Energetic Passivity Decoding of Human Hip Joint for Physical Human-Robot Interaction
Energetic Passivity Decoding of Human Hip Joint for Physical Human-Robot Interaction
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DOI：
10.1109/lra.2020.3010459
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发表时间：
2020-10-01
影响因子：
5.2
通讯作者：
Farina, Dario
中科院分区：
文献类型：
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作者：
Atashzar, S. Farokh;Huang, Hsien-Yung;Farina, Dario
在物理人机交互（pHRI）过程中，人体肢体的生物力学吸收能量的能力在控制以人为中心的机器人系统的性能方面发挥着至关重要的作用。使用“被动过度”的概念，我们最近设计了肘关节和腕关节的被动签名地图。我们还表明，这种知识可以利用和外推过程中的互动与机器人系统的冗余最大化算法。一个主要的应用是机器人康复系统和辅助技术。本文首次揭示了髋关节非线性能容及其影响因素。这对于最大化可穿戴外骨骼的性能至关重要。关于能量吸收行为的知识可以显著地帮助减少控制算法的保守性。在这项工作中，能量的行为进行了研究，为三个不同的髋关节角度，而在三个不同的相互作用速度提供扰动。结果表明，增加激动肌和拮抗肌的收缩可以一致地扩大被动图的边界。此外，通过分离激动剂和拮抗剂收缩的影响，它被确定的被动边际与受试者的姿势在与机器人的相互作用和相关性取决于肌肉收缩的类型。还制定了一个稳定器的初步设计，考虑到可变的被动行为的联合，在能量域，以提高性能，同时保证pHRI稳定性。
The capacity of the biomechanics of human limbs to absorb energy during physical human-robot interaction (pHRI) can play an imperative role in controlling the performance of human-centered robotics systems. Using the concept of "excess of passivity," we have recently designed passivity signature maps for elbow and wrist joints. We have also shown that this knowledge can be exploited and extrapolated during the interaction with a robotic system by transparency-maximized algorithms. A major application is in robotic rehabilitation systems and assistive technologies. Here, for the first time, the nonlinear energy capacitance of the hip joint and the affecting factors are decoded. This can be critical for maximizing the performance of wearable exoskeletons. Knowledge regarding energy absorption behavior can significantly help to reduce the conservatism of control algorithms. In this work, the energetic behavior is studied for three different hip angles, while perturbations were provided at three different interaction speeds. The results show that the increase in agonist and antagonist muscle contractions can consistently expand the margins of the passivity map. Additionally, by separating the effects of agonist and antagonist contractions, it was identified that the passivity margins have a correlation with the subject's posture during interaction with the robot and the correlation depends on the type of muscle contraction. A preliminary design of a stabilizer is also formulated that takes into account variable passivity behavior of the joint, in the energy domain, to enhance the performance while guaranteeing pHRI stability.