Full Text:  <258>

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CLC number: TP242.6

On-line Access: 2023-01-21

Received: 2022-02-22

Revision Accepted: 2022-08-08

Crosschecked: 2023-01-21

Cited: 0

Clicked: 167

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Jiaqi WANG

https://orcid.org/0000-0003-4084-135X

Wei DONG

https://orcid.org/0000-0002-1211-6444

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Frontiers of Information Technology & Electronic Engineering 

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Probabilistic movement primitive based motion learning for a lower limb exoskeleton with black-box optimization


Author(s):  Jiaqi WANG, Yongzhuo GAO, Dongmei WU, Wei DONG

Affiliation(s):  State Key Laboratory of Robotics and Systems, Harbin Institute of Technology, Harbin 150001, China

Corresponding email(s):  wangjq@hit.edu.cn, gaoyongzhuo@hit.edu.cn, wdm@hit.edu.cn, dongwei@hit.edu.cn

Key Words:  Lower limb exoskeleton; Human-robot interaction; Motion learning; Trajectory generation; Movement primitive; Black-box optimization


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Jiaqi WANG, Yongzhuo GAO, Dongmei WU, Wei DONG. Probabilistic movement primitive based motion learning for a lower limb exoskeleton with black-box optimization[J]. Frontiers of Information Technology & Electronic Engineering , 2023, 24(6): 104-116.

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volume="24",
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year="2023",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200065"
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Abstract: 
As a wearable robot, an exoskeleton provides a direct transfer of mechanical power to assist or augment the wearer’s movement with an anthropomorphic configuration. When an exoskeleton is used to facilitate the wearer’s movement, a motion generation process often plays an important role in high-level control. One of the main challenges in this area is to generate in real time a reference trajectory that is parallel with human intention and can adapt to different situations. In this paper, we first describe a novel motion modeling method based on probabilistic movement primitive (ProMP) for a lower limb exoskeleton, which is a new and powerful representative tool for generating motion trajectories. To adapt the trajectory to different situations when the exoskeleton is used by different wearers, we propose a novel motion learning scheme based on black-box optimization (BBO) PIBB combined with ProMP. The motion model is first learned by ProMP offline, which can generate reference trajectories for use by exoskeleton controllers online. PIBB is adopted to learn and update the model for online trajectory generation, which provides the capability of adaptation of the system and eliminates the effects of uncertainties. Simulations and experiments involving six subjects using the lower limb exoskeleton HEXO demonstrate the effectiveness of the proposed methods.

针对下肢外骨骼机器人的基于概率运动基元的黑盒优化运动学习

王嘉琪,高永卓,吴冬梅,董为
哈尔滨工业大学机器人技术与系统国家重点实验室,中国哈尔滨市,150001
摘要:外骨骼作为一种可穿戴的机器人,通过拟人化的构型直接传递机械动力来辅助或增强穿戴者运动。当外骨骼用于促进穿戴者的运动时,运动生成过程通常在高层控制中发挥重要作用。该领域的主要挑战之一是实时生成符合人类意图且可以适应不同情况的参考轨迹。在本文中,我们首先提出了一种基于概率运动基元(ProMP)的下肢外骨骼运动建模方法,它是一种用于生成运动轨迹的新型且强大的代表性工具。为了在不同穿戴者使用外骨骼时使轨迹适应不同情况,我们接着提出了一种基于黑盒优化PIBB结合ProMP的新型运动学习方案。运动模型首先由ProMP离线学习,它可以生成参考轨迹供外骨骼控制器在线使用,再采用PIBB在线学习和更新模型,提供了系统的自适应能力,消除了不确定性的影响。使用下肢外骨骼HEXO对六名受试者进行的模拟和实验证明了所提出方法的有效性。

关键词组:下肢外骨骼机器人;人机交互;运动学习;轨迹生成;运动基元;黑盒优化

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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