Publication Date

1-1-2025

Document Type

Article

Publication Title

Frontiers in Robotics and AI

Volume

12

DOI

10.3389/frobt.2025.1492526

Abstract

In the realm of real-time environmental monitoring and hazard detection, multi-robot systems present a promising solution for exploring and mapping dynamic fields, particularly in scenarios where human intervention poses safety risks. This research introduces a strategy for path planning and control of a group of mobile sensing robots to efficiently explore and reconstruct a dynamic field consisting of multiple non-overlapping diffusion sources. Our approach integrates a reinforcement learning-based path planning algorithm to guide the multi-robot formation in identifying diffusion sources, with a clustering-based method for destination selection once a new source is detected, to enhance coverage and accelerate exploration in unknown environments. Simulation results and real-world laboratory experiments demonstrate the effectiveness of our approach in exploring and reconstructing dynamic fields. This study advances the field of multi-robot systems in environmental monitoring and has practical implications for rescue missions and field explorations.

Funding Number

CMMI-1917300

Funding Sponsor

National Science Foundation

Keywords

dynamic field reconstruction, environmental monitoring, mobile sensor networks, multi-robot systems, reinforcement learning, source seeking

Comments

© 2025 Lu, Sobti, Talwar and Wu

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Department

Computer Engineering

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