Ko Hungtang, Saro-Cortes Valeria, Mmari Brian, Ni Di, Wissa Aimy, Nagpal Radhika
Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ 08540, United States of America.
Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08540, United States of America.
Bioinspir Biomim. 2025 Aug 21;20(5). doi: 10.1088/1748-3190/adf2e9.
Aquatic ecosystems vital to biodiversity and climate change-such as coral reefs, kelp forests, and mangrove forests-are often cluttered with natural obstacles. To navigate these complex habitats, fish have evolved relatively small body sizes and outstanding maneuverability. In contrast, most unmanned underwater vehicles currently deployed for ocean monitoring are bulky and slow, limiting their ability to access these environments. Developing small and agile underwater robots that mimic native fish species provides a unique opportunity for automated sampling of dynamic aquatic ecosystems. In this paper, we present BlueGuppy, a miniature, low-cost, and untethered fish-like robot (9.5×2.4×3.0cm, 33.1 g) capable of maneuvering with a single actuator. It achieves swimming speeds of up to 2.8 body lengths per second and can execute tight turns with small circles 1.4 body lengths in radius. BlueGuppy can generate a net thrust even in the presence of an incoming flow, but the flow field around BlueGuppy only mirrors that of biological organisms when it is free-swimming, underscoring the importance of untethered robots for biomimetic research. We explored the maneuverability of BlueGuppy by tuning its kinematics. By varying its flapping frequencies and temporal bias, BlueGuppy can access a wide range of speeds and turning curvatures. The combination of speed, maneuverability, and simplicity establishes BlueGuppy as a unique platform in the literature with tremendous potential for both uncovering the biomechanics of schooling fish and advancing the state-of-the-art in autonomous ocean sampling.
对生物多样性和气候变化至关重要的水生生态系统，如珊瑚礁、海带林和红树林，常常布满天然障碍物。为了在这些复杂的栖息地中穿行，鱼类进化出了相对较小的体型和出色的机动性。相比之下，目前用于海洋监测的大多数无人水下航行器体积庞大且速度缓慢，限制了它们进入这些环境的能力。开发模仿本地鱼类的小型且敏捷的水下机器人，为动态水生生态系统的自动采样提供了独特的机会。在本文中，我们展示了BlueGuppy，这是一种微型、低成本且无缆绳的类鱼机器人（9.5×2.4×3.0厘米，33.1克），能够通过单个致动器进行机动。它的游泳速度可达每秒2.8个体长，并且能够以半径为1.4个体长的小圆圈进行急转弯。即使在有来流的情况下，BlueGuppy也能产生净推力，但只有在自由游动时，BlueGuppy周围的流场才会反映生物有机体的流场，这凸显了无缆绳机器人在仿生研究中的重要性。我们通过调整BlueGuppy的运动学来探索其机动性。通过改变其拍打频率和时间偏差，BlueGuppy可以获得广泛的速度和转弯曲率。速度、机动性和简易性的结合，使BlueGuppy在文献中成为一个独特的平台，在揭示集群鱼类的生物力学以及推动自主海洋采样的技术水平方面具有巨大潜力。
