Research on the Application of improved AMCL Algorithm in Robot Obstacle Avoidance in Logistics Sorting Scene

Abstract

Compared with the normal scenes, the positions of obstacles, delivery boxes and sorting stations in the logistics sorting scene are relatively fixed, what’s more, the number of static obstacles in the logistics sorting scene is large. It requires higher navigation accuracy and pose adjustment of the robot. The author proposes an Adaptive Monte Carlo Localization (AMCL) algorithm that integrates the Dynamic Window Approach (DWA) algorithm to improve the accuracy and efficiency of robots in real-time positioning, navigation in static indoor environments, and obstacle avoidance efficiency. Using this method, the data collection of lidar sensor are optimized, reducing positioning calculation. By adjusting the parameters’ value, the adaptive positioning accuracy and real-time positioning rate of the logistics robot are improved. The experimental results show that the Adaptive Monte Carlo Localization integrated with Dynamic Window Approach algorithm is about 13.682 % higher than the normal Adaptive Monte Carlo Localization algorithm in the obstacle avoidance rate, which effectively makes the number of particles collected by lidar more standardized and rational.