In the present world, mobile robot has been widely used for many functions across different areas of life. These mobile robots can be engaged in a static or dynamic environment where they are expected to accomplish a task optimally against all odds. Path planning for mobile robot is a very crucial problem in robotics that has been greatly researched upon; it is aimed at finding an optimal path in a given environment from a start point to the goal point. Several techniques have been employed in solving this crucial problem. These techniques are broadly classified as classical and heuristics. The Swarm Intelligence Techniques form a sub-class of the heuristics approach. The aim of this research is to review the swarm intelligence techniques in solving the mobile robot path planning problem. The drawbacks and merits of each of the techniques were discussed and a comparative analysis was given.

This article presents the implementation and comparison of fruit fly optimization (FOA), ant colony optimization (ACO) and particle swarm optimization (PSO) algorithms in solving the mobile robot path planning problem. FOA is one of the newest nature-inspired algorithms while PSO and ACO has been in existence for a long time. PSO has been shown by other studies to have long search time while ACO have fast convergence speed. Therefore there is need to benchmark FOA performance with these older nature-inspired algorithms. The objective is to find an optimal path in an obstacle free static environment from a start point to the goal point using the aforementioned techniques. The performance of these algorithms was measured using three criteria: average path length, average computational time and average convergence speed. The results show that the fruit fly algorithm produced shorter path length (19.5128 m) with faster convergence speed (3149.217 m/secs) than the older swarm intelligence algorithms. The computational time of the algorithms were in close range, with ant colony optimization having the minimum (0.000576 secs). Keywords:  Swarm intelligence, Fruit Fly algorithm, Ant Colony Optimization, Particle Swarm Optimization, optimal path, mobile robot.