Technology gaps in Human-Machine Interfaces for autonomous construction robots

This paper presents the results of a holistic research experiment on the identification of gaps in the current state-of-the-art that hamper the utilization of Unmanned Ground Vehicles (UGVs) in various construction scenarios. The challenge of developing infrastructure with the use of UGVs has common...

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Bibliographic Details
Published in:Automation in construction 2018-10, Vol.94, p.179-190
Main Authors: Czarnowski, Jan, Dąbrowski, Adam, Maciaś, Mateusz, Główka, Jakub, Wrona, Józef
Format: Article
Language:English
Subjects:
Autonomous vehicles
Construction
Human-computer interaction
Infrastructure
Man-machine interfaces
Military
Robots
Unmanned ground vehicles
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Summary:This paper presents the results of a holistic research experiment on the identification of gaps in the current state-of-the-art that hamper the utilization of Unmanned Ground Vehicles (UGVs) in various construction scenarios. The challenge of developing infrastructure with the use of UGVs has common aspects in both civilian and military contexts. The aim of the presented work is to show the influence of UGV Operational Requirements (ORs) on technological aspects of autonomous operation and on UGV Human-Machine Interfaces (HMIs). The authors have based the presented approach on previous experiences in analyzing UGV operations in security and military contexts, where they are widely applied. First, gap identification methodology and UGV Operational Requirements (ORs) are described. Next, analyses of gaps in the area of UGV autonomous operation and gaps in the field of UGV Human-Machine Interfaces (HMIs) are presented. Finally, selected aspects of these gaps are highlighted along with conclusions concerning challenges to meet the requirements. •What are the gaps in human-machine interfaces for autonomous construction robots?•Analysis of the technology gaps and their impact on the industry is presented.•The paper suggests solutions that can be developed to bridge the gaps.
ISSN:0926-5805
1872-7891
DOI:10.1016/j.autcon.2018.06.014