Resetting mediated navigation of active Brownian searcher in a homogeneous topography

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.

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Cited by in corpus (11)

• Thermodynamic work of partial resetting
• Kuramoto model subject to subsystem resetting: How resetting a part of the system may synchronize the whole of it
• Chiral run-and-tumble walker: transport and optimizing search
• Search with stochastic home-returns can expedite classical first passage under resetting
• Queues with resetting: a perspective
• Target Search Optimization by Threshold Resetting
• Random resetting in search problems
• Crossover from Anomalous to Normal Diffusion: Ising Model with Stochastic Resetting
• Active Brownian particle under stochastic position and orientation resetting in a harmonic trap
• Optimal switching strategies for navigation in stochastic settings
• Speeding up Brownian escape via intermediate finite potential barriers