SLAM of Mobile Robot for Wireless Communication Based on Improved Particle Filter

Abstract

The mobile robot is moved by receiving instructions through wireless communication, and the particle filter is used to simultaneous localization and mapping. Aiming at the problem of the degradation of particle filter weights and loss of particle diversity, which leads to the decrease of filter accuracy, this paper uses the plant cell swarm algorithm to optimize the particle filter. First of all, combining the characteristics of plant cells that affect the growth rate of cells when the auxin content changes due to light stimulation realizes the optimization of the particles after importance sampling, so that they are concentrated in the high-likelihood area, and the problem of particle weight degradation is solved. Secondly, in the process of optimizing particle distribution, the auxin content of each particle is different, which makes the optimization effect on each particle different, so it effectively solves the problem of particle diversity loss. Finally, a simulation experiment is carried out. During the experiment, the robot moves by receiving control commands through wireless communication. The experimental results show that the algorithm effectively solves the problem of particle weight degradation and particle diversity loss and improves the filtering accuracy. The improved algorithm is verified in the simultaneous localization and mapping of the robot, which effectively improves the robot’s performance at the same time positioning accuracy. Compared with the classic algorithm, the robot positioning accuracy is increased by 49.2%. Moreover, the operational stability of the algorithm has also been improved after the improvement.