Target searching in unknown environment of multi-robot system using a hybrid particle swarm optimization

Abstract

Target searching in unknown environment using multi-robot search systems has received increasing attention in recent years. Particle Swarm Optimization (PSO) has applied successfully on multi-robot target searching system. However, this algorithm suffer from premature convergence problem and cannot escape from the local optima. It is, therefore, important to have an efficient method to escape from the local optima and create and efficient balance between exploitation and exploration. In this study, we propose a new method based on PSO algorithm (ATREL-PSO) to find the target in unknown environment using multi-robot system within a limited time. This novel algorithm is demonstrated to escape from the local optima and create an efficient balance between exploration and exploitation to reach the target faster. The concept of attraction, repulsion and the combination of repulsion and attraction enhancing the search exploration, and when the robot get closer to the target it should forget the PSO concept and apply the local search method to reach the target faster. Experimental results obtained in a simulated environment show that biological and sociological inspiration could be useful to meet the challenges of robotic applications that can be described as optimization problems.