Thomas Gray C, Campbell Orion, Nichols Nick, Brissonneau Nicolas, He Binghan, James Joshua, Paine Nicholas, Sentis Luis
Department of Electrical Engineering and Computer Science, The University of Michigan, Ann Arbor, MI, United States.
Apptronik Inc., Austin, TX, United States.
Front Robot AI. 2021 Sep 27;8:720231. doi: 10.3389/frobt.2021.720231. eCollection 2021.
Augmenting the physical strength of a human operator during unpredictable human-directed (volitional) movements is a relevant capability for several proposed exoskeleton applications, including mobility augmentation, manual material handling, and tool operation. Unlike controllers and augmentation systems designed for repetitive tasks (e.g., walking), we approach physical strength augmentation by a task-agnostic method of force amplification-using force/torque sensors at the human-machine interface to estimate the human task force, and then amplifying it with the exoskeleton. We deploy an amplification controller that is integrated into a complete whole-body control framework for controlling exoskeletons that includes human-led foot transitions, inequality constraints, and a computationally efficient prioritization. A powered lower-body exoskeleton is used to demonstrate behavior of the control framework in a lab environment. This exoskeleton can assist the operator in lifting an unknown backpack payload while remaining fully backdrivable.
在不可预测的人为导向(自主)运动中增强人类操作员的体力,对于包括增强移动性、人工物料搬运和工具操作在内的多种外骨骼应用来说是一项重要能力。与为重复性任务(如行走)设计的控制器和增强系统不同,我们采用一种与任务无关的力放大方法来增强体力——在人机接口处使用力/扭矩传感器估计人类任务力,然后通过外骨骼对其进行放大。我们部署了一个放大控制器,该控制器集成到一个完整的全身控制框架中,用于控制外骨骼,该框架包括人为引导的足部过渡、不等式约束和计算效率高的优先级排序。一个动力下肢外骨骼被用于在实验室环境中展示控制框架的行为。这种外骨骼可以帮助操作员提起未知的背包负载,同时保持完全可回推。