多机器人协同探索雨林，用蚂蚁启发的虚拟信息素减少重复覆盖。
RANT: Ant-Inspired Multi-Robot Rainforest Exploration Using Particle Filter Localisation and Virtual Pheromone Coordination
- 模仿蚂蚁觅食，用虚拟信息素避免重复探测。
- 团队规模越大覆盖越广，但干扰导致收益递减。
- 粒子滤波定位确保热点区域被准确发现。
本文提出RANT，一种面向噪声与不确定性环境的多机器人探索框架。由差速驱动机器人组成团队，在10×10米区域内探测隐藏的丰富度场，收集带有噪声的探针数据，并构建局部概率地图，同时由监督器维护全局评估。RANT融合粒子滤波定位、基于梯度的热点挖掘行为控制器，以及轻量级无重复探测协调机制（虚拟信息素阻断）。实验分析了团队规模、定位精度和协调策略对覆盖率、热点召回率及冗余度的影响。结果表明：粒子滤波对热点精准定位至关重要；协调机制显著降低重复探测；增加团队规模可提升覆盖率，但因相互干扰出现边际效益递减。
原文摘要 · Abstract (English)
This paper presents RANT, an ant-inspired multi-robot exploration framework for noisy, uncertain environments. A team of differential-drive robots navigates a 10 x 10 m terrain, collects noisy probe measurements of a hidden richness field, and builds local probabilistic maps while the supervisor maintains a global evaluation. RANT combines particle-filter localisation, a behaviour-based controller with gradient-driven hotspot exploitation, and a lightweight no-revisit coordination mechanism based on virtual pheromone blocking. We experimentally analyse how team size, localisation fidelity, and coordination influence coverage, hotspot recall, and redundancy. Results show that particle filtering is essential for reliable hotspot engagement, coordination substantially reduces overlap, and increasing team size improves coverage but yields diminishing returns due to interference.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。