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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Vydáno v:	Autonomous robots Ročník 49; číslo 4; s. 34
Hlavní autoři:	Shi, Guangyao, Ishat-E-Rabban, Md, Bonner, Griffin, Tokekar, Pratap
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.12.2025 Springer Nature B.V
Témata:	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
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Abstract	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.
AbstractList	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. 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.
ArticleNumber	34
Author	Shi, Guangyao Bonner, Griffin Tokekar, Pratap Ishat-E-Rabban, Md
Author_xml	– sequence: 1 givenname: Guangyao surname: Shi fullname: Shi, Guangyao email: shig@usc.edu organization: Computer Science, University of Southern California – sequence: 2 givenname: Md surname: Ishat-E-Rabban fullname: Ishat-E-Rabban, Md organization: Computer Science, University of Maryland – sequence: 3 givenname: Griffin surname: Bonner fullname: Bonner, Griffin organization: Computer Science, University of Maryland – sequence: 4 givenname: Pratap surname: Tokekar fullname: Tokekar, Pratap organization: Computer Science, University of Maryland
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Snippet	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...
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SubjectTerms	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
Title	Fast k-connectivity restoration in multi-robot systems for robust communication maintenance: algorithmic and learning-based solutions
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