Adaptive Neural Networks for Image-Based Visual Servoing with Uncertain Parameters

Visual servoing of robot manipulators can be viewed as an optimization problem and recurrent neural network is widely accepted as a powerful tool for solving optimization problems. Inspired by this, an adaptive neural network method is proposed for image-based visual servoing (IBVS) with uncertain parameters. It is the first work focused on IBVS simultaneously considering the uncertain camera configuration parameters and uncertain kinematics of robot manipulators in the framework of recurrent neural networks. Theoretical analysis including convergence and stability of the proposed method is presented. In order to verify the effectiveness of the theoretical results and the portability of the proposed method, simulations are conducted on different robot manipulators for different tracking tasks with excellent performance. In addition, comparisons with control schemes employing traditional gradient neural network (GNN), Kalman filter (KF) and model-based recurrent neural network highlight the great advantages of the proposed control system.