Ugur Kuter

University of Maryland, College Park, Smart Information Flow Technologies (United States)

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About

Ugur Kuter is a leading researcher in artificial intelligence and autonomous systems, with a primary focus on real-time planning, robotics, and human-machine teaming. His most cited work, "Real-Time Planning for Covering an Initially-Unknown Spatial Environment" (2011, 19 citations), introduces four novel strategies—Iterated WaveFront, Greedy-Scan, Delayed Greedy-Scan, and Closest-First Scan—that enable robotic vehicles to autonomously and cost-effectively cover unknown environments on the fly. This foundational contribution has advanced the field of autonomous exploration and mapping. In his influential paper "Towards Self-Confidence in Autonomous Systems" (2016, 9 citations), Kuter addresses the critical challenge of building machine autonomy that can operate effectively under uncertainty while enabling meaningful human supervision. He explores how autonomous systems can develop self-confidence to support "on-the-loop" human roles, shifting the paradigm from traditional human-in-the-loop control. Kuter's work bridges theoretical planning algorithms with practical applications in civilian and military systems, making him a key figure in developing trustworthy, adaptive autonomy that can operate reliably in complex, dynamic environments.