A comprehensive survey of unmanned ground vehicle terrain traversability for unstructured environments and sensor technology insights

Simple item page
dc.contributor.authorBeyƧimen, Semih
dc.contributor.authorIgnatyev, Dmitry
dc.contributor.authorZolotas, Argyrios
dc.date.accessioned2023-10-09T11:51:42Z
dc.date.available2023-10-09T11:51:42Z
dc.date.issued2023-09-29
dc.description.abstractThis article provides a detailed analysis of the assessment of unmanned ground vehicle terrain traversability. The analysis is categorized into terrain classification, terrain mapping, and cost-based traversability, with subcategories of appearance-based, geometry-based, and mixed-based methods. The article also explores the use of machine learning (ML), deep learning (DL) and reinforcement learning (RL) and other based end-to-end methods as crucial components for advanced terrain traversability analysis. The investigation indicates that a mixed approach, incorporating both exteroceptive and proprioceptive sensors, is more effective, optimized, and reliable for traversability analysis. Additionally, the article discusses the vehicle platforms and sensor technologies used in traversability analysis, making it a valuable resource for researchers in the field. Overall, this paper contributes significantly to the current understanding of traversability analysis in unstructured environments and provides insights for future sensor-based research on advanced traversability analysis.en_UK
dc.identifier.citationBeyƧimen S, Ignatyev D, Zolotas A. (2023) A comprehensive survey of unmanned ground vehicle terrain traversability for unstructured environments and sensor technology insights, Engineering Science and Technology, an International Journal, Volume 47, November 2023, Article Number 101457en_UK
dc.identifier.issn2215-0986
dc.identifier.urihttps://doi.org/10.1016/j.jestch.2023.101457
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20346
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectArtificial intelligenceen_UK
dc.subjectAutonomous vehiclesen_UK
dc.subjectData fusionen_UK
dc.subjectData processingen_UK
dc.subjectMachine learningen_UK
dc.subjectOff-roaden_UK
dc.subjectSensorsen_UK
dc.subjectUnstructured environmenten_UK
dc.subjectTerrain traversabilityen_UK
dc.titleA comprehensive survey of unmanned ground vehicle terrain traversability for unstructured environments and sensor technology insightsen_UK
dc.typeArticleen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Unmanned_ground_vehicle_terrain_traversability-2023.pdf
Size:
4.15 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SATM)