A new localization method for mobile robots using Genetic Simulated Annealing Monte Carlo Localization

A new localization method Genetic Simulated Annealing Monte Carlo Localization (GSAMCL) is presented for mobile robots in this paper. By using the observation matching as the fitness function to make the particles adjust to the high probability area meanwhile utilizing the high optimization performance of Genetic Simulated Annealing Algorithm, GSAMCL alleviates particle recession and improves the convergence efficiency compared with Monte Carlo Localization (MCL). Implementation of a system for multiple mobile robots localization using GSAMCL is gained based on the establishment of motion model and RSSI-based awareness model of mobile robots. Through analyzing of simulation results of the mobile robots system above, it shows that, using GSAMCL, mobile robots need fewer particles and less time to achieve higher localization efficiency and obtain higher localization accuracy under the same condition in global localization compared with MCL.

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