Rapid global path planning algorithm for unmanned surface vehicles in large-scale and multi-island marine environments.

Fast marching method Fast marching square Global path planning Time optimal Unmanned surface vehicle

Journal

PeerJ. Computer science
ISSN: 2376-5992
Titre abrégé: PeerJ Comput Sci
Pays: United States
ID NLM: 101660598

Informations de publication

Date de publication:
2021
Historique:
received: 16 02 2021
accepted: 04 06 2021
entrez: 26 7 2021
pubmed: 27 7 2021
medline: 27 7 2021
Statut: epublish

Résumé

A global path planning algorithm for unmanned surface vehicles (USVs) with short time requirements in large-scale and complex multi-island marine environments is proposed. The fast marching method-based path planning for USVs is performed on grid maps, resulting in a decrease in computer efficiency for larger maps. This can be mitigated by improving the algorithm process. In the proposed algorithm, path planning is performed twice in maps with different spatial resolution (SR) grids. The first path planning is performed in a low SR grid map to determine effective regions, and the second is executed in a high SR grid map to rapidly acquire the final high precision global path. In each path planning process, a modified inshore-distance-constraint fast marching square (IDC-FM

Identifiants

pubmed: 34307863
doi: 10.7717/peerj-cs.612
pii: cs-612
pmc: PMC8279141
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e612

Informations de copyright

©2021 Wang et al.

Déclaration de conflit d'intérêts

The authors declare there are no competing interests.

Références

Front Robot AI. 2020 Jan 28;7:2
pubmed: 33501171

Auteurs

Dong Wang (D)

School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, China.
First Institute of Oceanography, Ministry of Natural Resources, Qingdao, Shandong, China.

Jie Zhang (J)

School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, China.
First Institute of Oceanography, Ministry of Natural Resources, Qingdao, Shandong, China.

Jiucai Jin (J)

First Institute of Oceanography, Ministry of Natural Resources, Qingdao, Shandong, China.

Deqing Liu (D)

First Institute of Oceanography, Ministry of Natural Resources, Qingdao, Shandong, China.

Xingpeng Mao (X)

School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, China.

Classifications MeSH