Michail Kontitsis

University of Denver, Georgia Institute of Technology

Papers

Total Citations

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About

Michail Kontitsis is a researcher whose work bridges the frontiers of autonomous robotics and aerospace engineering, with a particular focus on decision-making under uncertainty. His key research areas include active simultaneous localization and mapping (SLAM), stochastic optimal control, and cooperative relative navigation for space operations. Kontitsis’s most impactful contribution is his pioneering work on multi-robot active SLAM, where he introduced a novel approach using Relative Entropy optimization to select trajectories that minimize both localization error and uncertainty bounds. This work, published in 2013, has garnered 35 citations and is foundational for teams of robots exploring unknown environments. In the aerospace domain, he developed a numerically efficient method for cooperative relative navigation during space rendezvous and proximity operations, using a monocular camera and a patterned target spacecraft. This 2015 paper, with 24 citations, addresses critical challenges in autonomous spaceflight. Additionally, his exploration of model-based path integral stochastic control using Bayesian nonparametric methods aims to improve sample efficiency in reinforcement learning for robotics. Through these contributions, Kontitsis has advanced the theoretical and practical capabilities of autonomous systems operating in complex, uncertain environments.