This paper presents a coordination architecture for heterogeneous UAV/UGV swarms that synthesises mission actions from uncertain, multi-modal sensor evidence while preserving hardware-enforced safety at the actuation boundary. The approach combines radar, RF, acoustic, and visual observations with Topic-Based Communication Space Petri Net (TB-CSPN) orchestration to support incremental mission formation under partial and evolving information. Consultant agents transform sensor outputs into temporally bounded semantic tokens, while supervisor agents provide authorisation and policy-governed release of mission transitions. This separation between interpretation, coordination, and execution yields auditable decision paths, constrains non-determinism within the coordination layer through guards and synchronisation, and enables bounded-time integration of heterogeneous evidence. To improve resilience in contested environments, including cyber compromise, spoofing, jamming, and communication loss, the digital coordination layer is complemented by independent analogue safety envelopes that clamp or veto unsafe actuator commands issued to individual vehicles. A coastal-surveillance case study illustrates how the proposed architecture enables dependable, governed, and physically safe swarm coordination under operational uncertainty.
A mixed-criticality architectural framework that applies SAE ARP4754B methods to swarm reconfiguration, enabling intelligent swarm behaviors without compromising flight-critical isolation is proposed.
Luiz Giacomossi, Zafer Yigit, Marwan Shakarna et al.· 0 citations
MulRobBench provides a reproducible benchmark for trustworthy multimodal UAV decision making under realistic operational constraints and identifies modality-trust selection, constraint extraction, glare, missing data, and operator shorthand as the primary causes of decision instability.
B. Alsinglawi, Wei-Zheng Wang, Jun-Yi Wu et al.· arXiv.org· 0 citations
This paper proposes AERIS, an offline policy improvement framework for multi-UAV ISAC that learns from fixed flight logs under centralized training and decentralized execution and designs STAR-CRDT, an offline multi-agent RL algorithm that performs support-aware local action rectification and distills only trusted impr...
Ziyuan Wang, Yi-Fan Sui, Wei Wei et al.· 0 citations
The three-layer temporal decoupling architecture provides a practical template for safely integrating LLM-assisted planning with real-time multi-agent control in contested environments.
Xingda Li, Jian-Qiang Zhang, Yiping Liu et al.· Drones· 1 citation
The proposed approach partitions the UAS team into stationary anchors and mobile workers operating under cyclic replacement modes, and constructs a deep neural network (DNN)-inspired communication topology that enables fully decentralized coordination through local interactions.
This paper proposes LLM-Centric Agentic AI for UAV Swarms (LAUS) and reviews key enabling technologies such as onboard and edge computing, 5G/6G connectivity, multimodal intelligence, and cybersecurity mechanisms, and analyzes threats such as Priority Manipulation Attacks (PMA) that can distort decision-making and degr...
Yousef Emami, Rahim Taheri, MohammadHossein Homaei et al.· arXiv.org· 0 citations
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