Off-Road Detection Analysis for Autonomous Ground Vehicles: A Review.

autonomous ground vehicles drivable ground negative obstacles off-road environment positive obstacles

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
03 Nov 2022
Historique:
received: 21 09 2022
revised: 28 10 2022
accepted: 31 10 2022
entrez: 11 11 2022
pubmed: 12 11 2022
medline: 15 11 2022
Statut: epublish

Résumé

When it comes to some essential abilities of autonomous ground vehicles (AGV), detection is one of them. In order to safely navigate through any known or unknown environment, AGV must be able to detect important elements on the path. Detection is applicable both on-road and off-road, but they are much different in each environment. The key elements of any environment that AGV must identify are the drivable pathway and whether there are any obstacles around it. Many works have been published focusing on different detection components in various ways. In this paper, a survey of the most recent advancements in AGV detection methods that are intended specifically for the off-road environment has been presented. For this, we divided the literature into three major groups: drivable ground and positive and negative obstacles. Each detection portion has been further divided into multiple categories based on the technology used, for example, single sensor-based, multiple sensor-based, and how the data has been analyzed. Furthermore, it has added critical findings in detection technology, challenges associated with detection and off-road environment, and possible future directions. Authors believe this work will help the reader in finding literature who are doing similar works.

Identifiants

pubmed: 36366160
pii: s22218463
doi: 10.3390/s22218463
pmc: PMC9657584
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Références

Sensors (Basel). 2021 May 05;21(9):
pubmed: 34063133
Sensors (Basel). 2020 Sep 05;20(18):
pubmed: 32899515
Sensors (Basel). 2022 Oct 28;22(21):
pubmed: 36365964
Sensors (Basel). 2020 Dec 25;21(1):
pubmed: 33375609
Sensors (Basel). 2021 Mar 18;21(6):
pubmed: 33803889
Methodist Debakey Cardiovasc J. 2022 Mar 14;18(2):114-116
pubmed: 35414853
Sensors (Basel). 2022 Sep 16;22(18):
pubmed: 36146359

Auteurs

Fahmida Islam (F)

Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA.

M M Nabi (MM)

Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA.

John E Ball (JE)

Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA.

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Classifications MeSH