Soft Origami Optical-Sensing Actuator for Underwater Manipulation - Shen, Zhong; Zhao, Yafei; Zhong, Hua; Tang, Kailuan; Chen, Yishan; Xiao, Yin; Yi, Juan; Liu, Sicong; Wang, Zheng (Frontiers in Robotics and AI, 2021)
Soft robots are ideal for underwater manipulation towards sampling and other servicing applications. Their unique features of compliance, adaptability, and naturally waterproof, enable robotic designs to be compact, lightweight, while achieving uncompromised dexterity and flexibility. However, the inherent flexibility and high nonlinearity of soft materials also bring combined complex motions, which make both soft actuator and sensor challenges for force output, modeling and sensory feedback, especially under highly dynamic underwater environments. To tackle these limitations, a novel Soft Origami Optical-Sensing Actuator (SOSA) with actuation and sensing integration is proposed in this paper. Inspired by the origami art, the proposed sensorized actuator enables large force output and contraction/elongation/passive bending actuation by fluid, and hybrid motion sensing with optical waveguides. The SOSA design brings two major novelties over state-of-the-art: 1) propose a new actuation-sensing mode which enables superior large payload output, robust and accurate sensing performance with introducing the origami design, significantly facilitating the integration of sensing and actuating technology for wider applications. 2) simplify the fabrication process for harsh environment application, by investigating the boundary features between optical waveguides and ambient water to get rid of the external cladding layer of traditional sensors. With these merits, the proposed actuator could be applied to harsh environments for complex interaction/operation tasks. To showcase the performance of the proposed SOSA actuator, a hybrid underwater 3-DOFs manipulator has been developed. The entire workflow on concept design, fabrication, modeling, experimental validation and application are presented in detail as reference for wider effective robot-environment applications.