A learning approach to image-based visual servoing with a bagging method of velocity calculations

Visual servoing allows accurate control of positioning and motion relative to a stationary or moving target using vision and is the subject of many studies. Most servoing gains for image-based visual servoing methods are selected heuristically or empirically so accuracy is affected. This study uses reinforcement learning to adaptively tune the proportional servoing gain over sequences of image based visual servoing, instead of using a constant gain. The system is used to control hovering and tracking for a stationary or slowly moving target. An image Jacobian matrix with four dimensions is constructed because a quad-rotor drone features under-actuated dynamics. The Moore-Penrose pseudo inverse method is usually used to calculate the inverse image Jacobian matrix, but this study uses a bagging approach to calculate the inverse kinematics. The desired velocity is obtained from time-varying image errors, which gives greater robustness. An adaptive method to calculate the servoing gain method is proposed, whereby the selection of an appropriate servo gain over time is regarded as a reinforcement learning problem. The proposed visual servoing control system is implemented and tested experimentally using a quad-rotor drone system. The experimental results demonstrate that the proposed method is more robust and converges faster than other methods.