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使用自主机器人系统检测民用基础设施中的裂缝:平台、认知与自主行动的协同综述

Crack Detection in Civil Infrastructure Using Autonomous Robotic Systems: A Synergistic Review of Platforms, Cognition, and Autonomous Action.

作者信息

Dai Rong, Wang Rui, Shu Chang, Li Jianming, Wei Zhe

机构信息

School of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, China.

出版信息

Sensors (Basel). 2025 Jul 26;25(15):4631. doi: 10.3390/s25154631.

Abstract

Traditional manual crack inspection methods often face limitations in terms of efficiency, safety, and consistency. To overcome these issues, a new approach based on autonomous robotic systems has gained attention, combining robotics, artificial intelligence, and advanced sensing technologies. However, most existing reviews focus on individual components in isolation and fail to present a complete picture of how these systems work together. This study focuses on robotic crack detection and proposes a structured framework that connects three core modules: the physical platform (robots and sensors), the cognitive core (crack detection algorithms), and autonomous action (navigation and planning). We analyze key technologies, their interactions, and the challenges involved in real-world implementation. The aim is to provide a clear roadmap of current progress and future directions, helping researchers and engineers better understand the field and develop smart, deployable systems for infrastructure crack inspection.

摘要

传统的人工裂缝检测方法在效率、安全性和一致性方面常常面临局限。为克服这些问题,一种基于自主机器人系统的新方法受到关注,该方法结合了机器人技术、人工智能和先进传感技术。然而,大多数现有综述孤立地关注各个组件,未能全面呈现这些系统如何协同工作。本研究聚焦于机器人裂缝检测,并提出一个结构化框架,该框架连接三个核心模块:物理平台(机器人和传感器)、认知核心(裂缝检测算法)和自主行动(导航与规划)。我们分析关键技术、它们之间的相互作用以及实际应用中涉及的挑战。目的是提供当前进展和未来方向的清晰路线图,帮助研究人员和工程师更好地理解该领域,并开发用于基础设施裂缝检测的智能、可部署系统。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d636/12349540/a1a9f4fec69b/sensors-25-04631-g001.jpg

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