Stiffness Estimation in Vision-Based Robotic Grasping Systems

This paper presents an on-line estimation method which can find a mathematical expression of stiffness property of the objects grasped in visionbased robotic systems. A robot manipulator in conjunction with visual servo control is applied to autonomously grasp the object. To increase the accuracy of the object compression values associated with the used low-cost hardware, an extended Kalman filter is adopted to fuse the sensing data obtained from webcam and gripper encoder. The grasping forces are measured by a piezoresistive pressure sensor installed on the jaw of the manipulator. The force and position data are used to represent the stiffness property of the grasped objects. An on-line least square algorithm is applied to fit a stiffness equation with time-varying parameters. The experimental results verify the feasibility of the proposed method.