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

Authors

Arun Lakshmanan, Aditya Gahlawat, Naira Hovakimyan

Year

2020

Preprint

Algorithm

CL1

Source

arXiv

Identifiers

arXiv 2004.01142

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 of. By leveraging recent results in contraction analysis and L_1-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 examples how contraction theory-based L_1-adaptive control can be used in conjunction with traditional motion planning algorithms to obtain provably safe trajectories.