Fuzzy adaptive extended Kalman filter SLAM algorithm based on the improved PSO algorithm

Abstract

In order to solve the problem that the mobile robot simultaneous localization and mapping (SLAM) is difficult to establish an accurate priori noise model, an improved particle swarm tuning fuzzy supervised adaptive extended Kalman filter localization method is proposed. In the algorithm, the fuzzy system adaptively adjusts the observed noise covariance R in the extended Kalman filter algorithm to minimize the discrepancy between the theoretical and actual covariance of the innovation. The improved particle swarm algorithm is used to train the parameters of a fuzzy system. The improved particle swarm optimization algorithm introduces the population iteration success rate as the feedback parameter to adjust the global and local search ability of particle swarm optimization, and uses the chaotic interference factor to improve the particle swarm algorithm to prone to premature defects. The simulation and experimental results show the performance of the proposed improving algorithm comparing with the fuzzy supervisors for the adaptive extended Kalman filter localization algorithm, where the free parameters of the fuzzy systems are tuned using basic particle swarm optimization algorithm.