Michael Quann
Papers
5
Total Citations
88
H-Index
5
About
Michael Quann is a robotics researcher whose work sits at the intersection of autonomous systems, energy-efficient planning, and terrain-aware navigation for ground robots. His research addresses one of the most pressing practical challenges in field robotics: how to enable autonomous robots to operate effectively in unpredictable, off-road environments while managing their inherent energy constraints. Quann's most influential contribution, "Off-Road Ground Robot Path Energy Cost Prediction through Probabilistic Spatial Mapping" (2019, 37 citations), introduced a probabilistic framework for modeling energy usage across varied terrain — a significant advance for real-world robot deployment. Complementing this, his work on chance-constrained reachability and 3-D topography mapping further refined how robots can anticipate and adapt to complex environmental conditions. Notably, his 2020 paper extended these ideas to heterogeneous multi-robot systems, enabling energy data sharing across different robot platforms — a meaningful step toward scalable, collaborative autonomous fleets. With a combined citation count exceeding 85 across his key publications, Quann has established himself as a thoughtful contributor to energy-aware robotics. His work is particularly valuable for students and practitioners developing autonomous systems for search-and-rescue, reconnaissance, or environmental monitoring missions in challenging terrain.
Research Focus
Key Achievements
Top Papers
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