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About

Guido Carnevale is a researcher specializing in distributed optimization, cooperative robotics, and multi-agent systems, with a particular focus on aggregative optimization frameworks that enable teams of autonomous robots to coordinate efficiently without centralized control. His most influential contribution, "Distributed Online Aggregative Optimization for Dynamic Multirobot Coordination" (2022, 63 citations), established a foundational online framework allowing networked robots to minimize collective cost functions while adapting to dynamic environments. Building on this, his work on Aggregative Tracking Feedback introduced novel distributed feedback laws that continuously steer robotic networks toward optimal configurations, earning 30 citations and demonstrating real-world applicability in cooperative robotics scenarios. Carnevale has progressively extended this framework to address nonconvex optimization landscapes and practical multi-robot tasks such as surveillance, target encirclement, and patrolling. His 2025 tutorial on distributed optimization for cooperative robotics reflects his growing role as a synthesizer and educator within the field, consolidating algorithms, toolboxes, and research directions for the broader community. Across his body of work, Carnevale has accumulated over 125 citations, signaling meaningful and growing impact. His research bridges rigorous mathematical optimization theory with tangible robotics applications, making him a notable emerging voice in autonomous multi-robot systems.