Smart navigation of a gravity-driven glider with adjustable centre-of-mass
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Abstract (arXiv)
Artificial gliders are designed to disperse as they settle through a fluid, requiring precise navigation to reach target locations. We show that a compact glider settling in a viscous fluid can navigate by dynamically adjusting its centre-of-mass. Using fully resolved direct numerical simulations (DNS) and reinforcement learning, we find two optimal navigation strategies that allow the glider to reach its target location accurately. These strategies depend sensitively on how the glider interacts with the surrounding fluid. The nature of this interaction changes as the particle Reynolds number Rep changes. Our results explain how the optimal strategy depends on Rep. At large Rep, the glider learns to tumble rapidly by moving its centre-of-mass as its orientation changes. This generates a large horizontal inertial lift force, which allows the glider to travel far. At small Rep, by contrast, high viscosity hinders tumbling. In this case, the glider learns to adjust its centre-of-mass so that it settles with a steady, inclined orientation that results in a horizontal viscous force. The horizontal range is much smaller than for large Rep, because this viscous force is much smaller than the inertial lift force at large Rep. *These authors contributed equally.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 49 references that are in the map. First posted on arXiv 10 Oct 2025.
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References · 5
- 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
- 2017Flow Navigation by Smart Microswimmers via Reinforcement Learningcited by 34
- 2021Learning Efficient Navigation in Vortical Flow Fieldscited by 26
- 2019Machine learning strategies for path-planning microswimmers in turbulent flowscited by 14
- 2019Machine learning with neural networkscited by 3
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