Yijian Tan

Wuhan University of Science and Technology

Papers

Total Citations

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About

Yijian Tan is a researcher advancing the frontier of autonomous mobile robotics through intelligent path planning algorithms. His primary research areas lie at the intersection of reinforcement learning, deep neural networks, and dynamic obstacle avoidance for mobile robots. Tan’s most influential work introduces an improved Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, which integrates Prioritized Experience Replay (PER) and Long Short-Term Memory (LSTM) networks to overcome critical limitations in sample efficiency and temporal dependency handling. His 2025 paper on this method has already garnered 13 citations, reflecting its timely impact. Earlier, in 2022, Tan’s PL-TD3 algorithm laid the groundwork by demonstrating how PER and LSTM could accelerate convergence and improve perception of dynamic obstacles—a contribution that earned 7 citations. Together, these works form a cohesive body of research that directly addresses the real-world challenge of navigating unpredictable environments. Tan’s innovations are particularly notable for their practical applicability, offering a pathway toward more reliable and efficient autonomous systems. His work is essential reading for students and researchers interested in the next generation of intelligent, adaptive robotics.