Work

Optimal Area Exploration by Resetting Active Particles

Year: 2026

Type: article

Abstract: Identifying optimal strategies for efficient spatial exploration is crucial, both for animals seeking food and for robotic search processes, where maximizing the explored 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 depends nonmonotonically 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 simulations of active Brownian dynamics. The covered area is analytically predicted for both large and small resetting rates, leading to a scaling relation between the optimal resetting rate and the self-propulsion speed.

Source: Physical Review Letters

DOI: 10.1103/sytg-m4ms

Authors: Kristian Stølevik Olsen, Hartmut Löwen, Lorenzo Caprini

Institutions: Heinrich Heine University Düsseldorf, Sapienza University of Rome

Language: English

Topic: Micro and Nano Robotics

Subfield: Condensed Matter Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,750

Funders: Alexander von Humboldt-Stiftung, European Commission, Deutsche Forschungsgemeinschaft