About

Wan‐Ting Jin is a pioneering roboticist whose work sits at the intersection of autonomous navigation, multi-robot systems, and environmental sensing. Her primary research focuses on Gas Source Localization (GSL)—enabling mobile robots to detect and map hazardous gas leaks in complex, real-world environments. Jin’s most impactful contribution, "Towards Efficient Gas Leak Detection in Built Environments: Data-Driven Plume Modeling for Gas Sensing Robots" (2023, 12 citations), addresses the critical challenge of gas dispersion complexity by introducing data-driven plume models that allow robots to operate safely in dangerous scenarios. She further advances the field with "3D Gas Sensing with Multiple Nano Aerial Vehicles" (2023, 8 citations), where she tackles interference issues in multi-drone systems for environmental monitoring—a key step toward scalable climate change mitigation tools. Beyond gas sensing, Jin explores human-robot interaction in "Proactive-cooperative Navigation in Human-like Environment" (2020, 3 citations), developing control strategies that enable robots to navigate socially among pedestrians. Her latest work, "Sense in Motion with Belief Clustering" (2024, 2 citations), tackles the patchy nature of gas plumes with efficient, belief-based algorithms. With a growing citation record and a focus on deploying robots in hazardous and human-centric settings, Jin is shaping the future of autonomous environmental monitoring and socially aware robotics.

Research Focus

Key Achievements

3
H-Index
4
Papers
25
Total Citations
6
Avg Citations/Paper
🏆 Most Cited Paper
Towards Efficient Gas Leak Detection in Built Environments: Data-Driven Plume Modeling for Gas Sensing Robots
12 citations · 2023
📈 Most Prolific Year: 2023 (2 Papers)
🤝 Key Collaborators: 5
🏛 Institutions: École Polytechnique Fédérale de Lausanne, Institut national de recherche en sciences et technologies du numérique

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago