Autonomous vision-based docking of a mobile robot with four omnidirectional wheels

Abstract

Docking of mobile robots requires precise position measurements relative to the docking platform to accomplish the task successfully. Besides, the pose estimation of the robot with the sensors in an indoor environment should be accurate enough for localization and navigation toward the docking platform. However, the sensors are entitled to errors due to the measurement uncertainty. In this thesis, sensor data fusion is exploited to decrease the measurement errors and increase the accuracy of the docking. Different approaches are employed in this thesis for the sensor fusion to investigate the precise docking of the mobile robot. Laser scanners, vision sensors and reinforcement learning are evaluated to find the optimal approach for docking of the mobile robot. The final approach is a reinforcement learning framework to investigate and compare the optimality of the docking with the vision-based control method in which training is handled in the simulation environment with the reward distribution.