Mapping and Autonomous Obstacle Avoidance of Mobile Robot Based on ROS Platform - Qian, Peng; Xu, Naijia; Fu, Cunlong; Deng, Shudong (Manufacturing Technology Journal, 2023)
With the progress of science and technology and the continuous development of robot technology, the performance and intelligence of robots are also constantly improving. It has been widely used in many fields such as life service, military, industrial production and so on. Among them, autonomous mobile is an important embodiment of intelligence. Therefore, it is necessary to solve the problem of robot real-time positioning and map building (SLAM). SLAM is the abbreviation of Simultaneous localization and mapping, which means "synchronous localization and mapping". It is mainly used to solve the problem of localization and mapping when robots move in unknown environments. This paper designs and implements a positioning and navigation system for mobile robots based on lidar in the environment of robot operating system (ROS). The system is based on the gamping algorithm of particle filter, so that robots can perform self-positioning and map building in strange environments. By studying the Rao-Blackwelized particle filter algorithm and enlarging the bandwidth of Kalman filter to increase its estimation accuracy, the filter was optimized. In the process of robot implementation of map construction and autonomous obstacle avoidance, the robot can conduct self-positioning and map building in unfamiliar environments by using the algorithm provided by the open source Gampping function package in the robot operating system (ROS).The navigation function package allows the robot to navigate independently and avoid obstacles with known maps of the environment. Finally, the simulation tool gazebo of the robot operating system (ROS) is used to build the simulation environment required for the experiment and simulate the real environment of the robot. Finally, the robot is equipped with lidar sensors to carry out experimental simulation, so that it can achieve the functions of self-positioning, map building, self-navigation and obstacle avoidance.
Keywords: Particle filter; Mobile robot; Mapping; Path planning