Gripper tracking with trajectory prediction and adaptive fuzzy control

This paper presents a new and robust algorithm to track a robot gripper during its movement in a teleoperation task. Based on acquired image and knowing the gripper model, the pose is obtained. This information is used to move a pan-tilt camera and keep the gripper centered in the image using an adaptive fuzzy logic controller. This control law is used combined with a position prediction technique (Extended Kalman Filter - EKF) Vision based systems have a lot of empirically adjustable parameters for a good working. With the algorithm proposed in this paper, the adjustable parameters are minimized, so the system robustness is increased.