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About

Yadan Zeng is a robotics and automation researcher whose work spans 3D sensing, robotic calibration, and intelligent manipulation systems. Best known for developing an improved calibration method for rotating 2D LiDAR systems — a technique that enables cost-effective 3D environmental mapping widely adopted in robotics applications, earning 48 citations since 2018 — Zeng has consistently focused on bridging the gap between theoretical frameworks and practical deployment in real-world settings. Their early work on measuring the dynamic path accuracy of six-axis industrial robots using optical trackers addressed a critical bottleneck in industrial automation: the persistent challenge of achieving high absolute positioning accuracy in everyday robot operation. More recently, Zeng has turned attention to mobile manipulation, contributing a task-sensing and adaptive control framework for robotic painting on large-scale surfaces — a technically demanding application requiring both precision and environmental adaptability. Across these contributions, a clear thread emerges: making robotic systems more accurate, self-aware, and deployable outside controlled laboratory environments. Zeng's research is particularly valuable for students and engineers working at the intersection of robot perception, calibration, and applied autonomy.