Papers
3
Total Citations
50
H-Index
3
About
Zu Lin Ewe is a robotics researcher specializing in autonomous navigation for unmanned vehicles in challenging, real-world environments. Her work primarily focuses on deep reinforcement learning, multi-robot systems, and sensor fusion for adverse conditions. Ewe’s most notable contribution is the development of cross-modal contrastive learning that enables lightweight, low-cost millimeter wave radar to navigate in poor visibility—such as smoke, dust, or fog—where traditional cameras fail, a paper cited 26 times. She also led the design of a heterogeneous team of unmanned ground vehicles and blimp robots for search and rescue in subterranean environments, integrating data-driven autonomy with communication-aware navigation to ensure reliable coordination (20 citations). Her recent work on spatial graph-based localization using scaleless floorplans (4 citations) aims to make navigation accessible to non-expert users, mimicking human ability to orient without prior maps. Ewe’s research has direct impact on disaster response, autonomous logistics, and field robotics, bridging the gap between complex algorithms and practical deployment. Her achievements highlight a commitment to robust, scalable autonomy for safety-critical missions.
Research Focus
Key Achievements
Top Papers
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- 3Spatial Graph-Based Localization and Navigation on Scaleless Floorplan4 citations · 2024