A Hierarchical Agentic Framework for Autonomous Drone-Based Visual Inspection

Abstract

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, we propose a hierarchical agentic framework for autonomous drone guidance, focusing on visual inspection tasks in indoor industrial settings, such as interpreting industrial readouts or inspecting equipment. Our framework 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 the worker agents reason over and execute low-level actions. Operating entirely in the natural language space, the framework 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 task (e.g., locating and reading a pressure gauge). The head agent’s evaluation phase serves as a feedback and/or replanning stage, ensuring the actions executed 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 levels of task complexity and agentic 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 a more autonomous problem-solving approach to industrial inspection tasks without requiring extensive user intervention.