Robust Safe Learning and Control in an Unknown Environment: An Uncertainty-Separated Control Barrier Function Approach-Reference-Cited by-同舟云学术

Robust Safe Learning and Control in an Unknown Environment: An Uncertainty-Separated Control Barrier Function Approach

Published:2023-10 Issue:10 Volume:8 Page:6539-6546
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Li Jiacheng¹^ORCID,Liu Qingchen¹^ORCID,Jin Wanxin²^ORCID,Qin Jiahu¹^ORCID,Hirche Sandra³^ORCID

Affiliation:

1. Department of Automation, University of Science and Technology of China, Hefei, China

2. School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, USA

3. Chair of Information-oriented Control, Department of Electrical and Computer Engineering, Technical University of Munich, Munich, Germany

Funder

Science and Technology Major Project of Anhui Province

Mobility Programme 2021 of Sino-German Center for Research Promotion

Fundamental Research Funds for the Central Universities

European Research Council

Next Generation Human Centered Robotics

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Computer Science Applications,Computer Vision and Pattern Recognition,Mechanical Engineering,Human-Computer Interaction,Biomedical Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/7083369/10220574/10232876.pdf?arnumber=10232876

Reference23 articles.

1. Gaussian Control Barrier Functions: Safe Learning and Control

2. Uncertain-aware safe exploratory planning using Gaussian process and neural control contraction metric;sun;Proc Learn Dyn Control,0

3. Safe Teleoperation of Dynamic UAVs Through Control Barrier Functions

4. An Explicit Description of the Reproducing Kernel Hilbert Spaces of Gaussian RBF Kernels

5. Control Barrier Function Based Quadratic Programs for Safety Critical Systems

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