Localization of autonomous robot based on differential evolution algorithm

The Localization based on particle filter is one of the important methods. However, this approach has high computational cost, inconsistency and degeneracy problem. In this paper, to solve these problems, the localization problem is converted to an optimization problem. Then the differential evolution algorithm is used to solve this problem. The proposed algorithm searches stochastically along the state space of robot and obtain the best estimate position based of odometery, laser range finder and map of environment. One advantage of the proposed method is that it requires no proposal distribution and resampling step like localization based EKF and PF. Therefore, the risk of degeneracy decreases. The performance of localization on differential evolution (DE) is compared with localization based on extended Kalman filter (EKF) and particle filter. Simulation result shows that performance of localization based on differential evolution filters is better than others algorithms of localization.