@inproceedings{7d8f1996608b4018a1164bb09b9a3d83,
title = "Safe Feedback Motion Planning: A Contraction Theory and L1-Adaptive Control Based Approach",
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.",
keywords = "L1-adaptive control, contraction theory, control contraction metrics, feedback motion planning, nonlinear references, robust adaptive control, robust trajectory tracking",
author = "Arun Lakshmanan and Aditya Gahlawat and Naira Hovakimyan",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 59th IEEE Conference on Decision and Control, CDC 2020 ; Conference date: 14-12-2020 Through 18-12-2020",
year = "2020",
month = dec,
day = "14",
doi = "10.1109/CDC42340.2020.9303957",
language = "English (US)",
series = "Proceedings of the IEEE Conference on Decision and Control",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1578--1583",
booktitle = "2020 59th IEEE Conference on Decision and Control, CDC 2020",
address = "United States",
}