for Path Planning of a Mobile Robot

Abstract

ABSTRACT Path planning for a mobile robot is a hot research topic in the field of robotics. To solve the path‐planning problem quickly and efficiently, an enhanced ant colony optimization algorithm is proposed in this paper. First, to achieve multi‐objective optimization of the path, heuristic information based on path length and the number of turns is designed. Second, an improved pheromone update rule is established that combines the best, mean, and worst solutions found during the search process. This approach addresses the shortcomings of the traditional ant colony optimization algorithm in path planning, such as slow convergence and the tendency to fall into local optima. Experimental results show that the proposed enhanced ant colony optimization has faster convergence and better performance in path planning for mobile robot. Compared with ant colony optimization, improved ant colony optimization multiple strategy, improved adaptive ant colony optimization, grey wolf optimization, Dijkstra, A*, and multi‐search strategy A*, the enhanced ant colony optimization algorithm yields respective enhancements of 14.2%, 9.2%, 21.2%, 20.6%, 14.9%, 8.0%, and 2.7% in performance.