Abstract:
Safe real-time navigation is a considerable challenge because engineers often need to work with uncertain vehicle dynamics, variable external disturbances, and imperfect controllers. A common strategy used to address safety is to employ hand-defined margins for obstacle inflation. However, arbitrary static margins often fail in more dynamic scenarios, and using worst-case assumptions proves to be overly conservative in most real-world settings where disturbance varies.

In this work, we propose a middle ground: safety margins that adapt on-the-fly using online measurements. In an offline phase, we use Monte Carlo simulations to pre-compute a library of safety margins for multiple levels of disturbance uncertainty. At runtime, our system estimates the current disturbance level and queries the associated safety margins for real-time replanning that present the best trade-off between safety and performance. We validate using extensive simulated and real-world flight tests that our approach better balances safety and performance than baseline static margin methods. More results can be found in our supplementary video: https://youtu.be/SHzKHSUjdUU

Committee:
Sebastian Scherer (Advisor)
Oliver Kroemer
Changliu Liu
Alex Spitzer