Viet-Anh Le
Papers
9
Total Citations
62
H-Index
6
About
Viet-Anh Le is an emerging researcher whose work spans mobile robotic sensor networks, autonomous navigation, and connected and automated vehicles — fields at the intersection of control theory, machine learning, and multi-agent systems. His most influential contributions center on developing computationally efficient algorithms for adaptive sampling in mobile robotic sensor networks, where he leverages Gaussian processes and the Alternating Direction Method of Multipliers (ADMM) framework to optimize environmental monitoring under real-world resource constraints. These works, collectively drawing over 30 citations, demonstrate both theoretical rigor and practical relevance. Le has also made notable strides in social robot navigation, proposing model predictive control frameworks enhanced by deep learning-based human trajectory prediction — including game-theoretic approaches for coordinating multiple robots in crowded environments — addressing one of robotics' most nuanced challenges. His 2024 survey on small-scale testbeds for connected and automated vehicles and robot swarms, among his most cited works, serves as a valuable reference for researchers designing experimental platforms. Across his portfolio, Le consistently bridges algorithmic innovation with scalable, real-world deployment, establishing himself as a thoughtful contributor to the future of intelligent autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9