Robot hand tracking using adaptive fuzzy control

This paper presents a visual tracking system developed for a teleoperation project. The goal of this project is a both hands master-slave system in which the operator needs to see the robot hand (gripper) every time. For this reason, a new and robust algorithm to track the robot hand during its movement is developed. This is a model based tracking so the knowledge of the robot hand CAD model is needed (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. Due to the continuous gripper movement, we need a position predictor to reduce the error. In our case, the extended Kalman filter - EKF is used to do it. Vision based systems have a lot of empirically adjustable parameters for a good working. With the algorithm proposed in this paper, some parameters are auto-adjustable, so the system is easier to use and the robustness is increased.