Ioannis Karamouzas
University of Minnesota, Clemson University, University of California, Riverside
Papers
10
Total Citations
156
H-Index
7
About
Ioannis Karamouzas is a leading researcher in multi-agent navigation and robotics, whose work bridges autonomous systems, human-robot interaction, and motion planning. His key contributions lie in developing algorithms for coordinated multi-robot teams, including stochastic tree search for patrolling missions and adaptive learning for collision-free navigation in crowded environments. Karamouzas pioneered the use of Monte Carlo Tree Search (MCTS) for multi-robot patrolling policies and introduced Formation Velocity Obstacles for prioritized group navigation. His C-OPT framework addresses coverage-aware trajectory optimization under uncertainty, while C-Nav enables distributed coordination in dense multi-agent settings. With over 150 citations across his most-cited papers, his work has significantly advanced autonomous navigation in complex, dynamic environments. Notable achievements include his research on anticipatory collision avoidance and model-free reinforcement learning for continuum manipulators, as well as recent insights into human-robot interactions with small service robots. Karamouzas’s contributions are essential reading for students and researchers working on multi-robot systems, crowd simulation, and autonomous navigation.
Research Focus
Key Achievements
Top Papers
- 1Stochastic Tree Search with Useful Cycles for patrolling problems30 citations · 2015
- 2Adaptive Learning for Multi-Agent Navigation29 citations · 2015
- 3C-OPT: Coverage-Aware Trajectory Optimization Under Uncertainty29 citations · 2016
- 4Prioritized group navigation with Formation Velocity Obstacles18 citations · 2015
- 5C-Nav: Distributed coordination in crowded multi-agent navigation17 citations · 2020
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- 7Guide to Anticipatory Collision Avoidance8 citations · 2019
- 8Anytime navigation with Progressive Hindsight optimization7 citations · 2014
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