Research On Robot SLAM Algorithm Based On Swarm Intelligence Optimized Particle Filter

Simultaneous localization and mapping(SLAM)is a crucial technology to achieve autonomous navigation function of mobile robots.The current application environment of mobile robots is relatively complex.In a complex environment with dense map features,the performance of the SLAM algorithm is greatly affected.Problems such as low estimation accuracy and decline of real-time performance will occur when SLAM algorithm is applied to a complex environment,which will seriously hinder practical applications.Therefore,this paper is focused on study of how to improve the localization and mapping accuracy of the SLAM algorithm,which lays a theoretical foundation for promoting the development of the mobile robot industry and really achieve the autonomous navigation function of mobile robots.SLAM refers to the process that the robot collects the surrounding environmental information through its own sensors in an unknown environment,and then uses the collected information to construct a map and localize itself.This paper discussed and built a motion model and observation model for SLAM system of a mobile robot,and detailed the mathematical model for problems of the SLAM,which laid the basis for the subsequent algorithm research.To deal with the problem of particle degradation and depletion in the particle filtering algorithm,which further influences the filtering accuracy,this paper drew lessons from swarm intelligence,and applied the improved firefly algorithm to the particle filter to obtain a firefly algorithm-based particle filter of adaptive closed-loop control.The algorithm revised the firefly position update formula,and incorporated parameter adaptive adjustment of optical absorption coefficient and step length factor of the firefly algorithm,which dynamically reached a balance between the algorithm's global optimization and local optimization capabilities,and made the particle distribution more properly and effectively solved the algorithm in this paper could effectively enhance the filtering accuracy and ensure the diversity of particles.In view of the problem of low accuracy of localization and mapping of the FastSLAM algorithm,this paper incorporated the particle filtering method optimized by firefly intelligent algorithm into the SLAM algorithm of mobile robot,which took the proportion of particles receiving new states as the feedback value,and combined with optimized feedback process,and conducted online closed-loop adjustment on SLAM algorithm to propose a firefly algorithm-based particle filter algorithm of adaptive closed-loop control.After conducting the simulation and comparison experiment on the proposed algorithm in the SLAM simulator and standard data set,the results showed that the algorithm proposed in this paper has higher estimation accuracy of position and landmark than the FastSLAM and firefly particle filtering SLAM algorithms.