Optimal navigation of microswimmers in complex and noisy environments

Abstract

We design new navigation strategies for travel time optimization of microscopic self-propelled particles in complex and noisy environments. In contrast to strategies relying on the results of optimal control theory, these protocols allow for semi-autonomous navigation as they do not require control over the microswimmer motion via external feedback loops. Although the strategies we propose rely on simple principles, they show arrival time statistics strikingly similar to those obtained from stochastic optimal control theory, as well as performances that are robust to environmental changes and strong fluctuations. These features, as well as their applicability to more general optimization problems, make these strategies promising candidates for the realization of optimized semi-autonomous navigation.

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• Fractional and scaled Brownian motion on the sphere: The effects of long-time correlations on navigation strategies
• Kinetic theory of decentralized learning for smart active matter
• Optimal navigation in two-dimensional flows: Control theory and reinforcement learning