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About

Andrew Beveridge is a leading researcher in algorithmic game theory and multi-agent coordination, with a primary focus on rendezvous search and pursuit-evasion problems. His work addresses fundamental challenges in robotics and autonomous systems, particularly how multiple agents can efficiently meet or capture one another without centralized control. Beveridge’s most influential contribution is his work on symmetric rendezvous search on the line, where two robots with identical strategies and no knowledge of each other’s starting positions must meet as quickly as possible. His 2013 paper on this topic, with 19 citations, provides a novel algorithm that significantly improves upon existing solutions. He has since extended these ideas to more complex planar environments, both with and without obstacles, as seen in his 2019 and 2021 papers. Beveridge also made key contributions to making pursuit-evasion theory more accessible to practitioners through his 2016 tutorial, which has garnered 13 citations. His research has direct applications in search-and-rescue operations, surveillance, and autonomous exploration, where efficient coordination under uncertainty is critical.