Yufeng Tian
Papers
2
Total Citations
12
H-Index
1
About
Yufeng Tian is a rising researcher in multi-robot systems and distributed optimization, with a focus on autonomous coordination in unknown and adversarial environments. His work bridges control theory and practical robotics, particularly addressing how teams of agents can efficiently search for targets without prior environmental knowledge. In his highly cited 2023 paper, “Multirobot Target Searches in Unknown Environments Via Waypoint Planning System” (11 citations), Tian developed a waypoint-based planning framework that enables multiple robots to systematically locate information-deficient targets—a critical capability for applications like hazardous area rescue, environmental monitoring, and leak source detection. More recently, in “Momentum-Based Distributed Disturbance Feedback Optimization of Heterogeneous Multiagent Systems” (2025), he tackled the challenging problem of driving heterogeneous agents with external disturbances toward the optimal solution of a global nonconvex objective function. By introducing a momentum-based, timescale-separation approach, Tian advanced the theory of distributed feedback optimization under real-world constraints. His work demonstrates a clear trajectory from practical multirobot coordination to theoretically rigorous distributed control, earning early recognition and laying a strong foundation for future contributions to autonomous multi-agent systems.
Research Focus
Key Achievements
Top Papers
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- 2