Robust Geospatial Coordination of Multi-Agent Communications Networks Under Attrition
This addresses the critical need for resilient communication in extreme environments like wildfires, though it is incremental as it extends existing connectivity maintenance methods.
The paper tackles the problem of maintaining communication networks for emergency responses under high node attrition by introducing a topological algorithm, which achieved over 99.9% task uptime in simulations with up to 500 drones.
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.