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    Benchmarking Human versus Robot Performance in Emergency Structural Inspection

    Source: Journal of Construction Engineering and Management:;2022:;Volume ( 148 ):;issue: 008::page 04022070
    Author:
    Pengxiang Xia
    ,
    Fang Xu
    ,
    Tianyu Zhou
    ,
    Jing Du
    DOI: 10.1061/(ASCE)CO.1943-7862.0002322
    Publisher: ASCE
    Abstract: Human–robot collaboration (HRC) has gained significant momentum in various civil engineering applications. A representative application is robot-assisted emergency structural inspection, i.e., a human–robot partnered on-site investigation of structural damage and possible causes after any human-caused or natural incidents. Human experts are good at making informed decisions that reflect structural failure patterns, whereas robots can help in searching larger areas and in hazardous places. An effective HRC team would leverage the advantages of human and robot agents. However, deeper insights and direct evidence are still needed to build a baseline model, and in particular to identify the relative advantages and limitations of human and robot agents in emergency structural inspections. To establish a basis for efficient HRC emergency inspection team design, this paper presents a benchmark to compare human and robot performance in a simulated emergency structural inspection task following an earthquake disaster. The total amount of identified structural damage, inspection time, and route patterns of humans and robots are compared. The results show that humans outperformed autonomous robots in most inspection metrics, possibly owing to prior knowledge about the patterns of structural failure, and presented a different but more efficient route pattern. The findings are expected to inspire better HRC team design for emergency structural inspection tasks and other relevant knowledge-based HRC tasks.
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      Benchmarking Human versus Robot Performance in Emergency Structural Inspection

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    contributor authorPengxiang Xia
    contributor authorFang Xu
    contributor authorTianyu Zhou
    contributor authorJing Du
    date accessioned2022-08-18T12:10:18Z
    date available2022-08-18T12:10:18Z
    date issued2022/06/02
    identifier other%28ASCE%29CO.1943-7862.0002322.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4286130
    description abstractHuman–robot collaboration (HRC) has gained significant momentum in various civil engineering applications. A representative application is robot-assisted emergency structural inspection, i.e., a human–robot partnered on-site investigation of structural damage and possible causes after any human-caused or natural incidents. Human experts are good at making informed decisions that reflect structural failure patterns, whereas robots can help in searching larger areas and in hazardous places. An effective HRC team would leverage the advantages of human and robot agents. However, deeper insights and direct evidence are still needed to build a baseline model, and in particular to identify the relative advantages and limitations of human and robot agents in emergency structural inspections. To establish a basis for efficient HRC emergency inspection team design, this paper presents a benchmark to compare human and robot performance in a simulated emergency structural inspection task following an earthquake disaster. The total amount of identified structural damage, inspection time, and route patterns of humans and robots are compared. The results show that humans outperformed autonomous robots in most inspection metrics, possibly owing to prior knowledge about the patterns of structural failure, and presented a different but more efficient route pattern. The findings are expected to inspire better HRC team design for emergency structural inspection tasks and other relevant knowledge-based HRC tasks.
    publisherASCE
    titleBenchmarking Human versus Robot Performance in Emergency Structural Inspection
    typeJournal Article
    journal volume148
    journal issue8
    journal titleJournal of Construction Engineering and Management
    identifier doi10.1061/(ASCE)CO.1943-7862.0002322
    journal fristpage04022070
    journal lastpage04022070-13
    page13
    treeJournal of Construction Engineering and Management:;2022:;Volume ( 148 ):;issue: 008
    contenttypeFulltext
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