FPGA-Based Hybrid GA-PSO Algorithm and Its Application to Global Path Planning for Mobile Robots

Abstract

This paper presents an FPGA-based (field-programmable gate array) hybrid metaheuristic GA (genetic algorithm)-PSO (particle swarm optimization) algorithm for mobile robots 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. Experimental results are conducted to show the merit of the proposed hybrid GA-PSO path planner for global path planning for mobile robots.