Multi-objective evolutionary approach to prevent premature convergence in Monte Carlo localization

In this paper, we propose a global localization algorithm for mobile robots based on Monte Carlo localization (MCL), which employs multi-objective particle swarm optimization (MOPSO) incorporating a novel archiving strategy, to deal with the premature convergence problem in global localization in highly symmetrical environments. Under three proposed rules, premature convergence occurring during the localization can be easily detected so that the proposed MOPSO is introduced to obtain a uniformly distributed Pareto front based on two objective functions respectively representing weights and distribution of particles in MCL. On the basis of the derived Pareto front, MCL is able to resample particles with balanced weights as well as diverse distribution of the population. As a consequence, the proposed approach provides better diversity for particles to explore the environment, while simultaneously maintaining good convergence to achieve a successful global localization. Simulations have confirmed that the proposed approach can significantly improve global localization performance in terms of success rate and computational time in highly symmetrical environments.