Artifical Intelligence & Automation

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Overview

Automation is the practice of using machines and control systems to execute tasks with minimal human intervention, relying on control theory to ensure precision, stability, and repeatable performance. In practical applications, control theory governs the dynamic behavior of robotic platforms, utilizing feedback control loops to maintain stable flight, position, and trajectory even in the presence of disturbances such as wind gusts or uneven terrain. This ensures that robotic inspectors can navigate complex structures with accuracy and consistency, capturing data under a broad range of operating conditions. Artificial intelligence (AI), especially advanced techniques like large language models (LLMs) and context-aware reasoning, adds higher-level cognitive capabilities to these automated systems. AI enables robots to interpret sensor data, understand their surroundings, and infer task requirements, allowing them to adapt to unstructured or evolving environments. LLMs further enhance these capabilities by facilitating the processing of textual information, integrating engineering standards, and responding intelligently to operator inputs in natural language. Together, control theory provides the stable and responsive foundation for automation, while AI and context-aware reasoning imbue robotic inspectors with autonomy and adaptability. This synergy enables robotic systems to conduct highly informed, flexible, and thorough structural assessments in dynamic, real-world settings.