Robust Geospatial Coordination of Multi-Agent Communications Networks Under Attrition
arXiv:2512.02079v2 Announce Type: replace-cross Abstract: Coordinating emergency responses in extreme environments, such as wildfires, requires resilient and high-bandwidth communication backbones. While autonomous aerial swarms can establish ad-hoc networks to provide this connectivity, the high risk of individual node attrition in these settings often leads to network fragmentation and mission-critical downtime. To overcome this challenge, we introduce and formalize the problem of Robust Task Networking Under Attrition (RTNUA), which extends connectivity maintenance in multi-robot systems to explicitly address proactive redundancy and attrition recovery. We then introduce Physics-Informed Robust Employment of Multi-Agent Networks ($\Phi$IREMAN), a topological algorithm leveraging physics
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Abstract:Coordinating emergency responses in extreme environments, such as wildfires, requires resilient and high-bandwidth communication backbones. While autonomous aerial swarms can establish ad-hoc networks to provide this connectivity, the high risk of individual node attrition in these settings often leads to network fragmentation and mission-critical downtime. To overcome this challenge, we introduce and formalize the problem of Robust Task Networking Under Attrition (RTNUA), which extends connectivity maintenance in multi-robot systems to explicitly address proactive redundancy and attrition recovery. We then introduce Physics-Informed Robust Employment of Multi-Agent Networks ($\Phi$IREMAN), a topological algorithm leveraging physics-inspired potential fields to solve this problem. In our evaluations, $\Phi$IREMAN consistently outperforms baselines, and is able to maintain greater than $99.9%$ task uptime despite substantial attrition in simulations with up to 100 tasks and 500 drones, demonstrating both effectiveness and scalability.
Comments: 8 pages, 4 figures, 4 tables, accepted to IEEE RA-L
Subjects:
Robotics (cs.RO); Multiagent Systems (cs.MA); Systems and Control (eess.SY)
MSC classes: 93C85, 68T40, 93A14, 68W15
ACM classes: I.2.9; I.2.11; C.2.1
Cite as: arXiv:2512.02079 [cs.RO]
(or arXiv:2512.02079v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2512.02079
arXiv-issued DOI via DataCite
Submission history
From: Jonathan Kent [view email] [v1] Sun, 30 Nov 2025 22:13:50 UTC (544 KB) [v2] Wed, 1 Apr 2026 17:21:59 UTC (623 KB)
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