面向视觉语言赋能的无人机分类与灾害响应的系统工程框架
A Systems Engineering Framework for Vision-Language-Enabled UAV Triage and Disaster Response
- University of Arkansas（阿肯色大学）
- University of Michigan-Dearborn（密歇根大学迪尔伯恩分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究提出将视觉语言模型（VLMs）作为协同智能体嵌入人-无人机回路的系统工程框架，经评估可降低人因负担、提升AI信任度，推进了高风险灾害响应的人-自主系统协同。
AI中文摘要：
视觉语言模型（VLMs）的最新进展为灾害响应创造了新机遇，灾害响应人员需在时间压力下解读大量传感器数据。当前VLM应用包括用于态势感知的社交媒体监测、行动计划草稿生成，以及将技术警报转换为面向公众的消息。尽管这些工作能加速信息流，但大多仍局限于决策支持角色，这类方法会增加操作人员负担，因为人类仍需将输出转化为跨团队及机器人资产的协同行动。本研究探究将VLMs作为协同智能体嵌入人-无人机（UAV）回路的可行性，提出的架构整合了自然语言交互、任务级任务协同、在环软件实现，以及与事故指挥系统（ICS）对齐的通信。VLMs并非仅作为咨询工具，还能促进人类操作人员、任务控制逻辑与无人机任务执行间的通信。该框架采用基于模型的系统工程（MBSE）方法开发，用用例图与块定义图表示系统角色、内部结构及组件交互。在集成仿真与控制环境中实现了三个关键元素：VLM协同智能体、无人机任务控制单元与任务分配器。对7名参与者开展的初步人因评估显示，其在心理需求、精力投入与挫败感方面的感知工作量降低，且对AI信任度与通信清晰度的评分较高。通过整合MBSE、在环软件测试与人因评估，本研究推进了高风险灾害响应场景下可扩展的人-自主系统协同，对航空航天自主系统与民用安全领域具有更广泛的意义。
英文摘要：
Recent advances in Vision Language Models (VLMs) have created new opportunities for disaster response, where responders must interpret large volumes of sensor data under time pressure. Current VLM applications include social media monitoring for situational awareness, generation of draft action plans, and translation of technical alerts into public-facing messages. While these efforts can accelerate information flow, they remain largely limited to decision-support roles. Such approaches can increase operator burden because humans must still translate outputs into coordinated actions across teams and robotic assets. This study explores the viability of embedding VLMs as coordination agents within the human-UAV loop. The proposed architecture integrates natural language interaction, mission-level task coordination, software-in-the-loop implementation, and communication aligned with the Incident Command System (ICS). Rather than functioning solely as advisory tools, VLMs facilitate communication between human operators, mission control logic, and UAV task execution. The framework was developed using a Model-Based Systems Engineering (MBSE) approach, with use case and block definition diagrams representing system roles, internal structure, and component interactions. Three key elements, the VLM Coordinator Agent, UAV Mission Control, and Task Allocator, were implemented within an integrated simulation and control environment. A preliminary human-factors evaluation with seven participants showed reduced perceived workload across mental demand, effort, and frustration, along with high ratings for AI trust and communication clarity. By integrating MBSE, software-in-the-loop testing, and human-factors evaluation, this work advances scalable human-autonomy teaming for high-stakes disaster response, with broader implications for aerospace autonomy and civil safety.
