{"ID":2835025,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.02079","arxiv_id":"2512.02079","title":"Robust Geospatial Coordination of Multi-Agent Communications Networks Under Attrition","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 ($Φ$IREMAN), a topological algorithm leveraging physics-inspired potential fields to solve this problem. In our evaluations, $Φ$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.","short_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 fragm...","url_abs":"https://arxiv.org/abs/2512.02079","url_pdf":"https://arxiv.org/pdf/2512.02079v2","authors":"[\"Jonathan S. Kent\",\"Eliana Stefani\",\"Brian Plancher\"]","published":"2025-11-30T22:13:50Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.MA\",\"eess.SY\"]","methods":"[]","has_code":false}