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Deep-learning-based vision for earth-moving automation
Automation in construction, 2022, Vol.133
[Peer Reviewed Journal]
ISSN: 0926-5805 ;ISSN: 1872-7891 ;EISSN: 1872-7891 ;DOI: 10.1016/j.autcon.2021.104013
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Title:
Deep-learning-based vision for earth-moving automation
Author:
Borngrund, Carl
;
Sandin, Fredrik
;
Bodin, Ulf
Subjects:
Automation
;
Computer vision
;
Construction equipment
;
Cyber-Physical Systems
;
Cyberfysiska system
;
Machine learning
;
Maskininlärning
;
Short-cycle Loading
Is Part Of:
Automation in construction, 2022, Vol.133
Description:
Earth-moving machines are heavy-duty vehicles designed for construction operations involving earthworks. The tasks performed by such machines typically involve navigation and interaction with materials such as soil, gravel, and blasted rock. Skilled operators use a combination of visual, sound, tactile and possibly motion feedback to perform tasks efficiently. We survey the literature in this research area and analyse the relative importance of different sensor system modalities focusing on deep-learning-based vision and automation for the short-cycle loading task. This is a common and repetitive task that is attractive to automate. The analysis indicates that computer vision, in combination with onboard sensors, is more critical than coordinate-based positioning. Furthermore, we find that data-driven approaches, in general, have high potential in terms of productivity, adaptability, versatility and wear and tear with respect to automation system solutions. The main knowledge gaps identified relate to loading non-fine heterogeneous material and navigation during loading and unloading.
Language:
English
Identifier:
ISSN: 0926-5805
ISSN: 1872-7891
EISSN: 1872-7891
DOI: 10.1016/j.autcon.2021.104013
Source:
SWEPUB Freely available online
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