自主无人机集群开放世界决策的认知护栏
Cognitive Guardrails for Open-World Decision Making in Autonomous Drone Swarms
AI总结：
针对自主无人机集群在开放世界搜索救援中传统CV识别能力不足及LLM幻觉风险，本文提出并验证了用于约束高层决策的认知护栏，实现了安全合理的自主任务决策。
摘要：
小型无人航空系统（sUAS）正越来越多地作为自主集群部署于搜索救援及其他灾害响应场景中。在这些场景下，它们利用计算机视觉（CV）检测感兴趣的目标并自主调整任务。然而，传统CV系统在开放世界环境中往往难以识别陌生目标，也难以推断这些目标对任务规划的相关性。为解决这一问题，我们引入大语言模型（LLMs）对检测到的目标及其影响进行推理。尽管LLMs能够提供有价值的见解，但它们也容易产生幻觉，可能给出错误、误导或不安全的建议。为确保不确定性条件下的安全与合理决策，高层决策必须由认知护栏加以约束。本文介绍了这些护栏在搜索救援任务中sUAS集群的设计、仿真与真实世界集成。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
