This paper proposes two extensions of Particle Swarm Optimization (PSO) and Darwinian Particle Swarm Optimization (DPSO), respectively denoted as RPSO (Robotic PSO) and RDPSO (Robotic DPSO), so as to adapt these promising biologically inspired techniques to the multi-robot systems domain, by considering obstacle avoidance and communication constraints. The concepts of social exclusion and social inclusion are used in the RDPSO algorithm as a 'punish-reward' mechanism, thus enhancing the ability to escape from local optima. Experimental results obtained in a simulated environment shows the superiority of the RDPSO evidencing that sociobiological inspiration can be useful to meet the challenges of robotic applications that can be described as optimization problems (e.g. search and rescue). Moreover, the performance of the RDPSO is further evaluated within a population of up to 12 physical robots under communication constraints. Experimental results with real platforms show that only 4 robots are needed to accomplish the herein proposed mission and, independently on the number of robots and maximum communication distance, the global optimum is achieved in approximately 90 of the experiments.
展开▼
