Docking Control Algorithm for Autonomous Mobile Robot Through Sensor Fusion

Abstract

We developed a methodology to achieve position estimation, path planning and control for autonomous docking systems of autonomous mobile robots (AMRs). For autonomous docking, the relative position between the AMR and the docking station must be accurately estimated. The relative position determined using a camera and lidar sensors is inaccurate, and the position update rate is insufficient. To solve this problem, we propose a Kalman filter that uses an inertial measurement unit and information from a wheel encoder sensor in combination. The position estimated by the Kalman filter has a smaller root mean square error and variance than those obtained from the camera and lidar sensors, and the position is updated every 25 ms. The control system for path planning and docking was implemented in the Robot Operating System, and the algorithm was verified through Gazebo simulation. Finally, the developed algorithm was verified in real environments. The experimental results yielded a position error of less than 1 cm and an angle error of less than 1°.