A Fuzzy Adaptive Approach to Decoupled Visual Servoing for a Wheeled Mobile Robot

To address the performance bottleneck for image-based visual servoing (IBVS), it is necessary to have appropriate servoing control laws, increased accuracy for image feature detection, and minimal approximation errors. This article proposes a fuzzy adaptive method for decoupled IBVS that allows the efficient control of a wheeled mobile robot (WMR). To address the under-actuated dynamics of the WMR, a decoupled controller is used and translation and rotation are decoupled by using two independent servoing gains, instead of the single servoing gain that is used for traditional IBVS. To reduce the effect of image noise, this article develops an improved bagging method for the decoupled controller that calculates the inverse kinematics and does not use the Moore–Penrose pseudoinverse method. To improve convergence, improved Q-learning is used to adaptively adjust the mixture parameter for the image Jacobian matrix (IQ-IBVS). This allows the mixture parameter can be adjusted while the robot moves under the influence of servo control. A fuzzy method is used to tune the learning rate for the IQ-IBVS method, which ensures effective learning. The results of simulation and experiments show that the proposed method performs better than other methods, in terms of convergence.