An overview of machine learning techniques in local path planning for autonomous underwater vehicles.
Autonomous underwater vehicles (AUVs) have become attractive and essential for underwater search and exploration because of the advantages they offer over manned underwater vehicles. Hence the need to improve AUV technologies. One crucial area of AUV technology involves efficiently solving the path...
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Institute of Electrical and Electronics Engineers Inc.
2023
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Online Access: | http://eprints.utm.my/104840/1/ChinonsoEOkerekeMohdMurtadhaMohamadNurHalizaAbdulWahab2023_AnOverviewofMachineLearningTechniques.pdf http://eprints.utm.my/104840/ http://dx.doi.org/10.1109/ACCESS.2023.3249966 |
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my.utm.1048402024-03-25T08:56:00Z http://eprints.utm.my/104840/ An overview of machine learning techniques in local path planning for autonomous underwater vehicles. Okereke, Chinonso E. Mohamad, Mohd. Murtadha Abdul Wahab, Nur Haliza Elijah, Olakunle Al-Nahari, Abdulaziz H. S., Zaleha T Technology (General) T58.5-58.64 Information technology Autonomous underwater vehicles (AUVs) have become attractive and essential for underwater search and exploration because of the advantages they offer over manned underwater vehicles. Hence the need to improve AUV technologies. One crucial area of AUV technology involves efficiently solving the path planning problem. Several approaches have been identified from the literature for AUV global and local path planning. The use of machine learning (ML) techniques in overcoming some of the challenges associated with AUV path planning problems such as safety and obstacle avoidance, energy consumption, and optimal time and distance travelled remains an active research area. While there is literature on global and local path planning that explores different techniques, there is still a lack of paper that provides an overview of the application of ML for local path planning. Hence the main objective of this paper is to present an overview of the state-of-the-art application of ML techniques on local path planning for AUVs. The ML algorithms are discussed under supervised, unsupervised, and reinforcement learning. The challenges faced in real-life deployment, simulated scenarios, computational issues, and application of ML algorithms are discussed, with future research directions presented. Institute of Electrical and Electronics Engineers Inc. 2023 Article PeerReviewed application/pdf en http://eprints.utm.my/104840/1/ChinonsoEOkerekeMohdMurtadhaMohamadNurHalizaAbdulWahab2023_AnOverviewofMachineLearningTechniques.pdf Okereke, Chinonso E. and Mohamad, Mohd. Murtadha and Abdul Wahab, Nur Haliza and Elijah, Olakunle and Al-Nahari, Abdulaziz and H. S., Zaleha (2023) An overview of machine learning techniques in local path planning for autonomous underwater vehicles. IEEE Access, 11 . pp. 24894-24907. ISSN 2169-3536 http://dx.doi.org/10.1109/ACCESS.2023.3249966 DOI: 10.1109/ACCESS.2023.3249966 |
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T Technology (General) T58.5-58.64 Information technology Okereke, Chinonso E. Mohamad, Mohd. Murtadha Abdul Wahab, Nur Haliza Elijah, Olakunle Al-Nahari, Abdulaziz H. S., Zaleha An overview of machine learning techniques in local path planning for autonomous underwater vehicles. |
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Autonomous underwater vehicles (AUVs) have become attractive and essential for underwater search and exploration because of the advantages they offer over manned underwater vehicles. Hence the need to improve AUV technologies. One crucial area of AUV technology involves efficiently solving the path planning problem. Several approaches have been identified from the literature for AUV global and local path planning. The use of machine learning (ML) techniques in overcoming some of the challenges associated with AUV path planning problems such as safety and obstacle avoidance, energy consumption, and optimal time and distance travelled remains an active research area. While there is literature on global and local path planning that explores different techniques, there is still a lack of paper that provides an overview of the application of ML for local path planning. Hence the main objective of this paper is to present an overview of the state-of-the-art application of ML techniques on local path planning for AUVs. The ML algorithms are discussed under supervised, unsupervised, and reinforcement learning. The challenges faced in real-life deployment, simulated scenarios, computational issues, and application of ML algorithms are discussed, with future research directions presented. |
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Article |
author |
Okereke, Chinonso E. Mohamad, Mohd. Murtadha Abdul Wahab, Nur Haliza Elijah, Olakunle Al-Nahari, Abdulaziz H. S., Zaleha |
author_facet |
Okereke, Chinonso E. Mohamad, Mohd. Murtadha Abdul Wahab, Nur Haliza Elijah, Olakunle Al-Nahari, Abdulaziz H. S., Zaleha |
author_sort |
Okereke, Chinonso E. |
title |
An overview of machine learning techniques in local path planning for autonomous underwater vehicles. |
title_short |
An overview of machine learning techniques in local path planning for autonomous underwater vehicles. |
title_full |
An overview of machine learning techniques in local path planning for autonomous underwater vehicles. |
title_fullStr |
An overview of machine learning techniques in local path planning for autonomous underwater vehicles. |
title_full_unstemmed |
An overview of machine learning techniques in local path planning for autonomous underwater vehicles. |
title_sort |
overview of machine learning techniques in local path planning for autonomous underwater vehicles. |
publisher |
Institute of Electrical and Electronics Engineers Inc. |
publishDate |
2023 |
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http://eprints.utm.my/104840/1/ChinonsoEOkerekeMohdMurtadhaMohamadNurHalizaAbdulWahab2023_AnOverviewofMachineLearningTechniques.pdf http://eprints.utm.my/104840/ http://dx.doi.org/10.1109/ACCESS.2023.3249966 |
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