Optimal area exploration by resetting active particles

Abstract

Identifying optimal strategies for efficient spatial exploration is crucial, both for animals seeking food and for robotic search processes, where maximizing the covered area is a fundamental requirement. Here, we propose position resetting as an optimal protocol to enhance spatial exploration in active matter systems. Specifically, we show that the area covered by an active Brownian particle exhibits a non-monotonic dependence on the resetting rate, demonstrating that resetting can optimize spatial exploration. Our results are based on experiments with active granular particles undergoing Poissonian resetting and are supported by active Brownian dynamics simulations. The covered area is analytically predicted at both large and small resetting rates, resulting in a scaling relation between the optimal resetting rate and the self-propulsion speed.