Asynchronous Particle Swarm Optimization-Based Search With a Multi-Robot System: Simulation and Implementation on a Real Robotic System

Abstract

In this article we consider a version of the Particle Swarm Optimization (PSO) algorithm, which is appropriate for search tasks of multi-agent systems consisting of small robots with limited sensing capability. The proposed method adopts asynchronous mechanism for information exchange and position (way point) updates of the agents. Moreover, at each (information exchange) step the agents communicate with only a possibly different subset of the other agents leading to a dynamic neighborhood topology. We implement the algorithm using the Player/Stage realistic robot simulator as well as on real KheperaIII robots using experimentally collected realistic data of ethanol gas concentration. Simulation and implementation results show that the algorithm performs well in a sense that the robots are able to move towards and aggregate in areas with high gas concentration around the maximum points of the gas concentration profile representing the environment.