A Fast Online Elastic-Spine-Based Stiffness Adjusting Mechanism for Fishlike Swimming.

Abstract

Fish tunes fishtail stiffness by coordinating its tendons, muscles, and other tissues to improve swimming performance. For robotic fish, achieving a fast and online fishlike stiffness adjustment over a large-scale range is of great significance for performance improvement. This article proposes an elastic-spine-based variable stiffness robotic fish, which adopts spring steel to emulate the fish spine, and its stiffness is adjusted by tuning the effective length of the elastic spine. The stiffness can be switched in the maximum adjustable range within 0.26 s. To optimize the motion performance of robotic fish by adjusting fishtail stiffness, a Kane-based dynamic model is proposed, based on which the stiffness adjustment strategy for multistage swimming is constructed. Simulations and experiments are conducted, including performance measurements and analyses in terms of swimming speed, thrust, and so on, and online stiffness adjustment-based multistage swimming, which verifies the feasibility of the proposed variable stiffness robotic fish. The maximum speed and lowest cost of transport for robotic fish are 0.43 m/s (equivalent to 0.81 BL/s) and 7.14 J/(kg·m), respectively.