Full-DOF Calibration-Free Robotic Hand-Eye Coordination Based on Fuzzy Neural Network

This paper studies coordination control for an uncalibrated single eye-in-hand robotic system to track an object in 3-D space with simultaneous translations and rotations. A set of object features is properly defined to derive an invertible nonlinear visual mapping model from visual feedback to robot control. A novel fuzzy neural network is proposed to realize the nonlinear mapping model effectively, which is essential to implement six-degree-of-freedom control of robot hand. Simulation results show the performance of the proposed method.