Developing hybrid aerial-aquatic vehicles that can interact with water surfaces while remaining aloft is valuable for various tasks, including ecological monitoring, water quality sampling, and search and rescue operations. Storm petrels are a group of pelagic seabirds that exhibit a unique locomotion pattern known as 'pattering' or 'sea-anchoring,' which is hypothesized to support forward locomotion and/or stationary posture at the water surface. In this study, we use morphological measurements of three storm petrel species and aero/hydrodynamic models to develop a computational storm petrel model and interact it with a hybrid fluid environment. Using deep reinforcement learning algorithms, we find that the storm petrel model exhibits high maneuverability and stability under a wide range of constant wind velocities after training. We also verify in the simulation that the storm petrel can use its 'pattering' or 'sea-anchoring' behavior to achieve different biomechanical sub-tasks (e.g. weight support, forward locomotion, stabilization) and adapt it under different wind speeds and optimization objectives. Specifically, we observe an adjustment in storm petrel's movement patterns as wind velocity increases and quantitively analyze its biomechanics underneath. Our results provide new insights into how storm petrels achieve efficient locomotion and dynamic stability at the air-water interface and adapt their behaviors to different wind velocities and tasks in open environments. Ultimately, our study will guide the design of next-generation biomimetic petrel-inspired robots for tasks requiring proximity to the water interface and efficiency.
Keywords: deep reinforcement learning; locomotory behaviors; storm petrel.
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