Papers
13
Total Citations
157
H-Index
7
About
Denise Rizzo is a robotics and autonomous systems researcher whose work sits at the intersection of multi-robot coordination, energy-aware planning, and decision-making under uncertainty. Her research has made significant contributions to how robot teams operate efficiently in complex, real-world environments — particularly in off-road and hazardous settings where energy constraints and terrain variability pose serious challenges. Rizzo's most cited work (43 citations) introduces a stochastic programming framework for scheduling heterogeneous robot teams under capability uncertainty, with applications ranging from disaster response to pandemic robotics. Complementing this, her research on probabilistic spatial mapping for energy cost prediction (37 citations) provides a principled approach to planning robot paths where terrain-dependent power consumption is variable and uncertain. Her 2017 paper on energy-efficient multi-robot reconnaissance (19 citations) further demonstrates her focus on resource-constrained autonomy in dangerous environments. Beyond individual platforms, Rizzo has advanced methods for sharing terrain data across heterogeneous robot fleets and developed stochastic frameworks for vehicle routing with energy uncertainty. Her work on multi-robot tour guiding and human-robot teaming reflects a broader interest in human-robot collaboration. With over 150 total citations, her research provides foundational tools for deploying reliable, energy-conscious autonomous systems in demanding, unpredictable environments.
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