Global path planning for autonomous robot navigation using hybrid metaheuristic GA-PSO algorithm

Abstract

This paper presents a hybrid metaheuristic GA (genetic algorithm)-PSO (particle swarm optimization) algorithm for autonomous robot navigation to find an optimal path between a starting and ending point in a grid environment. GA has been combined with PSO in evolving new solutions by applying crossover and mutation operators on solutions constructed by particles. This hybrid algorithm avoids the premature convergence and time complexity in conventional GA and PSO algorithms. The initial feasible path generated from the hybrid GA-PSO planner is then smoothed using the cubic B-spline technique, in order to construct a near-optimal collision-free continuous path. Simulation results are conducted to show the merit of the proposed hybrid GA-PSO path planner and smoother for global path planning of autonomous robot navigation.