Design and Control Strategy of Soft Robot Based on Gas–Liquid Phase Transition Actuator

Guojian Lin, Wenkai Huang (), Chuanshuai Hu, Junlong Xiao, Fobao Zhou, Xiaolin Zhang, Jiajian Liang and Jiaqiao Liang
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Guojian Lin: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Wenkai Huang: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Chuanshuai Hu: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Junlong Xiao: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Fobao Zhou: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Xiaolin Zhang: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Jiajian Liang: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Jiaqiao Liang: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China

Mathematics, 2022, vol. 10, issue 16, 1-20

Abstract: In this paper, a soft robot driven by a gas–liquid phase transition actuator with a new structure is designed; The soft robot is driven by the pressure generated by electrically induced ethanol phase transition. The gas–liquid phase transition drive was found to be able to generate a larger driving force by using only low voltage. Compared with the gas drive of a traditional soft robot, gas–liquid phase transition-driven soft robot does not require a complex circuit system and a huge external energy supply air pump, making its overall structure more compact. At the same time, because of the new structure of the actuator on the soft robot, the soft robot has good gas tightness and less recovery time. A reinforcement depth learning control strategy is also added so that the soft robot with this actuator could better grip objects of different sizes and weights.

Keywords: soft robot; gas–liquid phase transition; deep reinforcement learning; DQN (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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