Multi-Dimensional Proprioception and Stiffness Tuning for Soft Robotic Joints
Zhonggui Fang, Chaoyi Huang, Yaxi Wang, Jiahao Xu, Jiyong Tan, Bin Li, Zichen Wang, Yige Wu
Abstract
Proprioception and variable stiffness are two trending topics in soft robotics research. The former could endow soft robots with the ability to perceive the environment as well as their internal states without the need of dedicated sensors, while the latter could strengthen the otherwise excessive compliance, enabling soft robots for tasks which require a higher force. Both directions have been extensively reported in existing literature, achieving both concurrently was even more challenging. The major limiting factor was the limited stiffness due to the hyper elasticity of conventional soft robots, which increases the difficulties in capturing the continues deformation. In this work, we proposed an alternative approach to tackle these two challenges, a novel “tune-down” approach, combining proprioception with stiffness regulation and implemented over-constrained soft robotic joint designs to further strengthen this spirit. As a result, the soft robotic joint could achieve multi-directional proprioception, as well as variable stiffness tuning, concurrently, using merely an on-board sensor for basic pneumatic control. The concept, design, modeling, actuation/control, and experimental validation were presented in detail, demonstrating the efficacy and potential of the proposed approach.
BibTeX
@inproceedings{icra2022_multidimensional,
title = {Multi-Dimensional Proprioception and Stiffness Tuning for Soft Robotic Joints},
author = {Zhonggui Fang and Chaoyi Huang and Yaxi Wang and Jiahao Xu and Jiyong Tan and Bin Li and Zichen Wang and Yige Wu and Anlun Huang and Juan Yi and Sicong Liu and Zheng Wang},
booktitle = {ICRA 2022},
year = {2022}
}