Compliance while resisting: A shear-thickening fluid controller for physical human-robot interaction

Author:

Chen Lu1ORCID,Chen Lipeng2,Chen Xiangchi2,Lu Haojian1ORCID,Zheng Yu2,Wu Jun1,Wang Yue1,Zhang Zhengyou2,Xiong Rong1

Affiliation:

1. Zhejiang University, Hangzhou, China

2. Tencent Robotics X Lab, Shenzhen, China

Abstract

Physical human-robot interaction (pHRI) is widely needed in many fields, such as industrial manipulation, home services, and medical rehabilitation, and puts higher demands on the safety of robots. Due to the uncertainty of the working environment, the pHRI may receive unexpected impact interference, which affects the safety and smoothness of the task execution. The commonly used linear admittance control (L-AC) can cope well with high-frequency small-amplitude noise, but for medium-frequency high-intensity impact, the effect is not as good. Inspired by the solid-liquid phase change nature of shear-thickening fluid, we propose a shear-thickening fluid control (SFC) that can achieve both an easy human-robot collaboration and resistance to impact interference. The SFC’s stability, passivity, and phase trajectory are analyzed in detail, the frequency and time domain properties are quantified, and parameter constraints in discrete control and coupled stability conditions are provided. We conducted simulations to compare the frequency and time domain characteristics of L-AC, nonlinear admittance controller (N-AC), and SFC and validated their dynamic properties. In real-world experiments, we compared the performance of L-AC, N-AC, and SFC in both fixed and mobile manipulators. L-AC exhibits weak resistance to impact. N-AC can resist moderate impacts but not high-intensity ones and may exhibit self-excited oscillations. In contrast, SFC demonstrated superior impact resistance and maintained stable collaboration, enhancing comfort in cooperative water delivery tasks. Additionally, a case study was conducted in a factory setting, further affirming the SFC’s capability in facilitating human-robot collaborative manipulation and underscoring its potential in industrial applications.

Funder

National Nature Science Foundation of China

Research and Development Project of Zhejiang Province

Zhejiang Provincial Natural Science Foundation of China

Innovation and Development Special Fund of the Hangzhou Chengxi Sci-tech Innovation Corridor

Tencent AI Lab Rhino-Bird Focused Research Program

Publisher

SAGE Publications

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