Unknown environment exploration of multi-robot system with the FORDPSO

Effective environment exploration in unknown environment is precondition of constructing the environment map and carrying out other tasks for multi-robot system. Due to its excellent performance, particle swarm optimization (PSO) has been widely used in multi-robot exploration field. To deal with its drawback – easily trapped in local optima, Darwinian PSO (DPSO) optimization is proposed by Tillett et al. [1] with the natural selection function and first used in real world robot exploration by Couceiro et al. [2], forming the robotic DPSO (RDPSO). To increase the algorithm performance and control its convergence rate, fractional calculus is used to replace inertia component in RDPSO for its “memory” ability and forming the fractional order RDPSO (FORDPSO). This paper presents a formal analysis of RDPSO and studies the influence of the coefficients on FORDPSO algorithm. To satisfy the requirement of dynamically changing robots׳ behaviors during the exploration, fuzzy inferring system is designed to achieve better control coefficients. Experiment results obtained in two complex simulated environments illustrate that biological and sociological inspiration is effective to meet the challenges of multi-robot system application in unknown environment exploration, and the exploration effect of the fuzzy adaptive FORDPSO is better than that of the fixed coefficient FORDPSO. Furthermore, the performance of FORDPSO with different neighborhood topologies are studied and compared with other six PSO variations. All the results demonstrate the effect of the FORDPSO on the multi-robot environment exploration.