UAV-SwarmAgent: コード生成と視覚言語推論によるスケーラブルなマルチUAV偵察・タスク割当
UAV-SwarmAgent: Scalable Multi-UAV Reconnaissance and Task Allocation via Code Generation and Vision-Language Reasoning
自然言語の1文から地上局エージェントがコードを生成し、視覚言語モデルで目標を地図上に特定してN機のUAVに経路を割り当てる階層型マルチエージェントシステムを提案。255ミッション中244件で事象を発見し、機数増加に対する飛行時間短縮がAmdahl型の法則に従うことを示した。
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: Oleg Sautenkov, Yasheerah Yaqoot, Muhammad Ahsan Mustafa, Grigorii Kalinin, Chih-Yung Wen, Dzmitry Tsetserukou
分類: cs.RO
原文アブストラクト
Supervising a multi-UAV reconnaissance mission usually means plotting waypoints and watching every drone. We present UAV SwarmAgent, a hierarchical multi-agent system in which an operator states the mission in one natural-language sentence and a ground-station agent writes and executes code that turns it into per-UAV routes. The agent grounds inspection targets in an aerial map with a vision-language model, splits them among N UAVs by balancing route length, and dispatches worker agents that verify incidents with a compact onboard decision model. In simulation over 4 incident types and 3 foundation models, the swarm located the incident in 244 of 255 missions (95.7%). A paired scaling study with one to 5 UAVs shows that flight time follows an Amdahl-type law. The second UAV delivers half of the attainable saving and the speedup is bounded at 1.52x. We discuss what this predictability, and the false alarms we observed, imply for an operator who supervises the swarm, and outline the user study needed to test it.