Abstract

To certify UAV operations in populated areas, risk mitigation strategies -- such as Emergency Landing (EL) -- must be in place to account for potential failures. EL aims at reducing ground risk by finding safe landing areas using on-board sensors. The first contribution of this paper is to present a new EL approach, in line with safety requirements introduced in recent research. In particular, the proposed EL pipeline includes mechanisms to monitor learning based components during execution. This way, another contribution is to study the behavior of Machine Learning Runtime Monitoring (MLRM) approaches within the context of a real-world critical system. A new evaluation methodology is introduced, and applied to assess the practical safety benefits of three MLRM mechanisms. The proposed approach is compared to a default mitigation strategy (open a parachute when a failure is detected), and demonstrates a much safer behavior.

Supplementary material

• Details about computing an estimate of the ground size for a given pixel: here.
• A notebook to compute the safety coefficient for safety radius: here.
• Full results: here.

Code

The code used to generate our results: here.

Contact

Joris GUERIN
Laboratoire d’Analyse et d’Architecture des Systèmes (LAAS-CNRS)
7 avenue du Colonel Roche, 31031 TOULOUSE, FRANCE
jorisguerin.research@gmail.com

Citing this work

If you find this research useful, please consider citing

@inproceedings{guerin2022icra,
title={Evaluation of Runtime Monitoring for UAV Emergency Landing},
author={Guerin, Joris and Delmas, Kevin and Guiochet, Jérémie},
booktitle={2022 IEEE International Conference on Robotics and Automation (ICRA)},
pages={to appear},
year={2022},
organization={IEEE}
}