Learning Barrier Functions for Constrained Motion Planning with Dynamical Systems

Matteo Saveriano, Dongheui Lee

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

32 Zitate (Scopus)

Abstract

Stable dynamical systems are a flexible tool to plan robotic motions in real-time. In the robotic literature, dynamical system motions are typically planned without considering possible limitations in the robot's workspace. This work presents a novel approach to learn workspace constraints from human demonstrations and to generate motion trajectories for the robot that lie in the constrained workspace. Training data are incrementally clustered into different linear subspaces and used to fit a low dimensional representation of each subspace. By considering the learned constraint subspaces as zeroing barrier functions, we are able to design a control input that keeps the system trajectory within the learned bounds. This control input is effectively combined with the original system dynamics preserving eventual asymptotic properties of the unconstrained system. Simulations and experiments on a real robot show the effectiveness of the proposed approach.

OriginalspracheEnglisch
Titel2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
Seiten112-119
Seitenumfang8
ISBN (elektronisch)9781728140049
DOIs
PublikationsstatusVeröffentlicht - Nov. 2019
Extern publiziertJa
Veranstaltung2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019 - Macau, China
Dauer: 3 Nov. 20198 Nov. 2019

Publikationsreihe

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (elektronisch)2153-0866

Konferenz

Konferenz2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
Land/GebietChina
OrtMacau
Zeitraum3/11/198/11/19

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