Papers

29

Total Citations

493

H-Index

12

About

Liang Yan is a multidisciplinary robotics and engineering researcher whose work spans autonomous navigation, human-robot collaboration, medical robotics, and actuator design. Over the course of his career, he has made significant contributions to intelligent robotic systems, most notably advancing deep reinforcement learning techniques for mobile robot navigation — his improved Deep Deterministic Policy Gradient framework for dynamic obstacle avoidance has garnered 66 citations, reflecting its practical impact on autonomous systems research. Yan's work on human-robot collaboration is equally notable, with hybrid recurrent neural network architectures and deep LSTM-based intention recognition systems collectively drawing over 70 citations, pushing the boundaries of intuitive human-machine interaction. Beyond software and control, Yan has demonstrated versatility in hardware innovation, developing a wireless-powered capsule robot for obesity treatment, a compact piezoelectric traveling wave micromotor, and a three-degree-of-freedom optical orientation measurement system for spherical actuators. His research portfolio also includes robust trajectory tracking control for wheeled mobile robots, magnetic wall-climbing robot design, and permanent-magnet machine topology optimization. With publications spanning over 15 years and a combined citation count exceeding 360, Yan's work bridges theoretical rigor and real-world engineering application, making him a compelling figure for students and researchers in robotics, mechatronics, and intelligent systems alike.

Research Focus

Key Achievements

12
H-Index
29
Papers
493
Total Citations
17
Avg Citations/Paper
🏆 Most Cited Paper
Improved Deep Deterministic Policy Gradient for Dynamic Obstacle Avoidance of Mobile Robot
66 citations · 2023
📈 Most Prolific Year: 2023 (6 Papers)
🤝 Key Collaborators: 49
🏛 Institutions: Ningbo University of Technology, Beihang University, Ningbo University, Nanyang Technological University

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago