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ORIGINAL RESEARCH article

Front. Plant Sci.

Sec. Sustainable and Intelligent Phytoprotection

Volume 16 - 2025 | doi: 10.3389/fpls.2025.1699124

This article is part of the Research TopicAI-Driven Plant Intelligence: Bridging Multimodal Sensing, Adaptive Learning, and Ecological Sustainability in Precision Plant ProtectionView all 5 articles

Distributed Multi-Robot Active Gathering for Non-Uniform Agriculture and Forestry Information

Provisionally accepted
  • 1Wenzhou Vocational College of Science and Technology, Wenzhou, China
  • 2Wenzhou Key Laboratory of Al Agents for Agriculture, Wenzhou, China
  • 3Nanjing Normal University, Nanjing, China
  • 4Temple University, Philadelphia, United States

The final, formatted version of the article will be published soon.

Active information gathering is a fundamental task in multi-robot systems in agriculture, with applications in precision planting and sowing, field management and inspection, intelligent weeding and pest control, etc. Traditional distributed strategies often struggle to adapt to environments where information of interest are unevenly clustered, leading to slow detection and inefficient coverage. In this paper, we reformulate the information gathering problem as a multi-armed bandit (MAB) problem and propose a novel distributed Bernoulli Thompson Sampling algorithm. Our approach enables robots to make exploration-exploitation decisions while sharing probabilistic information across the team, thus improving global coordination without centralized control. We further combine the distributed Bernoulli Thompson Sampling policy with Lloyd's algorithm for dynamic target tracking and introduce a goal swapping strategy to improve task allocation efficiency. Extensive simulations demonstrate that our method significantly outperforms baseline approaches in terms of search speed and target coverage, particularly in scenarios with clustered target distributions.

Keywords: Multi-Robot Systems, Active information gathering, Thompson sampling, Multi-target tracking, distributed control

Received: 04 Sep 2025; Accepted: 05 Oct 2025.

Copyright: © 2025 Chen, Chen, Mao, Xie and Dames. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Jun Chen, jun.chen@nnu.edu.cn

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