Advancements in learning-based navigation systems for robotic applications in MRO hangar: review

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dc.contributor.authorAdiuku, Ndidiamaka
dc.contributor.authorAvdelidis, Nicolas P.
dc.contributor.authorTang, Gilbert
dc.contributor.authorPlastropoulos, Angelos
dc.date.accessioned2024-03-18T14:54:56Z
dc.date.available2024-03-18T14:54:56Z
dc.date.issued2024-02-21
dc.description.abstractThe field of learning-based navigation for mobile robots is experiencing a surge of interest from research and industry sectors. The application of this technology for visual aircraft inspection tasks within a maintenance, repair, and overhaul (MRO) hangar necessitates efficient perception and obstacle avoidance capabilities to ensure a reliable navigation experience. The present reliance on manual labour, static processes, and outdated technologies limits operation efficiency in the inherently dynamic and increasingly complex nature of the real-world hangar environment. The challenging environment limits the practical application of conventional methods and real-time adaptability to changes. In response to these challenges, recent years research efforts have witnessed advancement with machine learning integration aimed at enhancing navigational capability in both static and dynamic scenarios. However, most of these studies have not been specific to the MRO hangar environment, but related challenges have been addressed, and applicable solutions have been developed. This paper provides a comprehensive review of learning-based strategies with an emphasis on advancements in deep learning, object detection, and the integration of multiple approaches to create hybrid systems. The review delineates the application of learning-based methodologies to real-time navigational tasks, encompassing environment perception, obstacle detection, avoidance, and path planning through the use of vision-based sensors. The concluding section addresses the prevailing challenges and prospective development directions in this domain.en_UK
dc.identifier.citationAdiuku N, Avdelidis NP, Tang G, Plastropoulos A. (2024) Advancements in learning-based navigation systems for robotic applications in MRO hangar: review. Sensors, Volume 24, Issue 5, February 2024, Article number 1377en_UK
dc.identifier.issn1424-8220
dc.identifier.urihttps://doi.org/10.3390/s24051377
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/21025
dc.language.isoen_UKen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectroboticsen_UK
dc.subjectmachine learningen_UK
dc.subjectMRO hangaren_UK
dc.subjectrobot navigationen_UK
dc.subjectobject detectionen_UK
dc.subjectdeep learningen_UK
dc.titleAdvancements in learning-based navigation systems for robotic applications in MRO hangar: reviewen_UK
dc.typeArticleen_UK
dcterms.dateAccepted2024-02-19

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