Papers
21
Total Citations
296
H-Index
9
About
Tommy Chang is a researcher specializing in autonomous mobile robotics, with particular expertise in sensor fusion, terrain traversability, and machine learning for ground vehicle navigation. Working primarily through the National Institute of Standards and Technology (NIST), Chang has made significant contributions to some of the U.S. military's most ambitious autonomous vehicle programs, including the Army's Demo III project and DARPA's Learning Applied to Ground Vehicles (LAGR) program. His most influential work centers on combining ladar range imaging with color camera data to enable robots to detect roads, identify obstacles, and navigate complex off-road environments — a foundational challenge in autonomous vehicle development. His papers on road detection and traversability learning have each garnered 49 citations, reflecting their lasting influence on the field. Through DARPA LAGR, Chang helped pioneer hierarchical control architectures that integrate machine learning directly into vehicle navigation systems, contributing to work cited nearly 40 times. Beyond navigation, Chang has contributed to performance evaluation methodologies for robotics systems, including dynamic 6DOF metrology and human detection in unstructured environments. His body of work, accumulating over 250 citations, represents a meaningful bridge between laboratory robotics research and real-world autonomous vehicle deployment.
Research Focus
Key Achievements
Top Papers
- 1Learning traversability models for autonomous mobile vehicles49 citations · 2007
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- 3Learning in a hierarchical control system: 4D/RCS in the DARPA LAGR program39 citations · 2006
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- 7Performance Evaluation of a Terrain Traversability Learning Algorithm in the DARPA LAGR Program13 citations · 2009
- 8Dynamic 6DOF metrology for evaluating a visual servoing system13 citations · 2008
- 9Integrating learning into a hierarchical vehicle control system11 citations · 2007
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