Multi-point Navigation Method for Intelligent Inspection Robots

Abstract

A multi-point navigation method is introduced for intelligent inspection robots, aimed at enhancing efficiency and safety across various industries. The newly proposed WW algorithm improves the robots' fault tolerance and positioning accuracy. The study utilized tools such as the ROS operating system, Rviz, and Gazebo, as well as the Agilex Bunker MINI intelligent trolley and SCOUT MINI sensor platform. It discussed SLAM map construction with Cartographer and Gmapping algorithms and the AMCL positioning system. Additionally, the DWA algorithm for dynamic obstacle avoidance was introduced. The system design includes a Qt interface and Rviz interface for multi-point navigation and obstacle avoidance. The effectiveness of the WW algorithm was verified through simulations and experiments, which enhances navigation stability by setting a maximum standby time and alternative point strategy. The experimental results show that the WW algorithm can prevent the robot from entering a false dead state. The paper concludes with suggestions for further optimization of the algorithm and the integration of more complex intelligent functions using deep learning.