Research on Improved Localization and Navigation Algorithm for Automatic Guided Vehicle

Abstract

Aiming at the problem of inaccurate positioning caused by the wheel slippage or “kidnapping” movement of the robot in the positioning navigation, an improved autonomous positioning navigation strategy based on the robot operating system (ROS) is proposed. Firstly, combined with adaptive Monte Carlo localization (AMCL) algorithm and laser-based point-and-line iterative closest/corresponding point (PLICP) pose estimation algorithm, the accuracy and robustness of positioning are effectively improved. Then, based on the path planning strategy combining A* algorithm and dynamic window algorithm (DWA), an improved navigation failure recovery method is proposed and integrated into the ROS navigation framework, which can effectively improve the efficiency of robot positioning navigation and task execution. Finally, the mobile robot model TurtleBot is used as the experimental platform. The simulation experiment and field test demonstrate that the improved algorithm is superior to the original algorithm. The improved algorithm can adapt to the inaccuracy of the odometer and can achieve accurate localization and navigation in long-distance environment.