Aiming at the problems of low mapping accuracy, slow path planning efficiency, and high radar frequency requirements in the process of mobile robot mapping and navigation in an indoor environment, this paper proposes a four-wheel drive adaptive robot positioning and navigation system based on ROS. By comparing and analyzing the mapping effects of various 2D-SLAM algorithms (Gmapping, Karto SLAM, and Hector SLAM), the Karto SLAM algorithm is used for map building. By comparing the Dijkstra algorithm with the A* algorithm, the A* algorithm is used for heuristic searches, which improves the efficiency of path planning. The DWA algorithm is used for local path planning, and real-time path planning is carried out by combining sensor data, which have a good obstacle avoidance performance. The mathematical model of four-wheel adaptive robot sliding steering was established, and the URDF model of the mobile robot was established under a ROS system. The map environment was built in Gazebo, and the simulation experiment was carried out by integrating lidar and odometer data, so as to realize the functions of mobile robot scanning mapping and autonomous obstacle avoidance navigation. The communication between the ROS system and STM32 is realized, the packaging of the ROS chassis node is completed, and the ROS chassis node has the function of receiving speed commands and feeding back odometer data and TF transformation, and the slip rate of the four-wheel robot in situ steering is successfully measured, making the chassis pose more accurate. Simulation tests and experimental verification show that the system has a high precision in environment map building and can achieve accurate navigation tasks.
Keywords: A* algorithm; ROS; SLAM; autonomous navigation.