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Grasped Object Weight Compensation in Reference to Impedance Controlled Robots

Author

Listed:
  • Tomasz Winiarski

    (Faculty of Electronics and Information Technology, Institute of Control and Computation Engineering, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland)

  • Szymon Jarocki

    (Faculty of Electronics and Information Technology, Institute of Control and Computation Engineering, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland)

  • Dawid Seredyński

    (Faculty of Electronics and Information Technology, Institute of Control and Computation Engineering, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland)

Abstract

This paper addresses the problem of grasped object weight compensation in the one-handed manipulation of impedance controlled robots. In an exemplary identification procedure, the weight of an object and its centre of mass together with gripper kinematic configuration are identified. The procedure is based on the measurements from a 6-axis force/torque sensor mounted near the gripper. The proposed method reduces trajectory tracking errors coming from the model imprecision without compromising the main advantages of impedance control. The whole approach is applied according to the embodied agent paradigm and verified on the two-arm service robot both in simulation and on hardware. Due to the general description that follows system engineering standards, the method can be easily modified or applied to similar systems.

Suggested Citation

  • Tomasz Winiarski & Szymon Jarocki & Dawid Seredyński, 2021. "Grasped Object Weight Compensation in Reference to Impedance Controlled Robots," Energies, MDPI, vol. 14(20), pages 1-15, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6693-:d:656913
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    References listed on IDEAS

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    1. Kun Yang & Yibin Li & Lelai Zhou & Xuewen Rong, 2019. "Energy Efficient Foot Trajectory of Trot Motion for Hydraulic Quadruped Robot," Energies, MDPI, vol. 12(13), pages 1-24, June.
    2. Phong B. Dao, 2021. "Learning Feedforward Control Using Multiagent Control Approach for Motion Control Systems," Energies, MDPI, vol. 14(2), pages 1-17, January.
    3. Savvas Piperidis & Iason Chrysomallis & Stavros Georgakopoulos & Nikolaos Ghionis & Lefteris Doitsidis & Nikos Tsourveloudis, 2021. "A ROS-Based Energy Management System for a Prototype Fuel Cell Hybrid Vehicle," Energies, MDPI, vol. 14(7), pages 1-19, April.
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