LMP Seminar: Optimal navigation strategies in complex and noisy environments

Finding the fastest path to a desired destination is a vitally important task for microorganisms moving in a fluid flow. We address this problem by designing new navigation strategies that allow for travel time optimization of microscopic self-propelled particles in complex and noisy environments. Inspired by the control maps provided by the stochastic optimal control framework, here we design new simple strategies that allow microscopic self-propelled particles to adapt their motility in response to external stimuli such as light gradients, mimicking the tactic behaviours observed in a number of natural and artificial microswimmers. This is in contrast to strategies relying on the results of optimal control theory or machine learning algorithms and which require control over the microswimmer motion via external feedback loops. Remarkably, even though 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 such as navigation on curved manifolds, make these strategies promising candidates for the realization of optimized semi-autonomous navigation.