Andrea Camisa

Papers

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About

Andrea Camisa’s research lies at the intersection of distributed optimization, cooperative robotics, and multi-agent systems, with a core focus on enabling autonomous robot teams to make real-time, decentralized decisions. Her most cited work, “Distributed Online Aggregative Optimization for Dynamic Multirobot Coordination” (2022, 63 citations), pioneers an online version of the distributed constrained aggregative optimization framework. This breakthrough allows robots in a network to dynamically minimize the sum of local cost functions—each dependent on both local and aggregate team decisions—making it ideal for applications like formation control and resource allocation in changing environments. In her related work on “Multi-Robot Pickup and Delivery via Distributed Resource Allocation” (2022), she tackles large-scale vehicle routing problems, enabling robots to self-coordinate and allocate tasks without a central supervisor. By combining rigorous theoretical guarantees with practical algorithms, Camisa’s contributions provide scalable solutions for complex multirobot coordination, significantly advancing the field of autonomous systems. Her work is essential reading for researchers interested in distributed control, online optimization, and the future of cooperative robotics.