Rhythm-Based Power Allocation Strategy of Bionic Tail-Flapping for Propulsion Enhancement

With the vast demand in marine development, robotic fish show promising potential in underwater exploration for their high-performance propulsion ability. However, fish-inspired robots are yet to utilize the structural flexibility of rhythmic actuation such as bony fish (Osteichthyes). The Body and Caudal Fin (BCF) locomotion in fish optimizes the use of muscle power and body flexibility by synchronizing muscle activation with the undulating- oscillatory tail-flapping, such as Thunniform, while robotic fish are primarily designed as motion trackers rather than as efficient swimmers. In this article, we propose a power allocation strategy (PAS) that imitates muscle rhythmic actuation, which increases the flapping amplitude by the coupling of the peduncle motion and the tail deformation. Inspired by this peduncle-tail mechanism, we de- veloped a direct-drive fish robot (DDRFishBot). The DDRFishBot is enhanced by our developed PAS in tail-elastic potential energy release by 228%, in propulsion by 45.6%, and in efficiency coeffi- cient by 16.3%. This study establishes the performance enhance- ment principle of exploiting tail flexibility through a simple scotch yoke mechanism, expanding the performance space of fish-inspired tail-flapping swimming robot. Received 12 March 2025; accepted 24 May 2025. Date of publication 9 June 2025; date of current version 30 June 2025. This work was sup- ported in part by the National Key R&D Program of China under Grant 2022YFB4701200, National Natural Science Foundation of China under Grant 52475302, in part by the Shenzhen Science and Technology Program un- der Grant JCYJ20220530114615034, Grant JCYJ20220818100417038, and Grant ZDSYS20220527171403009, in part by the University high level of special funds under Grant G03034K003 of Southern University of Science and Technology, Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics under Grant 2023B1212010005, in part by the Strategic Topics Grant of Research Grants Council under Grant STG1/E- 401/23-N, and in part by the CRCG under Grant 2302101740. This article was recommended for publication by Associate Editor L. Wen and Editor A. Menciassi upon evaluation of the reviewers’ comments. (Biao Wu and Chaoyi Huang contributed equally to this work.) (Corresponding authors: Sicong Liu; Jiansheng Dai.) Biao Wu, Jiahao Xu, and Jiansheng Dai are with the Department of Mechan- ical and Energy Engineering, Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems, Southern University of Science and Technology, Shenzhen 518055, China (e-mail: 12132303@mail.sustech.edu.cn; 12132312@mail.sustech.edu.cn; daijs@sustech.edu.cn). Chaoyi Huang and James Lam are with the Department of Mechanical Engineering,TheUniversityofHongKong,Pokfulam,HongKongSAR(e-mail: u3562918@hku.hk; james.lam@hku.hk). Xiangru Li is with the Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR (e-mail: xlihh@connect.ust.hk). Sicong Liu is with the Sino-German College of Intelligent Manufactur- ing, Shenzhen Technology University, Pingshan 518118, China (e-mail: liu- sicong@sztu.edu.cn). Zheng Wang is with the Wisson Robotics, Shenzhen 518001, China (e-mail: zheng.wang@ieee.org). This article has supplementary downloadable material available at https://doi.org/10.1109/TRO.2025.3577985, provided by the authors. Digital Object Identifier 10.1109/TRO.2025.3577985