Safe Feedback Motion Planning: A Contraction Theory and L1-Adaptive Control Based Approach

Arun Lakshmanan, Aditya Gahlawat, Naira Hovakimyan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Autonomous robots that are capable of operating safely in the presence of imperfect model knowledge or external disturbances are vital in safety-critical applications. In this paper, we present a planner-agnostic framework to design and certify safe tubes around desired trajectories that the robot is always guaranteed to remain inside. By leveraging recent results in contraction analysis and L1-adaptive control we synthesize an architecture that induces safe tubes for nonlinear systems with state and time-varying uncertainties. We demonstrate with a few illustrative examples1 how contraction theory-based L1-adaptive control can be used in conjunction with traditional motion planning algorithms to obtain provably safe trajectories.

Original languageEnglish (US)
Title of host publication2020 59th IEEE Conference on Decision and Control, CDC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1578-1583
Number of pages6
ISBN (Electronic)9781728174471
DOIs
StatePublished - Dec 14 2020
Event59th IEEE Conference on Decision and Control, CDC 2020 - Virtual, Jeju Island, Korea, Republic of
Duration: Dec 14 2020Dec 18 2020

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume2020-December
ISSN (Print)0743-1546

Conference

Conference59th IEEE Conference on Decision and Control, CDC 2020
CountryKorea, Republic of
CityVirtual, Jeju Island
Period12/14/2012/18/20

Keywords

  • L1-adaptive control
  • contraction theory
  • control contraction metrics
  • feedback motion planning
  • nonlinear references
  • robust adaptive control
  • robust trajectory tracking

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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