Adaptive Tube Library for Safe Online Planning under Unknown Tracking PerformanceDownload PDF

Jun 15, 2020 (edited Jul 09, 2020)RSS 2020 Workshop Robust Autonomy Blind SubmissionReaders: Everyone
  • 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.
  • TL;DR: We adapt safety margins on-the-fly to safely capture unknown varying dynamics without overly sacrificing performance.
  • Keywords: Safe Autonomy, UAV, Planning and Control, Identification and Control
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