做机器人或物联网系统架构的开发者，这篇综述帮你理清AI、IoT和机器人三者如何真正融合，避免重复造轮子，值得收藏作为技术路线参考。
这篇综述论文系统梳理了人工智能、物联网与机器人三者融合的现状与挑战。尽管AIoT和IoRT（物联网机器人）已有进展，但缺乏统一的设计框架。论文强调了小语言模型（SLM）在边缘端和大语言模型（LLM）在云端的协同作用，用于分布式认知与自主决策。作者提出模块化系统架构，分析了互操作性和反馈控制方面的持续缺口，并按集成深度对现有工作分类。该工作为构建下一代模块化、可解释、能动态学习的AI-IoT-机器人生态系统提供了概念和技术路线图。
AI-IoT-Robotics Integration: Survey of Frameworks, Emerging Trends, and the Path Toward Connected Robotics
The convergence of Artificial Intelligence, the Internet of Things, and Robotics is no longer a futuristic vision; it is rapidly becoming the foundation of real-time, intelligent, and context-aware systems. AI enables perception and reasoning, IoT provides scalable sensing and communication, and robotics delivers embodied actuation. Despite significant progress in pairwise combinations such as AIoT and the Internet of Robotic Things (IoRT), there remains a lack of unified design frameworks that fully integrate all three. This survey synthesizes the state-of-the-art across these domains, emphasizing the emerging role of Small Language Models (SLMs) at the edge and Large Language Models (LLMs) in the cloud for distributed cognition and autonomous decision-making. We propose a modular system architecture that aligns with these trends, analyze persistent gaps in interoperability and feedback control, and classify existing work by integration depth. Our review highlights how hybrid SLM-LLM systems, when coupled with IoT infrastructure and robotic agents, can address challenges in real-time adaptation, scalability, and reliability. This work offers a conceptual and technical roadmap for designing next-generation AI-IoT-Robotic ecosystems that are modular, interpretable, and capable of learning within dynamic environments, paving the way for the emerging paradigm of Connected Robotics and Physical AI.