Biologically inspired underwater soft robotics: Enhanced force production and disturbance rejection.

Abstract

Swimming animals have higher manoeuvrability for equivalent efficiency than engineered rigid underwater vehicles. It is proposed that this capability stems from their highly deformable nature. Soft robotics and their control could be the key to unlocking improved manoeuvrability and efficiency for swimming robots. The optimal stiffness for soft swimming robots depends on swimming speed, which means no single stiffness can maximise efficiency in all swimming conditions. Tunable-stiffness would produce an increased range of high-efficiency swimming speeds for robots with flexible propulsors and enable soft control surfaces for steering underwater vehicles. I propose and demonstrate a method for tunable soft robotic stiffness using inflatable rubber tubes to stiffen a silicone foil through pressure and second moment of area change. I achieved double the effective stiffness of the system for an input pressure change from 0 to 0.8 bar and 60 J energy input. I achieved a resonant amplitude gain of 5 to 7 times the input amplitude and doubled the high-gain frequency range compared to a foil with fixed stiffness. These results show that changing the second moment of area is an energy efficient approach to tunable-stiffness robots. I propose that there would also be additional propulsive efficiency benefits by using time varying stiffness modulation within a kinematic cycle. I show that the stiffness modulation at twice the flapping frequency, 90° out of phase from the motion, allows for the tail to load and de-load at advantageous stages within the kinematic cycle. This results in a 20% tail amplitude increase, and increased lift and thrust production without the system needing to be at the natural frequency. The use of soft robotics for real-world underwater applications is limited, even more than in terrestrial applications, by the ability to accurately measure and control the deformation of the soft materials in real time without the need for feedback from an external sensor. I propose that real-time underwater shape estimation would allow for accurate closed-loop control of soft propulsors, enabling high-performance swimming and manoeuvring. I propose and demonstrate a method for closed-loop underwater soft robotic foil control based on a flexible capacitive e-skin with machine learning, which does not necessitate feedback from an external sensor. The underwater e-skin is applied to a highly flexible foil undergoing deformations from 2% to 9% of its camber, by means of soft hydraulic actuators. Accurate set point regulation of the camber is successfully tracked during sinusoidal and triangle actuation routines; (a) with a 5% peak-to-peak amplitude, a 10-second period, and a normalised RMS error of 0.11; (b) with a 2% peak-to-peak amplitude, a period of 5 seconds, and a normalised RMS error of 0.03. The tail tip deflection can be measured across a 30mm (0.15 chord) range. These results pave the way for using e-skin technology for underwater soft robotic closed-loop control applications. I present a rapidly morphing proprioceptive soft wing for disturbance rejecting autonomous underwater vehicles. The system has a response time of 4.5 s for a 30mm (0.15 chord) range. I demonstrate the ability of the soft wing to autonomously identify a sudden disturbance in the flow direction and compensate for the associated unwanted lift change by actively modifying its shape. The closed-loop system autonomously reduces the impulse of a gust, by 87%, that would otherwise generate a disturbance as large as twice the non-disturbed lift coefficient of an equivalent rigid wing.

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Identifiers

ORCID for Gabriel Weymouth: orcid.org/0000-0001-5080-5016

ORCID for Francesco Giorgio-serchi: orcid.org/0000-0002-5090-9007

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