Design, Modeling, and Optimization of Hydraulically Powered Double-Joint Soft Robotic Fish

Abstract

This article explores a hydraulically powered double-joint soft robotic fish called HyperTuna and a set of locomotion optimization methods. HyperTuna has an innovative, highly efficient actuation structure that includes a four-cylinder piston pump and a double-joint soft actuator with self-sensing. We conducted deformation analysis on the actuator and established a finite element model to predict its performance. A closed-loop strategy combining a central pattern generator controller and a proportional–integral–derivative controller was developed to control the swimming posture accurately. Next, a dynamic model for the robotic fish was established considering the soft actuator, and the model parameters were identified via data-driven methods. Then, a particle swarm optimization algorithm was adopted to optimize the control parameters and improve the locomotion performance. Experimental results showed that the maximum speed increased by 3.6% and the cost of transport ($\text{COT}$) decreased by up to 13.9% at 0.4 m/s after optimization. The proposed robotic fish achieved a maximum speed of 1.12 BL/s and a minimum $\text{COT}$ of 12.1 J/(kg·m), which are outstanding relative to those of similar soft robotic fish. Finally, HyperTuna completed turning and diving–floating movements and long-distance continuous swimming in open water, which confirmed its potential for practical application.