WESPR: 風に適応するエネルギー効率の良い安全な知覚・計画でクアッドロータの堅牢な飛行を実現
WESPR: Wind-adaptive Energy-Efficient Safe Perception & Planning for Robust Flight with Quadrotors
環境形状が局所風に与える影響を高速に予測し、10秒以内に風を考慮した経路計画と制御適応を行うフレームワークを提案。Crazyflieで乱流障害物コースを飛行し、風を考慮しない適応制御と比べ最大軌道偏差を12.5～58.7%削減、安定性を24.6%向上させた。
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？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Khuzema Habib, Pranav Deshakulkarni Manjunath, Kasra Torshizi, Troi Williams, Pratap Tokekar
分類: cs.RO
原文アブストラクト
Local wind conditions strongly influence drone performance: headwinds increase flight time, crosswinds and wind shear hinder agility in cluttered spaces, while tailwinds reduce travel time. Although adaptive controllers can mitigate turbulence, they remain unaware of the surrounding geometry that generates it, preventing proactive avoidance. Existing methods that model how wind interacts with the environment typically rely on computationally expensive fluid dynamics simulations, limiting real-time adaptation to new environments and conditions. To bridge this gap, we present WESPR, a fast framework that predicts how environmental geometry affects local wind conditions, enabling proactive path planning and control adaptation. Our lightweight pipeline integrates geometric perception and local weather data to estimate wind fields, compute cost-efficient paths, and adjust control strategies-all within 10 seconds. We validate WESPR on a Crazyflie drone navigating turbulent obstacle courses. Our results show a 12.5-58.7% reduction in maximum trajectory deviation and a 24.6% improvement in stability compared to a wind-agnostic adaptive controller.
