Smart navigation of a gravity-driven glider with adjustable centre-of-mass
This work addresses precise navigation for artificial gliders in fluid environments, which is incremental as it builds on existing methods by introducing adaptive control strategies.
The study tackled the problem of navigating a gravity-driven glider in a viscous fluid by dynamically adjusting its center-of-mass, using DNS and reinforcement learning to find optimal strategies that depend on particle Reynolds number, achieving accurate target reach with strategies like tumbling at high Re_p for large horizontal range and steady inclination at low Re_p for smaller range.
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 Re$_p$ changes. Our results explain how the optimal strategy depends on Re$_p$. At large Re$_p$, 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 Re$_p$, 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 Re$_p$, because this viscous force is much smaller than the inertial lift force at large Re$_p$. *These authors contributed equally.