Jinglun Yu
Papers
2
Total Citations
132
H-Index
1
About
Jinglun Yu is a pioneering researcher at the intersection of intelligent robotics and advanced optical metrology, whose work bridges autonomous navigation and biomedical imaging. Yu’s primary research areas include mobile robot path planning, hierarchical reinforcement learning, and deep-learning-driven profilometry. In their highly cited 2020 work, “The Path Planning of Mobile Robot by Neural Networks and Hierarchical Reinforcement Learning” (131 citations), Yu addressed critical limitations in autonomous robotics—specifically, slow convergence, non-smooth trajectories, and lack of self-learning capability—by integrating neural networks with hierarchical reinforcement learning, enabling robots to perceive and adapt to complex environments in real time. This contribution has significantly advanced the field of autonomous navigation, providing a foundation for more intelligent and efficient mobile systems. More recently, Yu has ventured into biomedical optics with their 2025 study on “Deep-learning-based endoscopic single-shot fringe projection profilometry,” which tackles the challenge of slow acquisition times in conventional fringe projection profilometry. By developing a single-shot, endoscope-compatible system, Yu has opened new possibilities for high-speed, dynamic 3D measurements in minimally invasive surgery. With a growing citation impact and a portfolio that spans from robotics to medical imaging, Jinglun Yu exemplifies interdisciplinary innovation, driving practical solutions for real-world automation and healthcare challenges.
Research Focus
Key Achievements
Top Papers
- 1
- 2