Abstract

Soft robots driven by stimuli-responsive materials have their own unique advantages over traditional rigid robots such as large actuation, light weight, good flexibility and biocompatibility. However, the large actuation of soft robots inherently co-exists with difficulty in control with high precision. This article presents a soft artificial muscle driven robot mimicking cuttlefish with a fully integrated on-board system including power supply and wireless communication system. Without any motors, the movements of the cuttlefish robot are solely actuated by dielectric elastomer which exhibits muscle-like properties including large deformation and high energy density. Reinforcement learning is used to optimize the control strategy of the cuttlefish robot instead of manual adjustment. From scratch, the swimming speed of the robot is enhanced by 91% with reinforcement learning, reaching to 21 mm/s (0.38 body length per second). The design principle behind the structure and the control of the robot can be potentially useful in guiding device designs for demanding applications such as flexible devices and soft robots.

Details

Title
A soft artificial muscle driven robot with reinforcement learning
Author
Yang, Tao 1 ; Xiao, Youhua 2 ; Zhang, Zhen 1 ; Liang, Yiming 1 ; Li, Guorui 1 ; Zhang, Mingqi 1 ; Li, Shijian 3 ; Wong, Tuck-Whye 4 ; Wang, Yong 5 ; Li, Tiefeng 5 ; Huang, Zhilong 5 

 Department of Engineering Mechanics, Zhejiang University, Hangzhou, China 
 Department of Chemical and Biological Engineering, Zhejiang University, Hangzhou, China 
 Department of Computer Science, Zhejiang University, Hangzhou, China 
 Advanced Membrane Technology Research Centre, Universiti Tekonologi Malaysia, Johor, Malaysia 
 State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, China; Department of Engineering Mechanics, Zhejiang University, Hangzhou, China; Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province, Zhejiang University, Hangzhou, China 
Pages
1-8
Publication year
2018
Publication date
Sep 2018
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2113768852
Copyright
© 2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.