A PSO-based multi-robot cooperation method for target searching in unknown environments

In this paper, we study the problem of multi-robot target searching in unknown environments. For target searching, robots need an efficient method with respect to their limitations and characteristics of the workspace. Every robotic search algorithm has several constraints. Our goal is to propose a distributed algorithm based on Particle Swarm Optimization (PSO) for target searching which satisfies the before-mentioned constraints. This extension of PSO is named A-RPSO (Adaptive Robotic PSO). A-RPSO acts as the controlling mechanism for robots. It is similar to PSO with two modifications: firstly it takes into account obstacle avoidance, secondly A-RPSO has a mechanism to escape from local optima. Various experimental results obtained in a simulated environment, show that A-RPSO is able to outperform other state of-the-art techniques in target searching problems. The performance of A-RPSO is much more significant compared with other approaches in two distinctive states particularly: large environments and small number of robots.