Yanfeng Lu

Korea University, Shandong Institute of Automation

Papers

Total Citations

H-Index

About

Yanfeng Lu is a robotics researcher specializing in 3D vision, humanoid locomotion, and human-robot interaction. His work focuses on enabling robots to perceive and navigate dynamic environments through advanced visual processing and motion prediction. In his highly cited 2012 paper, "3D vision based local obstacle avoidance method for humanoid robot," Lu developed a system that uses SURF features to create a panorama environment map, allowing humanoid robots to autonomously determine avoidance directions and walking motions—a foundational contribution to real-time robotic navigation. His 2019 study, "Human Motion Prediction Based on Visual Tracking," extends this work into predictive modeling, integrating visual tracking with neural networks to anticipate human movements, which is critical for safe and responsive robot behavior in shared spaces. With over 11 citations across his key publications, Lu’s research bridges computer vision and robotics, offering practical solutions for obstacle avoidance and motion planning. His achievements include designing robust visual tracking systems that enhance robot autonomy, making his work valuable for students and researchers exploring embodied AI, humanoid robotics, and vision-based control systems.