用流模型监测无人机偏离训练环境程度，自动切换安全控制器。
Improving the Resilience of Quadrotors in Underground Environments by Combining Learning-based and Safety Controllers
- 用归一化流建模环境分布，实时判断无人机是否偏离训练场景。
- 在真实洞穴数据集上测试，任务完成速度快且零碰撞。
- 适合需要高安全性与自主性的地下无人机应用。
在大型地下环境中自主控制四旋翼无人机适用于环境勘测、采矿作业和搜救等场景。基于学习的控制器虽具吸引力，但对训练外的未知环境泛化能力差。本文训练一种基于归一化流的环境先验模型，可实时衡量无人机当前所处环境与训练分布的偏离程度。该度量作为运行时监控器，当偏离程度超过阈值时，自动切换至安全控制器。我们在基于DARPA地下挑战赛决赛数据的真实点云构建的三维洞穴仿真环境中，对点对点导航任务进行了基准测试。实验结果表明，该联合控制器兼具学习型控制器的高效性（快速完成任务）与安全控制器的鲁棒性（避免碰撞）。
原文摘要 · Abstract (English)
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).
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