Long Wen
Papers
2
Total Citations
9
H-Index
1
About
Long Wen is an emerging researcher specializing in safety-critical control systems and autonomous mobile robotics, with a particular focus on real-time obstacle avoidance in complex, unstructured environments. His work addresses some of the most pressing challenges in robot navigation, including the development of intelligent frameworks that enable mobile robots to operate safely and efficiently among both static and dynamic obstacles. Wen's most notable contribution, "Online Efficient Safety-Critical Control for Mobile Robots in Unknown Dynamic Multi-Obstacle Environments" (2024), has garnered 8 citations and introduces a LiDAR-based goal-seeking and exploration framework that significantly advances the efficiency of online obstacle avoidance beyond the limitations of traditional dynamic control barrier function approaches. Building on this foundation, his 2025 work introduces a sophisticated dual-filter architecture leveraging RGB-D camera data and dynamic control barrier functions (D-CBFs), further enhancing real-time responsiveness to suddenly appearing and moving obstacles. A defining theme across Wen's research is the integration of modern sensing technologies — LiDAR and RGB-D cameras — with rigorous control-theoretic safety guarantees. His contributions are particularly valuable for researchers and engineers developing robust autonomous systems for real-world deployment in unpredictable environments.
Research Focus
Key Achievements
Top Papers
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- 2