
Lingxuan Kong
Doctoral Student
Lingxuan (Leo) Kong is a PhD student supervised by Dr. Alexandra Brintrup in the Manufacturing Analytics Group in Institute of Manufacturing. His current research area is on distributed learning-based control methods for supply chain and multi-agent system.
Lingxuan received his Bachelor of Engineering (Honours) degree from Australian National University. He majored in mechatronics system. His previous research projects during bachelor study mainly focus on the distributed clustering algorithm and graph partitioning techniques. These algorithms can be applied to multi-robot system and data processing problems. He also took an internship at Bernecker & Rainer Industrial. Automation. He designed a learning-based controller which combines the BP neural network learning and pole-placement control methods for the inverted pendulum.
Publications:
L. Kong, Q. Liu and C. Yu, "Range-limited distributed algorithms on higher-order Voronoi partitions in multi-robot systems", in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, 2019
Yuni Zhou, Lingxuan Kong, Stefan Sosnowski, Qingchen Liu, Sandra Hirche" Distributed Event/Self-Triggered Coverage Control with Speed Constrained Unicycle Robots", in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, 2021

- Institute for Manufacturing
- 17 Charles Babbage Road
- Cambridge CB3 0FS
Research
- Artificial Intelligence
- Asset Management
- Business Model Innovation
- Complex Additive Materials
- Decision-Making for Emerging Technologies
- Design Management
- Digital Manufacturing
- Distributed Information & Automation Laboratory
- Fluids in Advanced Manufacturing
- Healthcare
- Industrial Photonics
- Industrial Resilience
- Industrial Sustainability
- Inkjet Research
- Innovation and Intellectual Property
- International Manufacturing
- Manufacturing Industry Education Research
- NanoManufacturing
- Science, Technology & Innovation Policy
- Strategy and Performance
- Technology Enterprise
- Technology Management
- Service Alliance
- University Commercialisation and Innovation Policy Evidence Unit