Hybrid algorithm based mobile robot localization using DE and PSO

• Published in: Proceedings of the 32nd Chinese Control Conference pp. 5955 - 5959
• Main Authors: Huo Junfei, Ma Liling, Yu Yuanlong, Wang Junzheng
• Format: Conference Proceeding
• Language: English
• Published: TCCT, CAA 01.07.2013
• Subjects: Convergence; differential evolution; localization; mobile robot; Mobile robots; Optimization; Particle filters; Particle swarm optimization; Sociology; Statistics

To take advantage of different algorithms and overcome their limitations, a new hybrid algorithm (DEPSO) based on Differential Evolution (DE) and Particle Swarm Optimization (PSO) is proposed in this paper for mobile robot localization. In the first step of DEPSO, the mutation and selection operators of DE are employed to produce a new population for effective variation. Next, PSO is carried out for local exploration with high efficiency, followed by crossover and selection operations. During iteration of the DEPSO progress, the extent of searching region for the population is increased and decreased in sequence, and eventually resulted in convergence to an optimal solution. This method has advantages of fast convergence, strong searching ability and good robustness. Compared with the DE and PSO, DEPSO inhibits the particle degeneracy and enhances the diversity, meanwhile improves the convergence speed and positioning accuracy. The simulation and experiment results prove its effectiveness and feasibility.