C$^2$-Explorer: 連結性を考慮したタスク表現と連続性駆動のタスク割り当てによる分散型マルチUAV探査
C$^2$-Explorer: Contiguity-Driven Task Allocation with Connectivity-Aware Task Representation for Decentralized Multi-UAV Exploration
通信制約下でのマルチUAV探査効率を高めるため、未知領域を連結グラフで分割し、非隣接割り当てを抑制する連続性重視のタスク割り当て手法を提案した。シミュレーションで探査時間を43.1%、経路長を33.3%削減し、実機でも有効性を確認した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Xinlu Yan, Mingjie Zhang, Yuhao Fang, Yanke Sun, Jun Ma, Youmin Gong, Boyu Zhou, Jie Mei
分類: cs.RO
原文アブストラクト
Efficient multi-UAV exploration under limited communication is severely bottlenecked by inadequate task representation and allocation. Previous task representations either impose heavy communication requirements for coordination or lack the flexibility to handle complex environments, often leading to inefficient traversal. Furthermore, short-horizon allocation strategies neglect spatiotemporal contiguity, causing non-contiguous assignments and frequent cross-region detours. To address this, we propose C$^2$-Explorer, a decentralized framework that constructs a connectivity graph to decompose disconnected unknown components into independent task units. We then introduce a contiguity-driven allocation formulation with a graph-based neighborhood penalty to discourage non-adjacent assignments, promoting more contiguous task sequences over time. Extensive simulation experiments show that C$^2$-Explorer consistently outperforms state-of-the-art (SOTA) baselines, reducing average exploration time by 43.1\% and path length by 33.3\%. Real-world flights further demonstrate the system's feasibility. The code will be released at https://github.com/Robotics-STAR-Lab/C2-Explorer
