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arXiv · 2302.01892

Nonconvex Distributed Feedback Optimization for Aggregative Cooperative Robotics

Guido Carnevale·Nicola Mimmo·Giuseppe Notarstefano
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Abstract

Distributed aggregative optimization is a recently emerged framework in which the agents of a network want to minimize the sum of local objective functions, each one depending on the agent decision variable (e.g., the local position of a team of robots) and an aggregation of all the agents' variables (e.g., the team barycentre). In this paper, we address a distributed feedback optimization framework in which agents implement a local (distributed) policy to reach a steady-state minimizing an aggregative cost function. We propose Aggregative Tracking Feedback, i.e., a novel distributed feedback optimization law in which each agent combines a closed-loop gradient flow with a consensus-based dynamic compensator reconstructing the missing global information. By using tools from system theory, we prove that Aggregative Tracking Feedback steers the network to a stationary point of an aggregative optimization problem with (possibly) nonconvex objective function. The effectiveness of the proposed method is validated through numerical simulations on a multi-robot surveillance scenario.

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Guido Carnevale, Nicola Mimmo, Giuseppe Notarstefano. 2023-02-03. Nonconvex Distributed Feedback Optimization for Aggregative Cooperative Robotics. https://arxiv.org/abs/2302.01892

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