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About

Yuanzhe Wang is a robotics researcher whose work spans multi-robot systems, autonomous navigation, and collaborative perception. His research addresses some of the most pressing challenges in mobile robotics, including formation control, semantic mapping, and robust localization in complex environments. Wang's early contributions focused on leader-follower control for nonholonomic mobile robots, developing practical schemes that handle unknown obstacle environments and flexible formation geometries — work that has collectively attracted over 160 citations. His 2019 paper on vision-based flexible formation tracking introduced curvilinear coordinate frameworks that significantly advanced beyond the rigid formations prevalent in prior literature. Expanding into collaborative autonomy, Wang pioneered hierarchical and probabilistic frameworks for multi-robot semantic mapping, bridging the gap between geometric and semantic understanding for cooperative systems. His multilevel sensor fusion work for heterogeneous 3D mapping further demonstrated his systems-level thinking across diverse hardware platforms. More recently, Wang has pushed into resilient and robust perception, contributing the NTU4DRadLM dataset — a landmark multi-modal benchmark enabling 4D radar-based SLAM under adverse conditions — and developing magnetic-lead localization techniques for repetitive, GPS-denied environments. His body of work, spanning foundational control theory to cutting-edge perception, has amassed over 420 citations, establishing him as a versatile and impactful contributor to autonomous systems research.