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.

An Overview of Nature-Inspired, Conventional, and Hybrid Methods of Autonomous Vehicle Path Planning

“…There are many other hybrid approaches used for path planning that did not include FL. Some of these include APF combined with SA [216], PSO combined with gravitational search (GS) algorithm [283], VFH algorithm combined with Kalman Filter [113], integrating game theory and geometry [284], combination of differential global positioning system (DGPS), APF, FL and A * path planning algorithm [41], A * search and discrete optimization methods [92], a combination of differential equation and SMC [243], virtual obstacle concept was combined with APF [106], and recently the use of LIDAR sensor accompanied with curve fitting. Few of such methods are discussed in this section.…”

An Exact Geometry–Based Algorithm for Path Planning

“…Particle swarm optimization (PSO) is a swift and simple randomized search algorithm applied to optimize numerous NP-hard problems, and by now, several versions of multi-objective PSO have been proposed (Coello et al, 2004;. One application of this algorithm is in the path planning problems (Zhang et al, 2013;Purcaru et al, 2013).…”

PSOGSA-Explore: A new hybrid metaheuristic approach for beampattern optimization in collaborative beamforming

“…Unlike [23], the work in [31] retains the local search scheme of PSO and introduces the GSA based acceleration as an additional element during the velocity update. This new version of algorithm is used to solve a dual objective problem to produce optimal collision free trajectory for mobile robots and provides faster convergence compared to the legacy PSO and GSA.…”

“…The hybridization of PSO and GSA in [31] is also categorized as low-level teamwork hybrid algorithm for the same reasons. The proposed algorithm retains the original local search of PSO and additionally appends the GSA's acceleration to the velocity update.…”