Learning Large-scale Multi-agent Control with Safety Certificates

Multi-agent intelligence in autonomous systems has been fascinating roboticists for decades. The recent advances in machine learning has created unprecedented opportunities for achieving ultimate multi-agent intelligence and full autonomy in a data-driven way. However, a fundamental bottleneck of machine learning-based methods is their safety and reliability in controlling the autonomous system at large scale, due to the lack of formal safety guarantee. In addressing these challenges, we develop: (1) An machine learning-based large-scale multi-agent control framework with safety certificates, which simultaneously enjoys the versatility of machine learning and the assurance of safety. (2) A multi-agent trajectory tracking framework with convergence and safety guarantees. (3) A general method to learn safe controllers for black-box systems with unknown dynamics. Comprehensive experiments have shown that the proposed methods have notable performance in terms of safety rate, task completion rate, computational efficiency and large-scale scalability.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Aeronautics and Astronautics

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Qin, Zengyi

Advisor dc:contributor.advisor

• How, Jonathan P.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/147477

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/147477