Optimal navigation strategy of active Brownian particles in target-search problems

TL;DR

This paper studies how active Brownian particles, modeled as microswimmers, optimize their search strategies in complex energy landscapes by tuning activity and persistence, revealing robust and counterintuitive exploration behaviors.

Contribution

It demonstrates that active Brownian particles can improve target search efficiency through environmental tuning, showing robustness against landscape complexity unlike passive particles.

Findings

Active tuning enhances search success rate.

Search patterns are robust to landscape changes.

Transition rates are landscape-sensitive.

Abstract

We investigate exploration patterns of a microswimmer, modeled as an active Brownian particle, searching for a target region located in a well of an energy landscape and separated from the initial position of the particle by high barriers. We find that the microswimmer can enhance its success rate in finding the target by tuning its activity and its persistence in response to features of the environment. The target-search patterns of active Brownian particles are counterintuitive and display characteristics robust to changes of the energy landscape. On the contrary, the transition rates and transition-path times are sensitive to the details of the specific energy landscape. In striking contrast to the passive case, the presence of additional local minima does not significantly slow down the active target-search dynamics.