Hanjing Ye

Guangdong University of Technology, Southern University of Science and Technology

Papers

Total Citations

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About

Hanjing Ye is a robotics and computer vision researcher whose work sits at the intersection of autonomous navigation, human perception, and task planning for intelligent robotic systems. Their most recognized contribution, "Mapping While Following: 2D LiDAR SLAM in Indoor Dynamic Environments with a Person Tracker" (2021, 10 citations), advances the field of simultaneous localization and mapping by addressing a critical real-world challenge — enabling robots to build accurate maps in dynamic indoor spaces populated by moving people, moving beyond the limitations of traditional static-environment approaches. Complementing this, their 2024 work on "Human Orientation Estimation Under Partial Observation" (4 citations) tackles the nuanced problem of inferring human intent from incomplete visual data, a capability essential for safe and socially aware autonomous agents. Their most recent contribution, "FlowPlan" (2025), pushes toward more generalizable robotic intelligence by introducing a zero-shot task planning framework leveraging large language model flow engineering, reducing dependence on task-specific training data. Together, Ye's research portfolio reflects a coherent vision: building robots that can perceive, understand, and interact with humans in complex, unstructured environments — a challenge central to the future of human-robot collaboration.