Marina Kolendovska

Kharkiv National University of Radio Electronics

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About

Marina Kolendovska is a robotics researcher whose work focuses on reducing uncertainty in multi-robot perception and navigation, particularly in densely cluttered environments. Her core contributions lie at the intersection of 3D data fusion, information theory, and technical vision systems (TVS). Her most cited work, "Multi-view 3D data fusion and patching to reduce Shannon entropy in Robotic Vision" (2024, 30 citations), introduces a novel method for merging multiple sensor views to minimize informational entropy, directly improving the reliability of robotic vision. Earlier foundational papers, such as "Individual Scans Fusion in Virtual Knowledge Base for Navigation of Mobile Robotic Group with 3D TVS" (2018, 17 citations), established a framework for distributed robotic group behavior, enabling efficient navigation through complex terrain by sharing fused 3D data. Her 2019 study on "Effective informational entropy reduction in multi-robot systems based on real-time TVS" (12 citations) further advanced this approach, using real-time laser TVS as the primary sensing tool to dynamically reduce entropy and boost system efficiency. Kolendovska’s work is notable for its practical application of Shannon entropy as a metric for robotic decision-making, offering a mathematically rigorous path to more autonomous and coordinated multi-robot teams.