Energy Priority and Current Model Based AUV Cruise Path Planning Algorithm

Liu, Guangcong; Chen, Hainan; Xiao-ling, WU; Li, Dong; Huang, Tingting

doi:10.14257/ijmue.2013.8.5.02

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…Liu et al 26 proposed an energy priority and current model based AUV path planning algorithm. Their focus was on the effect of the model applied for water current on selection of AUV path in a 3D environment.…”

Section: Auv Path Planning For Data Collectionmentioning

confidence: 99%

Autonomous underwater vehicles support for enhanced performance in the Internet of underwater things

Al-Bzoor

Al‐assem

Alawneh

et al. 2021

Trans Emerging Tel Tech

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The Internet of underwater things (IoUT) has become a promising and vibrant paradigm which mainly relies on the underwater acoustic wireless sensor networks (UASN). IoUT was first proposed to facilitate offshore real-time monitoring and exploration of underwater environments. It is now becoming a key component for building and connecting the futuristic underwater smart cities. However, Acoustic waves used in underwater communication suffer from long propagation delays and high transmission energy requirements. Therefore, approaches for autonomous underwater vehicle (AUV) assisted data collection and cluster-based routing were proposed to overcome these drawbacks. In this article, we present a smart genetic-based AUV path planning algorithm for data collection to enhance the performance of UASN. In addition, we combine our smart data collection technique with a dynamic location-unaware clustering algorithm to further reduce energy consumption with mobility consideration. Our simulation results showed that our genetic-based technique achieves more than 145% increase in network lifetime and almost a 75% reduction in energy expenditure compared with traditional diagonal path selection technique. More simulations comparing our combined technique with a novel clustering technique showed that the network lifetime is twice as better, total energy consumption is nearly 30% less, and the delivery ratio is 15% more at low densities. K E Y W O R D S acoustic wireless sensor networks, autonomous underwater vehicles, clustering, Internet of underwater things, underwater communications Recently, the Internet of things (IoT) has gained the attention and interest of the scientific, engineering, and industrial communities. In IoT, smart devices from different heterogeneous systems are connected and controlled over the Internet to exchange important data for several purposes. 1 IoT can be further extended and established in oceans, seas, and big rivers to form the Internet of underwater things (IoUT). 2,3 IoUT is a promising field for a vast number of applications such as tsunami early warning systems, connecting offshore oil and gas platforms, real-time tracking of the submarine ecosystem, and analyzing waste and pollution of specific underwater environments. 4

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Section: Auv Path Planning For Data Collectionmentioning

confidence: 99%

Autonomous underwater vehicles support for enhanced performance in the Internet of underwater things

Al-Bzoor

Al‐assem

Alawneh

et al. 2021

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…Global path is generated on the basis of preliminary environmental understanding and modelling. 5 On the other hand, local path planning is responsible for the generation of local or regional path to handle unknown and temporary obstacles under environmental disturbances. 6 In the last decade, a variety of solutions have been developed for the underwater vehicle path planning problems.…”

Section: Introductionmentioning

confidence: 99%

Cognition-based hybrid path planning for autonomous underwater vehicle target following

Ming

Huang

et al. 2019

International Journal of Advanced Robotic Systems

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Intelligent path planning is one of the key techniques for autonomous underwater vehicles for the purpose of target detection, environmental survey and so on. In order to realize automatic motion plan, an intelligent cognitive architecture for autonomous underwater vehicle motion planning has been proposed to realize complicated target detection and mobile target following in the disturbance environment. A novel adaptive ant colony optimization and particle swarm optimization fusion-based fuzzy rules optimization algorithm has been proposed to generate optimized fuzzy rules. Through this optimization algorithm, the preliminary fuzzy rules can be optimized to realize intelligent motion planning for complicated operation tasks. Experiments of channel following for wall detection and mobile target following in the oceanic environment have verified the validity of path planning method in the implementation of detection and operation tasks.

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Energy Priority and Current Model Based AUV Cruise Path Planning Algorithm

Abstract: In this paper, we study the influence of the current on the selection of the cruise path of AUV (Autonomous Underwater Vehicle

Cited by 2 publications

References 8 publications

Autonomous underwater vehicles support for enhanced performance in the Internet of underwater things

Autonomous underwater vehicles support for enhanced performance in the Internet of underwater things

Cognition-based hybrid path planning for autonomous underwater vehicle target following

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