Yu-Ting Chen
Papers
1
Total Citations
2
H-Index
1
About
Yu-Ting Chen is a researcher at the forefront of intelligent robotics and autonomous systems, with a core focus on integrating artificial intelligence into industrial automation. Their most-cited work, "Q-learning based Tracking Control and Slope Climbing Strategy Design of Autonomous Mobile Robot and Flatbed Vehicle" (2021, 2 citations), represents a key contribution to the field of Industry 4.0. In this study, Chen leverages reinforcement learning—specifically Q-learning—to enhance the tracking control and slope-climbing capabilities of autonomous mobile robots and flatbed vehicles, addressing critical challenges in dynamic, real-world manufacturing environments. This work bridges the gap between AI-driven decision-making and practical robotic locomotion, offering scalable solutions for smart factories. While their citation count is still growing, Chen’s research is notable for its direct application to the fourth industrial revolution, where artificial intelligence and auto-guided vehicles are central technologies. Their approach demonstrates a commitment to solving tangible engineering problems, making their work valuable for students and researchers exploring the intersection of machine learning, control systems, and autonomous navigation. As the demand for intelligent automation rises, Chen’s contributions are poised to have increasing impact on both academic research and industrial practice.
Research Focus
Key Achievements
Top Papers
- 1