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<p>In the field of human-robot collaboration, enhancing the performance of interaction tasks in open space remains a critical challenge. Human-robot interaction based on surface electromyography (sEMG) has been attracted wide attention driven by the potential motion intentions. To improve the performance of robots performing interaction tasks in open space, a method based on a neuromusculoskeletal model and joint variable impedance controller is proposed, which is driven by intentions inferred from sEMG signals. First, a human upper limb musculoskeletal model is developed from the sEMG signal and calibrated to match the optimal motor behavior of the operator. Then, a joint variable impedance control algorithm is designed. Finally, an experimental study is conducted to perform drag trajectory tracking motion in open space, and the proposed method is experimentally compared with the constant impedance method of robots. The results show that the operator’s energy consumption is reduced by 6.28&#xa0;J, the average force is reduced by 3.83&#xa0;N, and the task execution time is reduced by 5.25s under the variable-impedance approach compared to high-impedance. The trajectory tracking error is also reduced by 1.4&#xa0;mm for variable-impedance compared to low-impedance. Our method using intention-driven adaptive impedance control balances the robot’s performance metrics between fast free motion and precise tracking motion in complex tracking motion tasks.</p>

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Intention-driven adaptive impedance control of human-robot collaboration for open space tasks

  • 期刊论文

  • 发表日期：

    2025年9月4日

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  • Hongmiao Zhang,
  • Rui Zhou,
  • Guozheng Wei,
  • Zhaohua Yuan,
  • Shaolong Kuang

摘要

In the field of human-robot collaboration, enhancing the performance of interaction tasks in open space remains a critical challenge. Human-robot interaction based on surface electromyography (sEMG) has been attracted wide attention driven by the potential motion intentions. To improve the performance of robots performing interaction tasks in open space, a method based on a neuromusculoskeletal model and joint variable impedance controller is proposed, which is driven by intentions inferred from sEMG signals. First, a human upper limb musculoskeletal model is developed from the sEMG signal and calibrated to match the optimal motor behavior of the operator. Then, a joint variable impedance control algorithm is designed. Finally, an experimental study is conducted to perform drag trajectory tracking motion in open space, and the proposed method is experimentally compared with the constant impedance method of robots. The results show that the operator’s energy consumption is reduced by 6.28 J, the average force is reduced by 3.83 N, and the task execution time is reduced by 5.25s under the variable-impedance approach compared to high-impedance. The trajectory tracking error is also reduced by 1.4 mm for variable-impedance compared to low-impedance. Our method using intention-driven adaptive impedance control balances the robot’s performance metrics between fast free motion and precise tracking motion in complex tracking motion tasks.