Safe Robot Trajectory Control Using Probabilistic Movement Primitives and Control Barrier Functions

Davoodi, Mohammadreza and Iqbal, Asif and Cloud, Joseph M. and Beksi, William J. and Gans, Nicholas R. (2022) Safe Robot Trajectory Control Using Probabilistic Movement Primitives and Control Barrier Functions. Frontiers in Robotics and AI, 9. ISSN 2296-9144

[thumbnail of pubmed-zip/versions/1/package-entries/frobt-09-772228/frobt-09-772228.pdf] Text
pubmed-zip/versions/1/package-entries/frobt-09-772228/frobt-09-772228.pdf - Published Version

Download (7MB)

Abstract

In this paper, we present a novel means of control design for probabilistic movement primitives (ProMPs). Our proposed approach makes use of control barrier functions and control Lyapunov functions defined by a ProMP distribution. Thus, a robot may move along a trajectory within the distribution while guaranteeing that the system state never leaves more than a desired distance from the distribution mean. The control employs feedback linearization to handle nonlinearities in the system dynamics and real-time quadratic programming to ensure a solution exists that satisfies all safety constraints while minimizing control effort. Furthermore, we highlight how the proposed method may allow a designer to emphasize certain safety objectives that are more important than the others. A series of simulations and experiments demonstrate the efficacy of our approach and show it can run in real time.

Item Type: Article
Subjects: Eprint Open STM Press > Mathematical Science
Depositing User: Unnamed user with email admin@eprint.openstmpress.com
Date Deposited: 22 Jun 2023 08:51
Last Modified: 10 Nov 2023 05:30
URI: http://library.go4manusub.com/id/eprint/777

Actions (login required)

View Item
View Item