用大模型+认知护栏让无人机群在灾场自主决策更安全
Cognitive Guardrails for Open-World Decision Making in Autonomous Drone Swarms
- 引入大语言模型理解陌生目标，提升开放环境感知能力
- 通过认知护栏机制避免大模型幻觉导致误判，保障任务安全
- 已在仿真与真实场景验证，适合灾后搜救等高风险应用
小型无人航空系统（sUAS）正越来越多地以自主蜂群形式应用于搜救及其他灾难响应场景。在这些情境中，它们利用计算机视觉（CV）检测感兴趣目标并自主调整任务。然而，传统CV系统在开放世界环境中难以识别陌生物体，也无法推断其对任务规划的相关性。为此，本文引入大语言模型（LLMs）来推理检测到的目标及其潜在影响。尽管LLMs能提供有价值的见解，但易产生幻觉，可能导致错误、误导或不安全的建议。为确保在不确定性下的安全合理决策，高层决策必须受到认知护栏的约束。本文提出并实现了sUAS蜂群在搜救任务中认知护栏的设计、仿真与真实世界集成。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。