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Highway digital twin-enabled Autonomous Maintenance Plant: a perspective
The importance of automating pavement maintenance tasks for highway systems has garnered interest from both industry and academia. Despite significant research efforts and promising demonstrations being devoted to reaching a level of semi-automation featuring digital sensing and inspection, site mai...
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| Published in: | Data-Centric Engineering (Online) 2024-01, Vol.5, Article e24 |
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| creator | Xu, Jie Anvo, N’Zebo R. Taha-Abdalgadir, Hussameldin d’Avigneau, Alix M. Palin, Damian Wei, Ran Hadjidemetriou, Georgios Schaefer, Samuel de Silva, Lavindra Al-Tabbaa, Abir Iida, Fumiya Brilakis, Ioannis |
| description | The importance of automating pavement maintenance tasks for highway systems has garnered interest from both industry and academia. Despite significant research efforts and promising demonstrations being devoted to reaching a level of semi-automation featuring digital sensing and inspection, site maintenance work still requires manual processes using special vehicles and equipment, reflecting a clear gap to transition to fully autonomous maintenance. This paper reviews the current progress in pavement maintenance automation in terms of inspection and repair operations, followed by a discussion of three key technical challenges related to robotic sensing, control, and actuation. To address these challenges, we propose a conceptual solution we term Autonomous Maintenance Plant (AMP), mainly consisting of five modules for sensing, actuation, control, power supply, and mobility. This AMP concept is part of the “Digital Roads” project’s cyber-physical platform where a road digital twin (DT) is created based on its physical counterpart to enable real-time condition monitoring, sensory data processing, maintenance decision making, and repair operation execution. In this platform, the AMP conducts high-resolution survey and autonomous repair operations enabled (instructed) by the road DT. This process is unmanned and completely autonomous with an expectation to create a fully robotized highway pavement maintenance system. |
| doi_str_mv | 10.1017/dce.2024.34 |
| format | article |
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Despite significant research efforts and promising demonstrations being devoted to reaching a level of semi-automation featuring digital sensing and inspection, site maintenance work still requires manual processes using special vehicles and equipment, reflecting a clear gap to transition to fully autonomous maintenance. This paper reviews the current progress in pavement maintenance automation in terms of inspection and repair operations, followed by a discussion of three key technical challenges related to robotic sensing, control, and actuation. To address these challenges, we propose a conceptual solution we term Autonomous Maintenance Plant (AMP), mainly consisting of five modules for sensing, actuation, control, power supply, and mobility. This AMP concept is part of the “Digital Roads” project’s cyber-physical platform where a road digital twin (DT) is created based on its physical counterpart to enable real-time condition monitoring, sensory data processing, maintenance decision making, and repair operation execution. In this platform, the AMP conducts high-resolution survey and autonomous repair operations enabled (instructed) by the road DT. This process is unmanned and completely autonomous with an expectation to create a fully robotized highway pavement maintenance system.</description><identifier>ISSN: 2632-6736</identifier><identifier>EISSN: 2632-6736</identifier><identifier>DOI: 10.1017/dce.2024.34</identifier><language>eng</language><publisher>Cambridge: Cambridge University Press</publisher><subject>Accuracy ; Actuation ; Algorithms ; Artificial intelligence ; Automation ; autonomous vehicle ; Condition monitoring ; Control equipment ; Cracks ; cyber-physical platform ; Data processing ; Digital twins ; Drones ; Inspection ; inspection automation ; Literature reviews ; Materials handling ; Neural networks ; pavement maintenance ; Pavements ; Real time ; Repair ; repair automation ; road infrastructure ; Road maintenance ; Roads & highways ; Robot control ; Robot sensors ; Robotics ; Robots ; Sealing compounds ; Unmanned aerial vehicles ; Vehicles</subject><ispartof>Data-Centric Engineering (Online), 2024-01, Vol.5, Article e24</ispartof><rights>The Author(s), 2024. Published by Cambridge University Press. This work is licensed under the Creative Commons Attribution License This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. (the “License”). 