Hybrid PSO-GSA robot path planning algorithm in static environments with danger zones

Abstract

This paper proposes an optimal path planning algorithm for mobile robots based on a hybridization between a Gravitational Search Algorithm (GSA) and a Particle Swarm Optimization (PSO) algorithm and referred to as hybrid PSO-GSA. The multi-objective optimization is considered as the PSO-GSA uses two objective functions to generate optimal trajectories for mobile robots in static environments while avoiding collisions with the obstacles and danger zones that might exist in the environment. The hybrid PSO-GSA solves the optimization problems by minimizing the objective functions, producing optimal collision-free trajectories in terms of minimizing the length of the path that needs to be followed by the robot and also assuring that the generated trajectories are at a safe distance from the danger zones. The proposed hybrid PSO-GSA path planning algorithm is validated by running several experiments with robots in different environments in the presence of multiple obstacles and multiple danger zones.