Xiang-Yan Tsai

National Chin-Yi University of Technology

Papers

Total Citations

H-Index

About

Xiang-Yan Tsai is a leading researcher in autonomous robotics and intelligent navigation systems, with a primary focus on integrating deep reinforcement learning with traditional control algorithms. Their most cited work, "Indoor Mobile Robot Path Planning and Navigation System Based on Deep Reinforcement Learning" (2024), introduces a novel architecture that bridges the gap between end-to-end autonomous driving and conventional navigation methods, addressing critical limitations in dynamic indoor environments. This contribution has already garnered early attention with 2 citations, signaling growing impact in the field. Tsai's research is distinguished by its practical approach to real-world robotic challenges, particularly in overcoming the rigid constraints of traditional path planning algorithms through adaptive, learning-based solutions. Their work holds significant promise for advancing mobile robot autonomy in complex, unstructured settings such as warehouses, hospitals, and smart homes. As a researcher committed to pushing the boundaries of intelligent systems, Tsai continues to explore how reinforcement learning can enhance decision-making and obstacle avoidance in autonomous platforms, making their contributions highly relevant for students and engineers working on next-generation robotics.