Mechanism Characteristics Identification and Anti-Disturbance Control for Door-Opening Using Supernumerary Robotic Limbs

Supernumerary robotic limbs (SRLs) are wearable robots that serve as extra limbs, assisting wearers in task completion. The strong coupling between SRLs and wearers results in wearer movements influencing SRLs' actions and the forces exerted on mechanisms during operations. The wearer's movements lead to uncertain and uncontrollable base movements, making operational control of the SRLs challenging. This study proposes a full-process assisted door-opening method using SRLs, integrating a task model and an anti-disturbance door-opening controller. Multimodal information from the SRLs system is utilized to facilitate task state transitions. The anti-disturbance door-opening controller consists of compliant controller, online identification of mechanism characteristics, end-effector trajectory generator, and notch filter. This method enables the SRLs to complete the door-opening task under motion disturbances caused by the wearer, without prior knowledge of the door's resistance, radius, or axis direction. Experimental results show that the SRLs can successfully open doors, with estimates converging to actual values under various radius and axis direction conditions (with radius estimation error less than 0.01 m and axial estimation error less than 3.8<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula>). The effectiveness of the proposed control framework is validated in daily environments.

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Institutions

Publication Details

Journal
IEEE Robotics and Automation Letters
Published
2024-12-04
DOI
https://doi.org/10.1109/lra.2024.3511172
Citations
3
Primary Topic
Robot Manipulation and Learning
Type
article
Field-Weighted Citation Impact
0.98

Funders

Controls
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article

Mechanism Characteristics Identification and Anti-Disturbance Control for Door-Opening Using Supernumerary Robotic Limbs

Xianglong Li, Qinghua Zhang, Hongwei Jing, Yanhe Zhu et al.
3 citations
IEEE Robotics and Automation Letters
Robot Manipulation and Learning
0.98
article

Mechanism Characteristics Identification and Anti-Disturbance Control for Door-Opening Using Supernumerary Robotic Limbs

Xianglong Li, Qinghua Zhang, Hongwei Jing, Yanhe Zhu, Tianjiao Zheng, Lele Li, Jie Zhao, Jian Qi, Kerui Sun
article en
3 citations

Abstract

Supernumerary robotic limbs (SRLs) are wearable robots that serve as extra limbs, assisting wearers in task completion. The strong coupling between SRLs and wearers results in wearer movements influencing SRLs' actions and the forces exerted on mechanisms during operations. The wearer's movements lead to uncertain and uncontrollable base movements, making operational control of the SRLs challenging. This study proposes a full-process assisted door-opening method using SRLs, integrating a task model and an anti-disturbance door-opening controller. Multimodal information from the SRLs system is utilized to facilitate task state transitions. The anti-disturbance door-opening controller consists of compliant controller, online identification of mechanism characteristics, end-effector trajectory generator, and notch filter. This method enables the SRLs to complete the door-opening task under motion disturbances caused by the wearer, without prior knowledge of the door's resistance, radius, or axis direction. Experimental results show that the SRLs can successfully open doors, with estimates converging to actual values under various radius and axis direction conditions (with radius estimation error less than 0.01 m and axial estimation error less than 3.8<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula>). The effectiveness of the proposed control framework is validated in daily environments.

IEEE Robotics and Automation LettersVol. 10(1)
Harbin Institute of Technology (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 22%
Robot Manipulation and Learning
0.98
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