Dror Dayan

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About

Dror Dayan is a robotics researcher whose work centers on multi-robot motion planning (MRMP), with a particular focus on developing theoretically grounded, efficient algorithms for coordinating multiple autonomous agents in continuous environments. His most recognized contributions revolve around the **tensor roadmap** framework — a mathematical structure that emerges from combining probabilistic roadmap (PRM) graphs for individual robots via a tensor product. Across a series of progressively refined works spanning 2020 to 2023, Dayan and his collaborators have systematically investigated the conditions under which tensor roadmaps encode near-optimal motion plans, advancing the theoretical foundations of sampling-based multi-robot planning. His 2023 paper, "Near-Optimal Multi-Robot Motion Planning with Finite Sampling," has garnered 17 citations, reflecting growing recognition of his contributions within the robotics and AI planning communities. Taken together, his body of work addresses a fundamental challenge in robotics — scaling motion planning to multi-agent settings without sacrificing optimality guarantees — making it valuable reading for researchers and students working at the intersection of algorithmic robotics, motion planning, and autonomous systems.