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Human–Robot Collaborative Lifting Motion Prediction and Experimental Validation

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Abstract

This paper presents a human–robot collaborative symmetric lifting motion prediction using inverse dynamics optimization. The human and robot arm are modeled in Denavit-Hartenberg (DH) representation. A floating-base rigid body with 6 global degrees of freedom (DOFs) is similarly modeled as a three-dimensional (3D) table. A set of grasping forces characterizes the human-table and robot-table interactions. The joint torque squares of human arm and robot arm are minimized and subjected to physical and task related constraints. During lifting, the design variables include the cubic B-spline control points of joint angle profiles of the human arm, robot arm, and table. In addition, the discretized grasping forces are also treated as design variables. Both numeric and experimental human–robot lifting was performed with a 2 kg table. The simulation reports the human and robot arm’s joint angle profiles, joint torque profiles, and grasping force profiles. These profiles were validated with experimental data, which was collected using a motion capture system, force sensors, and the robot operating system (ROS). The human and robot arms’ joint angle and torque profiles demonstrate a similar trend in the experimental environment. The grasping force comparison implies that the human and robot share the load while lifting together.

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Acknowledgements

This study is partially supported by Grant No. T42OH008421 from the National Institute for Occupational Safety & Health (NIOSH) to the Southwest Center for Occupational and Environmental Health (SWCOEH), National Science Foundation project 1849279 and 2014281, and Research Jumpstart/Accelerator Grant from Oklahoma State University. We thank anonymous reviewers for providing us the valuable suggestions to improve the presentation of the material in this study.

Funding

This work was supported by Grant No. T42OH008421 from the National Institute for Occupational Safety & Health (NIOSH/CDC) to the Southwest Center for Occupational and Environmental Health (SWCOEH), National Science Foundation project CBET 1849279 and 2014281, and Research Jumpstart/Accelerator Grant from Oklahoma State University. All these grants are funded to Y.X.

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Authors and Affiliations

  1. School of Mechanical and Aerospace Engineering, Oklahoma State University, Stillwater, OK, 74078, USA

    Asif Arefeen, Joel Quarnstrom, Shahbaz P. Qadri Syed, He Bai & Yujiang Xiang

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  1. Asif Arefeen
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Contributions

Conceptualization, Y.X.; methodology, A.A. and Y.X.; validation, A.A., J.Q. and S.S.; formal analysis, A.A., J.Q. and S.S.; data curation, A.A., J.Q. and S.S.; writing—original draft preparation, A.A., J.Q., and Y.X.; writing—review and editing, Y.X. and H.B.; supervision, Y.X. and H.B.; project administration, Y.X.; funding acquisition, Y.X. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Yujiang Xiang.

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Ethics Approval

The human subject experiment was approved by Institutional Review Board at Oklahoma State University. The IRB number is IRB-21–501.

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Informed consent was obtained from all individual participants included in the study.

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The authors affirm that human research participants provided informed consent for publication of the images in Figure(s) 3 and 12.

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The authors declare they have no financial interests.

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Arefeen, A., Quarnstrom, J., Syed, S.P.Q. et al. Human–Robot Collaborative Lifting Motion Prediction and Experimental Validation. J Intell Robot Syst 109, 80 (2023). https://doi.org/10.1007/s10846-023-02013-y

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