Optimization of the Grid Mapping Algorithm for Mobile Robots

Abstract

Navigation precision, which is an important performance index of mobile robots, is closely related to the precision of grid mapping (Gmapping) algorithm. The modeling of the Gmapping algorithm is completed on the basis of Rao-Blackwellized particle filter (RBPF), with a light detection and ranging (LiDAR) robot as the research background, and the factors that influence the accuracy error of the Gmapping algorithm are analyzed. Firefly algorithm is introduced into the Gmapping system to complete the modeling and simulation analysis. The simulation results show that applying the firefly algorithm to the Gmapping system can effectively smooth the fluctuations of position, attitude angle, and other data output using sensors, which can decrease the number of particles and reduce object feature degradation. This method can effectively improve the accuracy of the Gmapping algorithm, reduce navigation error, and provide reference and guidance for the Gmapping system design of mobile robots.