颗粒地形上人类行走的膝关节外骨骼辅助控制
Assistive Control of Knee Exoskeletons for Human Walking on Granular Terrains
AI总结：
针对沙地行走能耗与步态变化，提出基于GRF和关节力矩估计的膝关节外骨骼模型预测刚度控制，室内外实验证实肌肉激活降低15%、代谢降低3.7%。
摘要：
人类步行者会穿行于各种环境，并且与在坚实地面上相比，在颗粒地形上表现出不同的步态运动和能量消耗。我们提出了一种用于沙地膝关节外骨骼辅助的、基于刚度的模型预测控制方法。首先讨论了人类步行者在沙地和坚实地面上的步态与运动比较。随后提出一种基于机器学习的估计方案，以实时预测人类步行者在不同地形上的地面反作用力（GRFs）。基于估计的GRFs和人体关节力矩，设计了膝关节外骨骼控制器，通过模型预测刚度控制方案提供辅助力矩。我们开展了室内和室外实验，以验证建模与控制设计及其性能。实验表明，在辅助外骨骼控制下，人类在沙地上行走时主要肌肉激活和代谢分别降低15%和3.7%。
英文摘要：
Human walkers traverse diverse environments and demonstrate different gait locomotion and energy cost on granular terrains compared to solid ground. We present a stiffness-based model predictive control approach of knee exoskeleton assistance on sand. The gait and locomotion comparison is first discussed for human walkers on sand and solid ground. A machine learning-based estimation scheme is then presented to predict the ground reaction forces (GRFs) for human walkers on different terrains in real time. Built on the estimated GRFs and human joint torques, a knee exoskeleton controller is designed to provide assistive torque through a model predictive stiffness control scheme. We conduct indoor and outdoor experiments to validate the modeling and control design and their performance. The experiments demonstrate the major muscle activation and metabolic reductions by respectively 15% and 3.7% under the assistive exoskeleton control of human walking on sand.
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