A Comparative Analysis of Ant Colony Optimization for Mobile Robot Route Optimization

This paper adapts the five classical ACOs, namely ant system (AS), max-min AS (MMAS), elite AS (EAS), rank-based AS (RAS), and ant colony system (ACS), to solve mobile robot path planning problems. Specifically, this paper leverages a directional position selection strategy to adapt the path construction. Then, instead of depositing pheromone on edges, this paper maintains a pheromone matrix with the same size of the environment and deposits pheromone on grids. By considering the path length first and the number of turns second, this paper evaluates the path constructed by each ant comprehensively. With the above adaption, the five ACOs are expected to address the mobile robot path planning problem effectively. Extensive experiments have been conducted on totally 10 environments with different scales and different degrees of obstacles. The experimental results have proved that as compared with two traditional algorithms, namely A* and Dijkstra, the five adapted ACOs and the two traditional algorithms all find the shortest paths with respect to the path length. However, the shortest paths found by the five ACOs have much fewer turns than those found by the two traditional algorithms. Particularly, among the five adapted ACOs, ACS performs the best concerning the path length and the number of turns.