Vladimir Ufimtsev
Papers
6
Total Citations
73
H-Index
6
About
Vladimir Ufimtsev’s research lies at the intersection of multi-robot systems, coalition formation, and modular robotics, with a focus on enabling teams of robots to tackle complex, real-world tasks that exceed the capabilities of any single machine. His core contributions center on developing novel, game-theoretic and graph-based algorithms for multi-robot task allocation and dynamic self-reconfiguration. Ufimtsev pioneered the use of correlation clustering to form optimal robot coalitions, as demonstrated in his most-cited work (17 citations), and extended these ideas to distributed hedonic coalition formation, allowing robots to autonomously and efficiently partition themselves for heterogeneous tasks. In modular self-reconfigurable robots, he introduced a paradigm shift by modeling reconfiguration as a coalition game under uncertainty, enabling dynamic adaptation without exhaustive search. His work has accumulated over 70 citations, with key papers appearing in venues like *Autonomous Robots* and IEEE conferences. Notably, his 2012 study on graph partitioning for modular robot reconfiguration remains a foundational reference in the field. Ufimtsev’s research provides scalable, principled solutions for coordinating robot swarms, making him a key figure in advancing autonomous multi-robot collaboration.
Research Focus
Key Achievements
Top Papers
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- 4Distributed Hedonic Coalition Formation for Multi-Robot Task Allocation12 citations · 2021
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- 6Correlation clustering-based multi-robot task allocation6 citations · 2020