兼顾人体工学与操作能力，优化双臂人机协作时的姿势。
Integrating Ergonomics and Manipulability for Upper Limb Postural Optimization in Bimanual Human-Robot Collaboration
- 通过优化简化人体骨骼的关节角度，平衡安全与操作效率。
- 实验显示优化后目标肌群激活显著降低，肌肉负担减轻。
- 适合人机协作中需长期稳定操作的场景，如医疗或工业装配。
本文提出一种上肢姿势优化方法，以提升双臂人机协同搬运任务中的身体舒适性与力控操作能力。现有研究多侧重于人身安全或操作效率，而本方法首次将两者融合，适应不同抓握姿态和物体形状。通过最小化代价函数，优化简化人体骨架模型的关节角度，优先保障安全与操作性能。利用变换模块生成机器人末端执行器的参考位姿，引导人类进入优化姿势。针对类人机器人CURI，设计了一种双臂模型预测阻抗控制器（MPIC），通过规划轨迹实时校正末端位姿。该方法在多人-人协作（HHC）和人-机器人协作（HRC）中对多种对象进行了验证。实验结果表明，优化前后目标肌群激活水平显著下降，肌肉状态明显改善。
原文摘要 · Abstract (English)
This paper introduces an upper limb postural optimization method for enhancing physical ergonomics and force manipulability during bimanual human-robot co-carrying tasks. Existing research typically emphasizes human safety or manipulative efficiency, whereas our proposed method uniquely integrates both aspects to strengthen collaboration across diverse conditions (e.g., different grasping postures of humans, and different shapes of objects). Specifically, the joint angles of a simplified human skeleton model are optimized by minimizing the cost function to prioritize safety and manipulative capability. To guide humans towards the optimized posture, the reference end-effector poses of the robot are generated through a transformation module. A bimanual model predictive impedance controller (MPIC) is proposed for our human-like robot, CURI, to recalibrate the end effector poses through planned trajectories. The proposed method has been validated through various subjects and objects during human-human collaboration (HHC) and human-robot collaboration (HRC). The experimental results demonstrate significant improvement in muscle conditions by comparing the activation of target muscles before and after optimization.
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