一种用于自主无人机视觉检测的分层智能体框架
A Hierarchical Agentic Framework for Autonomous Drone-Based Visual Inspection
AI总结：
本研究提出一种用于室内工业视觉检测的分层无人机智能体框架及ReActEval推理方法，通过总控与工作智能体的分工协作，以自然语言交互实现自主检测，为传统方案提供灵活易用的新选择。
摘要：
自主检测系统对于保障工业资产的性能和使用寿命至关重要。近年来，智能体框架在自动化检测工作流方面展现出巨大潜力，但目前仅局限于数字任务，其在真实环境中实体资产上的应用仍有待深入探索。\n本研究的贡献分为两方面：第一，我们提出了一种用于自主无人机控制的分层智能体框架；第二，提出了一种用于单个功能执行的推理方法，我们将其命名为ReActEval。该框架聚焦于室内工业场景下的视觉检测任务，例如解读工业读数或检测设备。它采用多智能体系统，由一个总控智能体和多个工作智能体组成，每个工作智能体控制一架无人机。总控智能体负责高层规划并评估执行结果，工作智能体则运用ReActEval对底层动作进行推理和执行。\nReActEval完全以自然语言运行，遵循“规划-推理-执行-评估”的循环流程，使无人机能够处理从简单导航（例如向前飞行10米并降落）到复杂高层任务（例如定位并读取压力表数值）的各类任务。评估阶段作为反馈和/或重新规划环节，确保动作符合用户目标，同时避免不良结果的产生。\n我们在包含两个工作智能体的仿真环境中对该框架进行了评估，基于不同复杂度水平下的任务完成情况和工作流效率，从定性和定量两个维度评估其性能。通过利用自然语言处理实现智能体间的通信，我们的方法为传统无人机解决方案提供了一种新颖、灵活且用户易上手的替代方案，无需大量用户干预即可实现工业检测的自主问题求解。
英文摘要：
Autonomous inspection systems are essential for ensuring the performance and longevity of industrial assets. Recently, agentic frameworks have demonstrated significant potential for automating inspection workflows but have been limited to digital tasks. Their application to physical assets in real-world environments, however, remains underexplored. In this work, our contributions are two-fold: first, we propose a hierarchical agentic framework for autonomous drone control, and second, a reasoning methodology for individual function executions which we refer to as ReActEval. Our framework focuses on visual inspection tasks in indoor industrial settings, such as interpreting industrial readouts or inspecting equipment. It employs a multi-agent system comprising a head agent and multiple worker agents, each controlling a single drone. The head agent performs high-level planning and evaluates outcomes, while worker agents implement ReActEval to reason over and execute low-level actions. Operating entirely in natural language, ReActEval follows a plan, reason, act, evaluate cycle, enabling drones to handle tasks ranging from simple navigation (e.g., flying forward 10 meters and land) to complex high-level tasks (e.g., locating and reading a pressure gauge). The evaluation phase serves as a feedback and/or replanning stage, ensuring actions align with user objectives while preventing undesirable outcomes. We evaluate the framework in a simulated environment with two worker agents, assessing performance qualitatively and quantitatively based on task completion across varying complexity levels and workflow efficiency. By leveraging natural language processing for agent communication, our approach offers a novel, flexible, and user-accessible alternative to traditional drone-based solutions, enabling autonomous problem-solving for industrial inspection without extensive user intervention.
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