Tung Dang
Papers
28
Total Citations
1,406
H-Index
18
About
Tung Dang is a robotics researcher whose work centers on autonomous exploration, path planning, and state estimation for aerial and legged robotic systems, with a particular emphasis on challenging subterranean environments. His most influential contributions include a suite of graph-based exploration path planning frameworks designed for underground mines and tunnel networks — work that has collectively garnered hundreds of citations and directly shaped how robots navigate GPS-denied, confined spaces. His 2020 paper on graph-based subterranean exploration using aerial and legged robots has accumulated 240 citations, reflecting its broad adoption within the field, while his motion primitives-based planner (176 citations) demonstrated how micro aerial vehicles could achieve fast, agile exploration despite limited endurance. Dang has also advanced resilient multi-modal sensor fusion for robust pose estimation (138 citations) and pioneered learning-based approaches to subterranean path planning through imitation learning. His practical impact extends to real-world mine rescue scenarios, where his autonomous systems frameworks have been validated in operational settings. Across his body of work, Dang has helped establish foundational methodologies that underpin modern subterranean robotics research, including contributions relevant to competitions such as the DARPA Subterranean Challenge.
Research Focus
Key Achievements
Top Papers
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- 8Autonomous Search for Underground Mine Rescue Using Aerial Robots57 citations · 2020
- 9Autonomous exploration and simultaneous object search using aerial robots54 citations · 2018
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