Papers
14
Total Citations
113
H-Index
7
About
Guillaume Lozenguez is a leading researcher in multi-robot systems, autonomous navigation, and ambient intelligence, with a focus on coordinated exploration and mapping in unknown environments. His major contributions include developing auction-based coordination protocols for multi-robot task allocation, as demonstrated in his most-cited work "Punctual versus continuous auction coordination for multi-robot and multi-task topological navigation" (24 citations), which addresses the challenge of efficient task distribution among robotic fleets. Lozenguez pioneered the use of Markovian Decision Processes (MDPs) combined with Road-Map techniques for map partitioning, enabling autonomous exploration strategies that approximate optimal coverage. His work on lightweight map formats, including Vector Maps and PolyMap, has advanced multi-robot localization and mapping, with notable applications in indoor environments. Lozenguez has also extended his research to water quality monitoring, integrating field measurements with robotic simulations for environmental assessment. His contributions to coordinated multi-robot assistance deployment in smart spaces highlight his impact on real-world robotic systems, with over 100 total citations across his publications.
Research Focus
Key Achievements
Top Papers
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- 3Map partitioning to approximate an exploration strategy in mobile robotics13 citations · 2012
- 4Vector Maps: A Lightweight and Accurate Map Format for Multi-robot Systems11 citations · 2016
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- 10Towards Robots-Assisted Ambient Intelligence3 citations · 2018