Research on Path Planning Model of Preschool Children's Robot Based on RBPF-SLAM Algorithm
In the field of preschool education,the application of robotics technology has become a remarkable research direction.In order to enhance the path optimization ability of preschool children's robots,this study uses the simultaneous localization and map-ping algorithm based on particle filtering to combine radar observation models with motion models,and using annealing parameters and resampling to avoid a decrease in sampling efficiency,a robot motion model and map are constructed.Finally,a hybrid path planning model was constructed using the improved A-Star algorithm and the time elastic band algorithm.The experimental results show that the average error of the improved particle filter based SLAM algorithm is 0.340.The improved SLAM algorithm based on particle filte-ring has run times of 0.215 s,0.225 s,and 0.268 s with particle numbers of 50,125,and 200,and estimation errors of 0.314,0.282,and 0.291,respectively.The overall accuracy of the map generated by the SLAM algorithm based on particle filtering is sig-nificantly higher than the improved algorithm,and the lines on the map are neat and clear.In both simulated and real experimental environments,the improved A*algorithm converged after 18 and 50 iterations,respectively,with a minimum path length of 12 m and 32 m.The results verified that the hybrid path planning model performs well in path optimization in different environments.This method achieves precise robot positioning and improves the accuracy and reliability of path planning.