Path planning for unmanned wheeled robot based on improved ant colony optimization

Abstract

This research presents a simple and novel improved ant colony optimization for path planning of unmanned wheeled robot. Our main concern is to avoid the random deadlock situation and to reach at the destination using the shortest path, to decrease lost ants and improve the efficiency of solutions. The aforementioned reasons, we design an adaptive heuristic function by adopting the Euclidean distance between the ant and the target destination, in order to avoid the initial blindness and later singleness of ant path searching. The historical best path when appropriate to retain the previous effort would supersede the current worst path. Simulation results under random maps show that the improved ant colony optimization considerably increases the number of effective ants. During the searching process, the probability to find the optimal path increases, as well as the search speed. Moreover, we also compare the improved ant colony optimization performance with the simple ant colony optimization.