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arXiv · 2503.21548

Combining Graph Attention Networks and Distributed Optimization for Multi-Robot Mixed-Integer Convex Programming

Viet-Anh Le·Panagiotis Kounatidis·Andreas A. Malikopoulos
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Abstract

In this paper, we develop a fast mixed-integer convex programming (MICP) framework for multi-robot navigation by combining graph attention networks and distributed optimization. We formulate a mixed-integer optimization problem for receding horizon motion planning of a multi-robot system, taking into account the surrounding obstacles. To address the resulting multi-agent MICP problem in real time, we propose a framework that utilizes heterogeneous graph attention networks to learn the latent mapping from problem parameters to optimal binary solutions. Furthermore, we apply a distributed proximal alternating direction method of multipliers algorithm for solving the convex continuous optimization problem. We demonstrate the effectiveness of our proposed framework through experiments conducted on a robotic testbed.

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Viet-Anh Le, Panagiotis Kounatidis, Andreas A. Malikopoulos. 2025-03-27. Combining Graph Attention Networks and Distributed Optimization for Multi-Robot Mixed-Integer Convex Programming. https://arxiv.org/abs/2503.21548

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