Efficient 3D robotic mapping and navigation method in complex construction environments

IF 8.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-10-09 DOI:10.1111/mice.13353

Tianyu Ren, Houtan Jebelli

{"title":"Efficient 3D robotic mapping and navigation method in complex construction environments","authors":"Tianyu Ren, Houtan Jebelli","doi":"10.1111/mice.13353","DOIUrl":null,"url":null,"abstract":"Recent advancements in construction robotics have significantly transformed the construction industry by delivering safer and more efficient solutions for handling complex and hazardous tasks. Despite these innovations, ensuring safe robotic navigation in intricate indoor construction environments, such as attics, remains a significant challenge. This study introduces a robust 3‐dimensional (3D) robotic mapping and navigation method specifically tailored for these environments. Utilizing light detection and ranging, simultaneous localization and mapping, and neural networks, this method generates precise 3D maps. It also combines grid‐based pathfinding with deep reinforcement learning to enhance navigation and obstacle avoidance in dynamic and complex construction settings. An evaluation conducted in a simulated attic environment—characterized by various truss structures and continuously changing obstacles—affirms the method's efficacy. Compared to established benchmarks, this method not only achieves over 95% mapping accuracy but also improves navigation accuracy by 10% and boosts both efficiency and safety margins by over 30%.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"25 1","pages":""},"PeriodicalIF":8.5000,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer-Aided Civil and Infrastructure Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1111/mice.13353","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}

引用次数: 0

Abstract

Recent advancements in construction robotics have significantly transformed the construction industry by delivering safer and more efficient solutions for handling complex and hazardous tasks. Despite these innovations, ensuring safe robotic navigation in intricate indoor construction environments, such as attics, remains a significant challenge. This study introduces a robust 3‐dimensional (3D) robotic mapping and navigation method specifically tailored for these environments. Utilizing light detection and ranging, simultaneous localization and mapping, and neural networks, this method generates precise 3D maps. It also combines grid‐based pathfinding with deep reinforcement learning to enhance navigation and obstacle avoidance in dynamic and complex construction settings. An evaluation conducted in a simulated attic environment—characterized by various truss structures and continuously changing obstacles—affirms the method's efficacy. Compared to established benchmarks, this method not only achieves over 95% mapping accuracy but also improves navigation accuracy by 10% and boosts both efficiency and safety margins by over 30%.

查看原文本刊更多论文

复杂建筑环境中的高效 3D 机器人绘图和导航方法

建筑机器人技术的最新进展极大地改变了建筑行业，为处理复杂而危险的任务提供了更安全、更高效的解决方案。尽管有了这些创新，但确保机器人在阁楼等错综复杂的室内建筑环境中安全导航仍是一项重大挑战。本研究介绍了一种专为这些环境定制的强大的三维（3D）机器人绘图和导航方法。该方法利用光探测和测距、同步定位和绘图以及神经网络，生成精确的三维地图。它还将基于网格的寻路与深度强化学习相结合，以增强在动态和复杂的建筑环境中的导航和避障能力。在以各种桁架结构和不断变化的障碍物为特征的模拟阁楼环境中进行的评估证实了该方法的有效性。与既定基准相比，该方法不仅实现了 95% 以上的绘图准确率，还将导航准确率提高了 10%，并将效率和安全系数提高了 30% 以上。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Computer-Aided Civil and Infrastructure Engineering 工程技术-工程：土木

CiteScore

17.60

自引率

19.80%

发文量

146

审稿时长

1 months

期刊介绍： Computer-Aided Civil and Infrastructure Engineering stands as a scholarly, peer-reviewed archival journal, serving as a vital link between advancements in computer technology and civil and infrastructure engineering. The journal serves as a distinctive platform for the publication of original articles, spotlighting novel computational techniques and inventive applications of computers. Specifically, it concentrates on recent progress in computer and information technologies, fostering the development and application of emerging computing paradigms. Encompassing a broad scope, the journal addresses bridge, construction, environmental, highway, geotechnical, structural, transportation, and water resources engineering. It extends its reach to the management of infrastructure systems, covering domains such as highways, bridges, pavements, airports, and utilities. The journal delves into areas like artificial intelligence, cognitive modeling, concurrent engineering, database management, distributed computing, evolutionary computing, fuzzy logic, genetic algorithms, geometric modeling, internet-based technologies, knowledge discovery and engineering, machine learning, mobile computing, multimedia technologies, networking, neural network computing, optimization and search, parallel processing, robotics, smart structures, software engineering, virtual reality, and visualization techniques.