Papers
4
Total Citations
11
H-Index
2
About
Yunlong Teng is a researcher advancing the intersection of robotics, autonomous navigation, and reliability engineering. His work primarily focuses on pedestrian trajectory prediction, collaborative robotic control, and precise localization for mobile systems. Teng’s most cited paper (2024, 4 citations) introduces a novel pedestrian trajectory prediction method using generative adversarial networks with scene constraints, directly addressing a critical challenge for unmanned driving and intelligent robots. He has also developed a visual-admittance-based model predictive control scheme for nuclear collaborative robots, tackling the complex problem of vision/force control under operational constraints (2023, 2 citations). In the domain of mobile robot localization, Teng proposed an improved genetic algorithm for satellite selection in multi-GNSS positioning (2024, 2 citations), enhancing accuracy for real-world applications. Additionally, his work on imprecise reliability analysis using the generalized inverse Weibull distribution (2019, 3 citations) provides robust methods for assessing robotic component lifetimes from limited data. Across these contributions, Teng demonstrates a commitment to solving practical, safety-critical problems in autonomous systems, from pedestrian-aware navigation to nuclear environment operations, establishing himself as a versatile researcher in intelligent robotics and reliability analysis.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4