Guillermo Angeris

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About

Guillermo Angeris is a researcher whose work lies at the intersection of optimization, robotics, and control theory, with a particular focus on enabling safe, decentralized multi-agent systems. His most significant contribution is the development of a fast, fully distributed collision avoidance algorithm that operates under realistic measurement uncertainty. This algorithm, detailed in his highly cited 2022 paper (8 citations) and its earlier 2019 precursor (3 citations), allows teams of mobile robots to navigate safely using only noisy, on-board sensor data, without any need for inter-agent communication. By framing the problem as a convex optimization, Angeris provides a computationally efficient and provably safe solution to a critical challenge in swarm robotics. This work has direct implications for autonomous driving, drone swarms, and warehouse logistics, where reliable, communication-free coordination is essential. His research demonstrates a powerful ability to translate theoretical optimization methods into practical, robust algorithms for real-world robotic systems, marking him as a rising contributor to the field of multi-agent motion planning.