Resetting mediated navigation of active Brownian searcher in a homogeneous topography

TL;DR

This paper investigates how resetting strategies influence the search efficiency of active Brownian particles in noisy, confined environments, revealing that resetting can significantly improve search times, especially when the reset points fluctuate.

Contribution

It introduces a novel analysis of resetting protocols for active Brownian searchers, highlighting their impact on search time statistics and demonstrating universal benefits across various systems.

Findings

Resetting protocols alter search time distributions.

Annealed resetting consistently speeds up searches.

Resetting strategies are broadly applicable to optimization problems.

Abstract

Designing navigation strategies for search time optimization remains of interest in various interdisciplinary branches in science. In here, we focus on microscopic self-propelled searchers namely active Brownian walkers in noisy and confined environment which are mediated by one such autonomous strategy namely resetting. As such, resetting stops the motion and compels the walkers to restart from the initial configuration intermittently according to an external timer that does not require control by the walkers. In particular, the resetting coordinates are either quenched (fixed) or annealed (fluctuating) over the entire topography. Although the strategy relies upon simple rules, it shows a significant ramification on the search time statistics in contrast to the original search. We show that the resetting driven protocols mitigate the performance of these active searchers based, robustly, on the inherent search time fluctuations. Notably, for the annealed condition, resetting is always found to expedite the search process. These features, as well as their applicability to more general optimization problems starting from queuing systems, computer science to living systems, make resetting based strategies universally promising.