面向GPS拒止建筑环境中自主巡检的智能MAV研发
Developing Smart MAVs for Autonomous Inspection in GPS-denied Constructions
AI总结：
本研究针对GPS拒止建筑环境下无人机人工巡检的弊端，提出带分层感知规划系统的智能MAV巡检框架及增强定位规划的MAV系统，经4000平场馆实验验证其路径执行、运动规划成功率均达100%，还可实现高保真3D重建，推动基建巡检机器人技术发展。
摘要：
智能微型飞行器（MAV）实现了施工各阶段高效、高分辨率的监测，包括难以抵达区域的监测，彻底变革了基础设施巡检方式。在工业设施、基建等GPS拒止环境中，传统人工操作无人机的方式不仅耗费人力、过程繁琐且易出错。本研究针对这类复杂GPS拒止室内环境，提出了一种用于智能MAV巡检的创新框架。该框架具备分层感知与规划系统，可识别感兴趣区域并优化任务路径。研究还提出了一套先进的MAV系统，具备增强的定位与运动规划能力，集成Neural Reconstruction（神经重建）技术，可对建筑结构进行全面三维重建。研究在一个面积4000平方米的室内基础设施场馆中验证了框架的有效性，该场馆内部长80米、宽50米、高7米，主体结构由立柱和墙体组成。实验结果表明，本文的MAV系统在自主巡检任务中表现优异，扫描路径生成与执行的成功率达100%。大量实验验证了所研发MAV的机动性能，运动规划成功率达100%，跟踪误差小于0.1米。此外，采用3D Gaussian Splatting（三维高斯溅射）技术的增强重建方法，可基于采集数据生成高保真渲染模型。总体而言，本文提出的新方法是机器人技术应用于基础设施巡检领域的一项重大进展。
英文摘要：
Smart Micro Aerial Vehicles (MAVs) have transformed infrastructure inspection by enabling efficient, high-resolution monitoring at various stages of construction, including hard-to-reach areas. Traditional manual operation of drones in GPS-denied environments, such as industrial facilities and infrastructure, is labour-intensive, tedious and prone to error. This study presents an innovative framework for smart MAV inspections in such complex and GPS-denied indoor environments. The framework features a hierarchical perception and planning system that identifies regions of interest and optimises task paths. It also presents an advanced MAV system with enhanced localisation and motion planning capabilities, integrated with Neural Reconstruction technology for comprehensive 3D reconstruction of building structures. The effectiveness of the framework was empirically validated in a 4,000 square meters indoor infrastructure facility with an interior length of 80 metres, a width of 50 metres and a height of 7 metres. The main structure consists of columns and walls. Experimental results show that our MAV system performs exceptionally well in autonomous inspection tasks, achieving a 100\% success rate in generating and executing scan paths. Extensive experiments validate the manoeuvrability of our developed MAV, achieving a 100\% success rate in motion planning with a tracking error of less than 0.1 metres. In addition, the enhanced reconstruction method using 3D Gaussian Splatting technology enables the generation of high-fidelity rendering models from the acquired data. Overall, our novel method represents a significant advancement in the use of robotics for infrastructure inspection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
