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- W4387204732 startingPage "101457" @default.
- W4387204732 abstract "This 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." @default.
- W4387204732 created "2023-09-30" @default.
- W4387204732 creator A5038662808 @default.
- W4387204732 creator A5058603607 @default.
- W4387204732 creator A5089252134 @default.
- W4387204732 date "2023-11-01" @default.
- W4387204732 modified "2023-10-14" @default.
- W4387204732 title "A comprehensive survey of unmanned ground vehicle terrain traversability for unstructured environments and sensor technology insights" @default.
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- W4387204732 doi "https://doi.org/10.1016/j.jestch.2023.101457" @default.
- W4387204732 hasPublicationYear "2023" @default.
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