Bilal Kartal

University of Minnesota System, University of Minnesota

Papers

Total Citations

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About

Bilal Kartal is a robotics and artificial intelligence researcher whose work centers on multi-robot systems, task allocation, and autonomous decision-making. His research tackles the complex challenge of coordinating robot teams to efficiently accomplish real-world objectives in domains such as warehouse automation, surveillance, and environmental patrolling. Kartal's most notable contributions involve the innovative application of Monte Carlo Tree Search (MCTS) to multi-robot problems. In his 2016 work on multi-robot task allocation, he developed an efficient, satisficing, and centralized planning approach that enables robot teams to optimize collective objectives across spatially and temporally constrained tasks — a paper that has garnered 33 citations. Complementing this, his 2015 research introduced stochastic tree search with useful cycles for patrolling problems, proposing an anytime algorithm that extends MCTS to enable continuous, strategic coverage of arbitrary environments by autonomous robot teams, accumulating 30 citations. Together, these contributions demonstrate Kartal's ability to adapt powerful search-based AI methods to practical multi-agent coordination challenges. His work has meaningfully advanced the state of the art in autonomous robot team planning, offering scalable solutions relevant to both academic research and real-world deployment scenarios.