标题： 自主无人机群开放世界决策的认知护栏 显示英文标题

标题： Cognitive Guardrails for Open-World Decision Making in Autonomous Drone Swarms

摘要： 小型无人飞行系统（sUAS）越来越多地被部署为自主蜂群，在搜索和救援以及其他灾害响应场景中发挥作用。 在这些情况下，它们利用计算机视觉（CV）来检测感兴趣的物体，并自主调整任务。 然而，传统的CV系统常常难以在开放世界环境中识别陌生的物体，或者推断它们对任务规划的相关性。 为了解决这个问题，我们引入了大型语言模型（LLMs）来推理检测到的物体及其影响。 虽然LLMs可以提供有价值的见解，但它们也容易产生幻觉，并可能生成错误、误导性或不安全的建议。 为了确保在不确定性下的安全且明智的决策，高层次的决策必须受到认知护栏的约束。 本文介绍了这些护栏的设计、仿真以及在搜索和救援任务中的小型无人飞行系统蜂群的实际集成。 显示英文摘要

摘要： Small Uncrewed Aerial Systems (sUAS) are increasingly deployed as autonomous swarms in search-and-rescue and other disaster-response scenarios. In these settings, they use computer vision (CV) to detect objects of interest and autonomously adapt their missions. However, traditional CV systems often struggle to recognize unfamiliar objects in open-world environments or to infer their relevance for mission planning. To address this, we incorporate large language models (LLMs) to reason about detected objects and their implications. While LLMs can offer valuable insights, they are also prone to hallucinations and may produce incorrect, misleading, or unsafe recommendations. To ensure safe and sensible decision-making under uncertainty, high-level decisions must be governed by cognitive guardrails. This article presents the design, simulation, and real-world integration of these guardrails for sUAS swarms in search-and-rescue missions.