Papers
8
Total Citations
215
H-Index
6
About
Hongye Su is a leading researcher in control theory and robotics, whose work bridges the gap between theoretical safety guarantees and real-world autonomous navigation. His primary research areas include nonlinear control systems, multi-robot coordination, and intelligent collision avoidance. Su’s most impactful contribution is his pioneering work on network-based fuzzy control for nonlinear Markov jump systems, which has garnered over 115 citations and established foundational methods for handling quantization and data dropout in networked control. In robotics, he has made significant advances in safe navigation, developing novel approaches that combine velocity obstacles with control barrier functions (CBFs) for dynamic collision avoidance in multi-robot systems. His 2023 paper on polytopic collision avoidance for distributed robots (36 citations) addresses critical scalability challenges, while his work on time-varying CBFs for unicycle-modeled robots enables both linear and angular velocity control. Su has also contributed to applied AI, developing deep learning methods for cotton disease detection (25 citations) and deep reinforcement learning for autonomous driving. His recent 2025 paper on velocity obstacle-based CBFs for acceleration-controlled robots represents a major step forward in safety-critical control design. With over 200 total citations and growing influence, Su’s research continues to shape the future of autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 3Cotton Disease Detection Based on ConvNeXt and Attention Mechanisms25 citations · 2022
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