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arXiv · 2509.02808

Improving the Resilience of Quadrotors in Underground Environments by Combining Learning-based and Safety Controllers

Isaac Ronald Ward·Mark Paral·Kristopher Riordan·Mykel J. Kochenderfer
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Abstract

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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Isaac Ronald Ward, Mark Paral, Kristopher Riordan, Mykel J. Kochenderfer. 2025-09-02. Improving the Resilience of Quadrotors in Underground Environments by Combining Learning-based and Safety Controllers. https://doi.org/10.1109/codit66093.2025.11321771

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