学習ベース制御と安全制御の組み合わせによる地下環境でのクアッドロータのレジリエンス向上
Improving the Resilience of Quadrotors in Underground Environments by Combining Learning-based and Safety Controllers
環境の分布外度を測る正規化フロー事前分布を学習し、実行時に学習ベース制御と安全制御を切り替えることで、地下洞窟でのナビゲーションの迅速性と安全性を両立させた。
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？
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著者: Isaac Ronald Ward, Mark Paral, Kristopher Riordan, Mykel J. Kochenderfer
分類: cs.RO, cs.AI, cs.SY, eess.SY
原文アブストラクト
Autonomously controlling quadrotors in large-scale subterranean environments is applicable to many areas such as environmental surveying, mining operations, and search and rescue. Learning-based controllers represent an appealing approach to autonomy, but are known to not generalize well to `out-of-distribution' environments not encountered during training. In this work, we train a normalizing flow-based prior over the environment, which provides a measure of how far out-of-distribution the quadrotor is at any given time. We use this measure as a runtime monitor, allowing us to switch between a learning-based controller and a safe controller when we are sufficiently out-of-distribution. Our methods are benchmarked on a point-to-point navigation task in a simulated 3D cave environment based on real-world point cloud data from the DARPA Subterranean Challenge Final Event Dataset. Our experimental results show that our combined controller simultaneously possesses the liveness of the learning-based controller (completing the task quickly) and the safety of the safety controller (avoiding collision).
