Report
Managing Context and Communication in Distributed Agentic UAV Swarms
| Title: | Managing Context and Communication in Distributed Agentic UAV Swarms |
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| Authors: | Iannoli, Andrea, Zyrianoff, Ivan, Trotta, Angelo, Gigli, Lorenzo, Di Felice, Marco |
| Publication Year: | 2026 |
| Subject Terms: | Multiagent Systems, Artificial Intelligence, Machine Learning, Networking and Internet Architecture, Robotics, 68T42, 68T40, 68T05, 68M14, 93C85, 93C95, I.2.11; C.3; C.2.1; I.2.8; H.5.2 |
| Description: | Unmanned aerial vehicle (UAV) swarms increasingly rely on language-model agents to provide adaptive mission-level reasoning in uncertain environments. Fully distributed control, in which each UAV hosts an independent Small Language Model (SLM), removes reliance on a centralized coordinator but introduces an information-management problem: long-running interaction histories can degrade the reasoning context, while indiscriminate information dissemination increases communication and inference overhead. We address these challenges with a distributed UAV-agent architecture that enables continuous local SLM control through an event-driven reason-act-observe lifecycle. Runtime knowledge is represented as structured atomic notes and organized into core, local, and peer-specific memory. A deterministic interest-aware gossip engine selectively disseminates these notes according to recipient-specific semantic novelty and recency. We evaluate the architecture using ten UAVs in a simulated search-and-rescue mission. Our approach completes all experimental runs, whereas unrestricted flooding messages completes only 70-85\%, and delegating forwarding decisions to the SLM prevents mission completion in every run. Compared with unrestricted flooding, our approach approximately halves inference-token consumption, reduces transmitted data, and achieves lower survivor-count error. 12 pages, 4 figures. This paper has been accepted for presentation at the 24th IEEE Consumer Communications & Networking Conference 2027 (CCNC 2027) |
| Document Type: | Working Paper |
| DOI: | 10.48550/arXiv.2610.01569 |
| Access URL: | https://arxiv.org/abs/2610.01569 |
| Accession Number: | edsarx.2610.01569 |
| Database: | arXiv |
| DOI: | 10.48550/arXiv.2610.01569 |
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