RANT: アリに着想を得た粒子フィルタ定位と仮想フェロモン調整による多ロボット熱帯雨林探索
RANT: Ant-Inspired Multi-Robot Rainforest Exploration Using Particle Filter Localisation and Virtual Pheromone Coordination
ノイズの多い未知環境で、粒子フィルタ定位と仮想フェロモンによる再訪防止調整を用いて複数ロボットが協調探索するフレームワークを提案し、チームサイズや定位精度が被覆率・ホットスポット再現率に与える影響を実験的に解析した。
著者: Ameer Alhashemi, Layan Abdulhadi, Karam Abuodeh, Tala Baghdadi, Suryanarayana Datla
分類: cs.RO
原文アブストラクト
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.