Metaheuristic optimization approach to mobile robot path planning

Abstract

This paper presents a metaheuristic optimization-based approach to mobile robot path planning problem. A comparative study between trajectory-based metaheuristic optimization and population-based metaheuristic optimization is conducted. Breadth-first deterministic search is used to find the optimal solution (ground truth) that is compared to the paths generated by tabu search, simulated annealing and genetic algorithm. The experimental study shows that simulated annealing outperforms the other algorithms in terms of computational time while tabu search gives the shortest path.