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This AMP concept is part of the “Digital Roads” project’s cyber-physical platform where a road digital twin (DT) is created based on its physical counterpart to enable real-time condition monitoring, sensory data processing, maintenance decision making, and repair operation execution. In this platform, the AMP conducts high-resolution survey and autonomous repair operations enabled (instructed) by the road DT. 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Anvo, N’Zebo R. ; Taha-Abdalgadir, Hussameldin ; d’Avigneau, Alix M. ; Palin, Damian ; Wei, Ran ; Hadjidemetriou, Georgios ; Schaefer, Samuel ; de Silva, Lavindra ; Al-Tabbaa, Abir ; Iida, Fumiya ; Brilakis, Ioannis</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c252t-dd3f0c7eb6ecc1aa2eb0862dbf9dcd8f0d8fded372fe65563084b4166832fa463</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Accuracy</topic><topic>Actuation</topic><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Automation</topic><topic>autonomous vehicle</topic><topic>Condition monitoring</topic><topic>Control equipment</topic><topic>Cracks</topic><topic>cyber-physical platform</topic><topic>Data processing</topic><topic>Digital twins</topic><topic>Drones</topic><topic>Inspection</topic><topic>inspection automation</topic><topic>Literature reviews</topic><topic>Materials handling</topic><topic>Neural networks</topic><topic>pavement maintenance</topic><topic>Pavements</topic><topic>Real time</topic><topic>Repair</topic><topic>repair automation</topic><topic>road infrastructure</topic><topic>Road maintenance</topic><topic>Roads & highways</topic><topic>Robot control</topic><topic>Robot sensors</topic><topic>Robotics</topic><topic>Robots</topic><topic>Sealing compounds</topic><topic>Unmanned aerial vehicles</topic><topic>Vehicles</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Xu, Jie</creatorcontrib><creatorcontrib>Anvo, N’Zebo R.</creatorcontrib><creatorcontrib>Taha-Abdalgadir, Hussameldin</creatorcontrib><creatorcontrib>d’Avigneau, Alix M.</creatorcontrib><creatorcontrib>Palin, Damian</creatorcontrib><creatorcontrib>Wei, Ran</creatorcontrib><creatorcontrib>Hadjidemetriou, Georgios</creatorcontrib><creatorcontrib>Schaefer, Samuel</creatorcontrib><creatorcontrib>de Silva, Lavindra</creatorcontrib><creatorcontrib>Al-Tabbaa, Abir</creatorcontrib><creatorcontrib>Iida, Fumiya</creatorcontrib><creatorcontrib>Brilakis, Ioannis</creatorcontrib><collection>CrossRef</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Data-Centric Engineering (Online)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Xu, Jie</au><au>Anvo, N’Zebo R.</au><au>Taha-Abdalgadir, Hussameldin</au><au>d’Avigneau, Alix M.</au><au>Palin, Damian</au><au>Wei, Ran</au><au>Hadjidemetriou, Georgios</au><au>Schaefer, Samuel</au><au>de Silva, Lavindra</au><au>Al-Tabbaa, Abir</au><au>Iida, Fumiya</au><au>Brilakis, Ioannis</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Highway digital twin-enabled Autonomous Maintenance Plant: a perspective</atitle><jtitle>Data-Centric Engineering (Online)</jtitle><date>2024-01-01</date><risdate>2024</risdate><volume>5</volume><artnum>e24</artnum><issn>2632-6736</issn><eissn>2632-6736</eissn><abstract>The importance of automating pavement maintenance tasks for highway systems has garnered interest from both industry and academia. Despite significant research efforts and promising demonstrations being devoted to reaching a level of semi-automation featuring digital sensing and inspection, site maintenance work still requires manual processes using special vehicles and equipment, reflecting a clear gap to transition to fully autonomous maintenance. This paper reviews the current progress in pavement maintenance automation in terms of inspection and repair operations, followed by a discussion of three key technical challenges related to robotic sensing, control, and actuation. To address these challenges, we propose a conceptual solution we term Autonomous Maintenance Plant (AMP), mainly consisting of five modules for sensing, actuation, control, power supply, and mobility. 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| source | Cambridge Journals Online; ProQuest - Publicly Available Content Database |
| subjects | Accuracy Actuation Algorithms Artificial intelligence Automation autonomous vehicle Condition monitoring Control equipment Cracks cyber-physical platform Data processing Digital twins Drones Inspection inspection automation Literature reviews Materials handling Neural networks pavement maintenance Pavements Real time Repair repair automation road infrastructure Road maintenance Roads & highways Robot control Robot sensors Robotics Robots Sealing compounds Unmanned aerial vehicles Vehicles |
| title | Highway digital twin-enabled Autonomous Maintenance Plant: a perspective |
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