Papers
5
Total Citations
127
H-Index
3
About
Honggui Han is a pioneering researcher at the intersection of artificial intelligence, robotics, and complex systems control. His work spans from foundational neural network architectures to cutting-edge applications in big data and autonomous navigation. Han’s most influential contribution is his 2023 paper "Towards big data driven construction industry," which has garnered 88 citations, establishing him as a key voice in the digital transformation of infrastructure. In robotics, he developed a Q-learning algorithm based on dynamical structure neural networks (2009, 8 citations), enabling robots to navigate unknown environments with adaptive intelligence. More recently, his 2024 work on deep distributional reinforcement learning for real-time local path planning (25 citations) has advanced autonomous vehicle navigation. Han has also made significant theoretical contributions to multiagent systems (MASs), including distributed adaptive Nash equilibrium seeking under time-varying disturbances (2025, 3 citations) and a predefined-time disturbance observer for cooperative control (2025, 3 citations). These works address critical challenges in stability and convergence for MASs operating in uncertain environments. With a career spanning foundational theory to applied AI, Han continues to shape how intelligent systems learn, cooperate, and operate in the real world.
Research Focus
Key Achievements
Top Papers
- 1Towards big data driven construction industry88 citations · 2023
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