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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.21548 |
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| _version_ | 1866913762684960768 |
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| author | Le, Viet-Anh Kounatidis, Panagiotis Malikopoulos, Andreas A. |
| author_facet | Le, Viet-Anh Kounatidis, Panagiotis Malikopoulos, Andreas A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_21548 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Combining Graph Attention Networks and Distributed Optimization for Multi-Robot Mixed-Integer Convex Programming Le, Viet-Anh Kounatidis, Panagiotis Malikopoulos, Andreas A. Systems and Control 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. |
| title | Combining Graph Attention Networks and Distributed Optimization for Multi-Robot Mixed-Integer Convex Programming |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2503.21548 |