Autonomous robot path optimization using firefly algorithm

Path planning is an NP-complete problem with numerous practical applications, and is especially important for the navigation and control of autonomous robots. However, due to its computational complex nature, an optimal solution is often very difficult to be found using traditional methods. In this research, a swarm intelligence approach inspired by the biological behavior of glowworms is studied and applied to the robot path optimization problem. Computer simulation results show this firefly algorithm can successfully find the optimal path in a dynamic environment, and outperforms the ant colony algorithm (ACO) for a larger grid workspace in terms of both path length and computational cost.