Mobile robot path planning using ant colony optimization

In this paper, the ant colony optimization (ACO) metaheuristic is proposed to solve the mobile robot path planning (MRPP) problem. In order to demonstrate the effectiveness of ACO in solving the MRPP problem, several maps of varying complexity used by an earlier researcher is used for evaluation. Each map consists of static obstacles and walls in different arrangements. Besides that, each map has a grid representation with an equal number of rows and columns. These maps have a starting point and a destination as well. At the beginning of the problem, the ants (representing the mobile robot) are placed at the starting point. The ants would then have to find their way towards the destination whilst avoiding all the obstacles and walls along the way. The ants should also do so with the shortest distance possible. The performance of the proposed ACO metaheuristic is tested on a given set of maps and the results are compared with those reported in the literature. The performance of the proposed ACO metaheuristic is found to be better than the result reported in the literature.