Fast k-connectivity restoration in multi-robot systems for robust communication maintenance: algorithmic and learning-based solutions

Maintaining a robust communication network is crucial for the success of multi-robot online task planning. A key capability of such systems is the ability to repair the communication topology in the event of robot failures, thereby ensuring continued coordination. In this paper, we address the Fast...

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Bibliographic Details
Published in:Autonomous robots Vol. 49; no. 4; p. 34
Main Authors: Shi, Guangyao, Ishat-E-Rabban, Md, Bonner, Griffin, Tokekar, Pratap
Format: Journal Article
Language:English
Published: New York Springer US 01.12.2025
Springer Nature B.V
Subjects:
Algebra
Algorithms
Approximation
Artificial Intelligence
Communication
Communications networks
Computer Imaging
Connectivity
Control
Engineering
Graph neural networks
Graph theory
Learning
Mechatronics
Multiple robots
Network topologies
Pattern Recognition and Graphics
Performance evaluation
Restoration
Robot dynamics
Robotics
Robotics and Automation
Robots
Robustness (mathematics)
Task planning (robotics)
Vision
connectivity
Multi-robot systems
Robust communication
ISSN:0929-5593, 1573-7527
Online Access:Get full text
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Summary:Maintaining a robust communication network is crucial for the success of multi-robot online task planning. A key capability of such systems is the ability to repair the communication topology in the event of robot failures, thereby ensuring continued coordination. In this paper, we address the Fast k -Connectivity Restoration (FCR) problem, which seeks to restore a network’s k -connectivity with minimal robot movement. Here, a k -connected network refers to a topology that remains connected despite the removal of up to nodes. We first formulate the FCR problem as a Quadratically Constrained Program (QCP), which yields optimal solutions but is computationally intractable for large-scale instances. To overcome this limitation, we propose EA-SCR, a scalable algorithm grounded in graph-theoretic principles, which leverages global network information to guide robot movements. Furthermore, we develop a learning-based approach, GNN-EA-SCR, which employs aggregation graph neural networks to learn a decentralized counterpart of EA-SCR, relying solely on local information exchanged among neighboring robots. Through empirical evaluation, we demonstrate that EA-SCR achieves solutions within 10% of the optimal while being orders of magnitude faster. Additionally, EA-SCR surpasses existing methods by 30% in terms of the FCR distance metric. For the learning-based solution, GNN-EA-SCR, we show it attains a success rate exceeding 90% and exhibits comparable maximum robot movement to EA-SCR.
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ISSN:0929-5593
1573-7527
DOI:10.1007/s10514-025-10224-5