Adaptive Tube Library for Safe Online Planning under Unknown Tracking Performance

Abstract

Robots are increasingly deployed into safety-critical applications. However, safe navigation remains a challenge due to uncertain vehicle dynamics and imperfect controllers. To handle safety, we often inflate obstacles or craft safety tubes around trajectories. Experts hand-tune static safety margins for particular missions, however this is valid only under low dynamics variation. Conversely, one can use worst-case margins by assuming high dynamics range, but overly conservative approaches can lead to no feasible planning solutions. We propose a middle ground: margins that adapt on-the-fly with online measurements. To enable real-time adaptation, our approach precomputes a library of safety tubes at different levels of dynamics uncertainty. Online, our system queries appropriate safety margins based on its estimated dynamics uncertainty for safe real-time planning. Finally, we demonstrate with real flight tests that we can safely capture unknown varying dynamics without overly sacrificing performance, with improvements over baseline static margin methods.

Supplementary Video: https://youtu.be/nrcfQx3rJnw.