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Upper Extremity Motion-Based Telemanipulation with Component-Wise Rescaling of Spatial Twist and Parameter-Invariant Skeletal Kinematics

Author

Listed:
  • Donghyeon Noh

    (Mechanical Engineering Department, Soongsil University, Seoul 06978, Republic of Korea)

  • Haegyeom Choi

    (Mechanical Engineering Department, Soongsil University, Seoul 06978, Republic of Korea)

  • Haneul Jeon

    (Mechanical Engineering Department, Soongsil University, Seoul 06978, Republic of Korea)

  • Taeho Kim

    (Mechanical Engineering Department, Soongsil University, Seoul 06978, Republic of Korea)

  • Donghun Lee

    (Mechanical Engineering Department, Soongsil University, Seoul 06978, Republic of Korea)

Abstract

This study introduces a framework to improve upper extremity motion-based telemanipulation by component-wise rescaling (CWR) of spatial twist. This method allows for separate adjustments of linear and angular scaling parameters, significantly improving precision and dexterity even when the operator’s heading direction changes. By finely controlling both the linear and angular velocities independently, the CWR method enables more accurate telemanipulation in tasks requiring diverse speed and accuracy based on personal preferences or task-specific demands. The study conducted experiments confirming that operators could precisely control the robot gripper with a steady, controlled motion even in confined spaces, irrespective of changes in the subject’s body-heading direction. The performance evaluation of the proposed motion-scaling-based telemanipulation leveraged Optitrack’s motion-capture system, comparing the trajectories of the operator’s hand and the manipulator’s end effector (EEF). This verification process solidified the efficacy of the developed framework in enhancing telemanipulation performance.

Suggested Citation

  • Donghyeon Noh & Haegyeom Choi & Haneul Jeon & Taeho Kim & Donghun Lee, 2024. "Upper Extremity Motion-Based Telemanipulation with Component-Wise Rescaling of Spatial Twist and Parameter-Invariant Skeletal Kinematics," Mathematics, MDPI, vol. 12(2), pages 1-18, January.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:2:p:358-:d:1324265
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    References listed on IDEAS

    as
    1. Haegyeom Choi & Haneul Jeon & Donghyeon Noh & Taeho Kim & Donghun Lee, 2023. "Hand-Guiding Gesture-Based Telemanipulation with the Gesture Mode Classification and State Estimation Using Wearable IMU Sensors," Mathematics, MDPI, vol. 11(16), pages 1-16, August.
    2. Kumar, Naveen & Lee, Seul Chan, 2022. "Human-machine interface in smart factory: A systematic literature review," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    Full references (including those not matched with items on IDEAS)

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