Design and Modeling of an Integral Molding Flexible Tail for Robotic Fish
Ru Tong, Zhengxing Wu, Sijie Li, Di Chen, Jian Wang, Min Tan, Junzhi Yu
Abstract
Tail flexibility optimization is crucial for improving the speed and efficiency of robotic fish. However, reliable flexible tail structures and accessible modeling methods are still in the exploratory stage. This article proposes a novel integral molding flexible fish tail (IMFFT) characterized by continuous flexibility, a hollow air cavity, and an embedded skeletal structure. These features empower the tail with continuous flexible propulsion capabilities, neutral buoyancy for stable fish posture, and limited compressibility for withstanding water pressure. In addition, a predictive-network-based model for the IMFFT is developed through thrust data acquisition, dynamic analysis, and predictive network training. Specifically, the predictive network enables the prediction of pattern parameters of passive angles on the flexible tail. Deploying the IMFFT on our self-developed robotic tuna, performance tests demonstrate significant improvements, including a high swimming speed of 3.28 body lengths per second, an average speed improvement of 25.30%, an average cost of transport (COT) reduction of 24.45%, and a 57.12% reduction in roll angle fluctuation range due to the neutral buoyancy of the fish tail, which competes favorably with rigid fish tails and other flexible tail structures. This study provides novel guidance for optimizing the flexibility of underwater bioinspired robots.