Abstract
This paper presents an on-line estimation method which can find a mathematical expression of stiffness property of the objects grasped in vision-based 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.
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© 2016 Springer International Publishing Switzerland
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Lin, CY., Hung, WT., Hsieh, PJ. (2016). Stiffness Estimation in Vision-Based Robotic Grasping Systems. In: Kubota, N., Kiguchi, K., Liu, H., Obo, T. (eds) Intelligent Robotics and Applications. ICIRA 2016. Lecture Notes in Computer Science(), vol 9834. Springer, Cham. https://doi.org/10.1007/978-3-319-43506-0_24
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DOI: https://doi.org/10.1007/978-3-319-43506-0_24
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