  Safe navigation in real-time is challenging because engineers need to work
with uncertain vehicle dynamics, variable external disturbances, and imperfect
controllers. A common safety strategy is to inflate obstacles by hand-defined
margins. However, arbitrary static margins often fail in more dynamic
scenarios, and using worst-case assumptions is overly conservative for most
settings where disturbances over time. In this work, we propose a middle
ground: safety margins that adapt on-the-fly. In an offline phase, we use Monte
Carlo simulations to pre-compute a library of safety margins for multiple
levels of disturbance uncertainties. Then, at runtime, our system estimates the
current disturbance level to query the associated safety margins that best
trades off safety and performance. We validate our approach with extensive
simulated and real-world flight tests. We show that our adaptive method
significantly outperforms static margins, allowing the vehicle to operate up to
1.5 times faster than worst-case static margins while maintaining safety.
Video: https://youtu.be/SHzKHSUjdUU
