Nonconvex Distributed Feedback Optimization for Aggregative Cooperative Robotics
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.
Cite
@article{arxiv.2302.01892,
title = {Nonconvex Distributed Feedback Optimization for Aggregative Cooperative Robotics},
author = {Guido Carnevale and Nicola Mimmo and Giuseppe Notarstefano},
journal= {arXiv preprint arXiv:2302.01892},
year = {2024}
}