Papers

13

Total Citations

258

H-Index

8

About

Brian D. O. Anderson is a towering figure in control theory and multi-agent systems, whose work has fundamentally shaped how robots and autonomous vehicles perceive, navigate, and coordinate. His research centers on target pursuit, localization, and formation control, with a particular emphasis on using minimal sensory data—such as distance measurements and bearing angles—to achieve complex tasks. One of his landmark contributions is the development of adaptive control frameworks for target localization and circumnavigation, enabling non-holonomic robots to pursue and circle a target using only bearing information (55+ citations). He also pioneered cooperative self-localization techniques for mobile agents like UAVs, solving the challenge of positioning without GPS by leveraging inter-agent distances and landmark angles (31 citations). More recently, Anderson has advanced path-following control with guiding vector fields, allowing robots to navigate occluded paths and avoid obstacles while maintaining convergence to a desired trajectory (26+ citations). His work on formation rigidity and distributed optimization has provided the theoretical backbone for maintaining stable multi-robot platoons (14+ citations). With over 66 citations on his adaptive pursuit paper alone, Anderson’s research continues to influence modern robotics, autonomous vehicles, and sensor networks.

Research Focus

Key Achievements

8
H-Index
13
Papers
258
Total Citations
20
Avg Citations/Paper
🏆 Most Cited Paper
Adaptive range‐measurement‐based target pursuit
66 citations · 2012
📈 Most Prolific Year: 2012 (2 Papers)
🤝 Key Collaborators: 16
🏛 Institutions: Australian National University, Data61, National Supercomputing Center in Wuxi

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago