A Deep Dive into Robot Vision - An Integrative Systematic Literature Review Methodologies and Research Endeavor Practices

Sultana, S; Alam, MM; Mohd Su’Ud, M; Mustapha, JC; Prasad, M

A Deep Dive into Robot Vision - An Integrative Systematic Literature Review Methodologies and Research Endeavor Practices

Sultana, S Alam, MM Mohd Su’Ud, M Mustapha, JC Prasad, M

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Publisher:: ASSOC COMPUTING MACHINERY
Publication Type:: Journal Article
Citation:: ACM Computing Surveys, 2024, 56, (9)
Issue Date:: 2024-10-31

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Field	Value	Language
dc.contributor.author	Sultana, S
dc.contributor.author	Alam, MM
dc.contributor.author	Mohd Su’Ud, M
dc.contributor.author	Mustapha, JC
dc.contributor.author	Prasad, M
dc.date.accessioned	2025-03-05T05:17:35Z
dc.date.available	2025-03-05T05:17:35Z
dc.date.issued	2024-10-31
dc.identifier.citation	ACM Computing Surveys, 2024, 56, (9)
dc.identifier.issn	0360-0300
dc.identifier.issn	1557-7341
dc.identifier.uri	http://hdl.handle.net/10453/185517
dc.description.abstract	Novel technological swarm and industry 4.0 mold the recent Robot vision research into innovative discovery. To enhance technological paradigm Deep Learning offers remarkable pace to move towards diversified advancement. This research considers the most topical, recent, related and state-of-the-art research reviews that revolve around Robot vision, and shapes the research into Systematic Literature Survey SLR. The SLR considers a combination of more than 100 reviews and empirical studies to perform a critical categorical study and shapes findings against research questions. The research study contribution spans over multiple categories of Robot vision and is tinted along with technical limitations and future research endeavors. Previously multiple research studies have been observed to leverage Robotic vision techniques. Yet, there is none like SLR summarizing recent vision techniques for all targeted Robotic fields. This research SLR could be a precious milestone in Robot vision for each glimpse of Robotics.
dc.language	English
dc.publisher	ASSOC COMPUTING MACHINERY
dc.relation.ispartof	ACM Computing Surveys
dc.relation.isbasedon	10.1145/3648357
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences
dc.subject.classification	Information Systems
dc.subject.classification	46 Information and computing sciences
dc.title	A Deep Dive into Robot Vision - An Integrative Systematic Literature Review Methodologies and Research Endeavor Practices
dc.type	Journal Article
utslib.citation.volume	56
utslib.for	08 Information and Computing Sciences
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Australian Artificial Intelligence Institute (AAII)
pubs.organisational-group	University of Technology Sydney/UTS Groups/The Trustworthy Digital Society
utslib.copyright.status	closed_access	*
dc.date.updated	2025-03-05T05:17:31Z
pubs.issue	9
pubs.publication-status	Published
pubs.volume	56
utslib.citation.issue	9

Abstract:

Novel technological swarm and industry 4.0 mold the recent Robot vision research into innovative discovery. To enhance technological paradigm Deep Learning offers remarkable pace to move towards diversified advancement. This research considers the most topical, recent, related and state-of-the-art research reviews that revolve around Robot vision, and shapes the research into Systematic Literature Survey SLR. The SLR considers a combination of more than 100 reviews and empirical studies to perform a critical categorical study and shapes findings against research questions. The research study contribution spans over multiple categories of Robot vision and is tinted along with technical limitations and future research endeavors. Previously multiple research studies have been observed to leverage Robotic vision techniques. Yet, there is none like SLR summarizing recent vision techniques for all targeted Robotic fields. This research SLR could be a precious milestone in Robot vision for each glimpse of Robotics.

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http://hdl.handle.net/10453/185517