Adaptive Image-Based Visual Servoing Using Reinforcement Learning With Fuzzy State Coding

Image-based visual servoing (IBVS) allows precise control of positioning and motion for relatively stationary targets using visual feedback. For IBVS, a mixture parameter
$\beta$
allows better approximation of the image Jacobian matrix, which has a significant effect on the performance of IBVS. However, the setting for the mixture parameter depends on the camera's real-time posture; there is no clear way to define the change rules for most IBVS applications. Using simple model-free reinforcement learning, Q-learning, this article proposes a method to adaptively adjust the image Jacobian matrix for IBVS. If the state-space is discretized, traditional Q-learning encounters problems with the resolution that can cause sudden changes in the action, so the visual servoing system performs poorly. Besides, a robot in a real-world environment also cannot learn on as large a scale as virtual agents, so the efficiency with which agents learn must be increased. This article proposes a method that uses fuzzy state coding to accelerate learning during the training phase and to produce a smooth output in the application phase of the learning experience. A method that compensates for delay also allows more accurate extraction of features in a real environment. The results for simulation and experiment demonstrate that the proposed method performs better than other methods, in terms of learning speed, movement trajectory, and convergence time.