一种将多模态人类意图转化为无人机安全机动的中介模型
A Model for Mediating Multi-Modal Human Intent into Safe Maneuvers for UAVs
- University of Notre Dame（诺丁汉大学）
- Imperial College London（伦敦帝国理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究如何将多模态人类意图转化为无人机安全机动，提出需求导向的机动响应模型，通过结构化管道处理操作员输入，经多种约束验证后执行，还形式化为此类规范模型，经实验室验证可可靠解释并安全执行相关输入。
AI中文摘要：
通过语音、手势和图形界面等方式可实现人类与自主无人机系统的直接交互。但将此类输入直接作为可执行命令会在动态环境中带来安全风险。本文提出一种需求导向的机动响应模型，将多模态人类意图转化为安全的无人机机动。把操作员输入视为有界机动请求，映射到受限运动原语，经结构化请求-评估-执行管道处理。每个请求都有置信度，针对多种约束进行验证，在持续运行监控下进行约束、拒绝或执行。还将该方法形式化为基于需求的规范模型，支持运行时验证等。通过基于实验室的初步验证，表明基于语音和GUI的输入能可靠解释并安全执行。
英文摘要：
Direct human interaction with autonomous UAV systems can be enabled through modalities such as speech, gestures, and graphical interfaces. However, interpreting such inputs as directly executable commands introduces safety risks in dynamic environments. Operator requests may conflict with terrain constraints, inter-UAV separation requirements, or flight-envelope limitations. In this paper, we present a requirements-governed maneuver-response model that mediates multi-modal human intent into safe UAV maneuvers by treating operator inputs as bounded maneuver requests rather than direct commands. Requested maneuvers are mapped to constrained motion primitives and processed through a structured request-evaluate-execute pipeline. Each request is interpreted with associated confidence, validated against terrain, separation, workspace, and flight-envelope constraints, and either constrained, rejected, or executed under continuous runtime monitoring. We further formalize the approach as a requirements-based specification model in which maneuver primitives are associated with explicit preconditions, invariants, guard conditions, and postconditions governing admissibility, execution safety, and emergency handling. These requirements support runtime verification and future reactive synthesis approaches. We present an initial lab-based validation demonstrating that voice and GUI-based inputs can be reliably interpreted and safely executed as constrained maneuver requests.
