发表机构
School of Computer Science and Technology, Xidian University; Shaanxi Key Laboratory of Network and System Security; College of Cyber Security, Jinan University; School of Cyber Engineering, Shaanxi Key Lab of Network and System Security（西安电子科技大学计算机科学与技术学院; 陕西省网络与系统安全重点实验室; 广州大学网络安全学院; 陕西省网络与系统安全重点实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对将SHCUA用于无人机控制的结构不匹配问题，提出实时安全导向的重组方法，把SHCUA输出转换为合同约束的无人机技能调用，设计分离架构，原型评估显示RT-SHCUA能保持响应能力并支持相关特性。
AI 中文摘要
自然语言控制为无人机提供了一个有前景的接口，但直接将自托管计算机使用代理（SHCUA）应用于无人机控制会导致结构不匹配。SHCUA专为交互式主机端工具使用而设计，延迟的代理迭代通常是可接受的。然而，无人机控制与不断变化的物理状态、严格的时间限制、安全风险和安全责任相关联。本文提出了一种基于SHCUA的无人机控制的实时和安全导向的重组。我们将其输出转换为具有明确时间、状态、权限、回退和证据语义的合同约束无人机技能调用。基于此抽象，设计了一种将语义推理与机载执行以及安全/安全执行分开的架构。原型评估表明，RT-SHCUA在支持降级处理、可信准入和可审计证据保存的同时，保持了有限的任务级响应能力。
英文摘要
Natural-language control offers a promising interface for unmanned aerial vehicles (UAVs), but directly applying self-hosted computer-use agents (SHCUAs) to UAV control introduces a structural mismatch. SHCUAs are designed for interactive host-side tool use, where delayed agent iterations are often acceptable. UAV control, however, is coupled with continuously changing physical states, strict timing constraints, safety risks, and security accountability. A stale, unauthorized, or tampered agent decision may therefore lead to unsafe or untraceable vehicle behavior. This paper proposes a real-time and security-oriented restructuring of SHCUA-based UAV control. Instead of allowing an SHCUA to directly issue flight commands, we transform its outputs into contract-bound UAV skill invocations with explicit timing, state, authority, fallback, and evidence semantics. Based on this abstraction, we design an architecture that separates semantic reasoning from onboard execution and security/safety enforcement. Slow cloud or edge reasoning is used for mission understanding, while onboard components validate and dispatch only timely, authorized, and state-consistent skills. Security-critical enforcement points can be protected by TEE-style or microcontroller isolation mechanisms without moving the full language agent or high-frequency flight-control loop into trusted components. Prototype evaluation shows that RT-SHCUA maintains bounded task-level responsiveness while supporting degraded handling, trusted admission, and auditable evidence preservation for SHCUA-mediated UAV actions.
