The development of mopso-based dynamic routing algorithm for the inspection of autonomous underwater vehicle

Yu-Hsien Lin, Lin Chin Huang, Shao Yu Chen

研究成果: Conference contribution

摘要

In this study, the authors developed the dynamic routing algorithm combining an image detection technique to support the optimal route plan of Autonomous Underwater Vehicle (AUV) inspecting an offshore wind farm affected by ocean currents. A modular structure is applied to program design by the graphical language, LabVIEW (Laboratory Virtual Instrument Engineering Workbench). The modular structure is composed of 6-DOF (Six Degrees-of-Freedom) motion module, a self-tuning fuzzy control module, a stereo-vision detection module, and a dynamic routing module. In terms of path planning for inspection, several Pareto frontiers are solved iteratively according to two objectives, namely, cruise time and energy consumption. Performances obtained from MOPSO (Multi-Objective Particle Swarm Optimization) -based dynamic routing algorithm would be in comparison with those from SOPSO (Single-Objective Particle Swarm Optimization) -based dynamic routing algorithm. In addition, selections of fixed weight and dynamic weight of MOPSO-based dynamic routing algorithms would be discussed in the environment with or without ocean currents. Eventually, the image inspection mode is not only beneficial for optimizing feasible routes but it can also identify features of obstacles for positioning.

原文English
主出版物標題Ocean Engineering
發行者American Society of Mechanical Engineers (ASME)
ISBN(電子)9780791857731
DOIs
出版狀態Published - 2017 一月 1
事件ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2017 - Trondheim, Norway
持續時間: 2017 六月 252017 六月 30

出版系列

名字Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE
7A-2017

Other

OtherASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2017
國家Norway
城市Trondheim
期間17-06-2517-06-30

指紋

Dynamic routing algorithms
Autonomous underwater vehicles
Inspection
Particle swarm optimization (PSO)
Ocean currents
Offshore wind farms
Stereo vision
Fuzzy control
Motion planning
Energy utilization
Tuning

All Science Journal Classification (ASJC) codes

  • Ocean Engineering
  • Energy Engineering and Power Technology
  • Mechanical Engineering

引用此文

Lin, Y-H., Huang, L. C., & Chen, S. Y. (2017). The development of mopso-based dynamic routing algorithm for the inspection of autonomous underwater vehicle. 於 Ocean Engineering (Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE; 卷 7A-2017). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/OMAE2017-61124
Lin, Yu-Hsien ; Huang, Lin Chin ; Chen, Shao Yu. / The development of mopso-based dynamic routing algorithm for the inspection of autonomous underwater vehicle. Ocean Engineering. American Society of Mechanical Engineers (ASME), 2017. (Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE).
@inproceedings{5ddb6117d4bc4e76a569840826aa8ffb,
title = "The development of mopso-based dynamic routing algorithm for the inspection of autonomous underwater vehicle",
abstract = "In this study, the authors developed the dynamic routing algorithm combining an image detection technique to support the optimal route plan of Autonomous Underwater Vehicle (AUV) inspecting an offshore wind farm affected by ocean currents. A modular structure is applied to program design by the graphical language, LabVIEW (Laboratory Virtual Instrument Engineering Workbench). The modular structure is composed of 6-DOF (Six Degrees-of-Freedom) motion module, a self-tuning fuzzy control module, a stereo-vision detection module, and a dynamic routing module. In terms of path planning for inspection, several Pareto frontiers are solved iteratively according to two objectives, namely, cruise time and energy consumption. Performances obtained from MOPSO (Multi-Objective Particle Swarm Optimization) -based dynamic routing algorithm would be in comparison with those from SOPSO (Single-Objective Particle Swarm Optimization) -based dynamic routing algorithm. In addition, selections of fixed weight and dynamic weight of MOPSO-based dynamic routing algorithms would be discussed in the environment with or without ocean currents. Eventually, the image inspection mode is not only beneficial for optimizing feasible routes but it can also identify features of obstacles for positioning.",
author = "Yu-Hsien Lin and Huang, {Lin Chin} and Chen, {Shao Yu}",
year = "2017",
month = "1",
day = "1",
doi = "10.1115/OMAE2017-61124",
language = "English",
series = "Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE",
publisher = "American Society of Mechanical Engineers (ASME)",
booktitle = "Ocean Engineering",

}

Lin, Y-H, Huang, LC & Chen, SY 2017, The development of mopso-based dynamic routing algorithm for the inspection of autonomous underwater vehicle. 於 Ocean Engineering. Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE, 卷 7A-2017, American Society of Mechanical Engineers (ASME), ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2017, Trondheim, Norway, 17-06-25. https://doi.org/10.1115/OMAE2017-61124

The development of mopso-based dynamic routing algorithm for the inspection of autonomous underwater vehicle. / Lin, Yu-Hsien; Huang, Lin Chin; Chen, Shao Yu.

Ocean Engineering. American Society of Mechanical Engineers (ASME), 2017. (Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE; 卷 7A-2017).

研究成果: Conference contribution

TY - GEN

T1 - The development of mopso-based dynamic routing algorithm for the inspection of autonomous underwater vehicle

AU - Lin, Yu-Hsien

AU - Huang, Lin Chin

AU - Chen, Shao Yu

PY - 2017/1/1

Y1 - 2017/1/1

N2 - In this study, the authors developed the dynamic routing algorithm combining an image detection technique to support the optimal route plan of Autonomous Underwater Vehicle (AUV) inspecting an offshore wind farm affected by ocean currents. A modular structure is applied to program design by the graphical language, LabVIEW (Laboratory Virtual Instrument Engineering Workbench). The modular structure is composed of 6-DOF (Six Degrees-of-Freedom) motion module, a self-tuning fuzzy control module, a stereo-vision detection module, and a dynamic routing module. In terms of path planning for inspection, several Pareto frontiers are solved iteratively according to two objectives, namely, cruise time and energy consumption. Performances obtained from MOPSO (Multi-Objective Particle Swarm Optimization) -based dynamic routing algorithm would be in comparison with those from SOPSO (Single-Objective Particle Swarm Optimization) -based dynamic routing algorithm. In addition, selections of fixed weight and dynamic weight of MOPSO-based dynamic routing algorithms would be discussed in the environment with or without ocean currents. Eventually, the image inspection mode is not only beneficial for optimizing feasible routes but it can also identify features of obstacles for positioning.

AB - In this study, the authors developed the dynamic routing algorithm combining an image detection technique to support the optimal route plan of Autonomous Underwater Vehicle (AUV) inspecting an offshore wind farm affected by ocean currents. A modular structure is applied to program design by the graphical language, LabVIEW (Laboratory Virtual Instrument Engineering Workbench). The modular structure is composed of 6-DOF (Six Degrees-of-Freedom) motion module, a self-tuning fuzzy control module, a stereo-vision detection module, and a dynamic routing module. In terms of path planning for inspection, several Pareto frontiers are solved iteratively according to two objectives, namely, cruise time and energy consumption. Performances obtained from MOPSO (Multi-Objective Particle Swarm Optimization) -based dynamic routing algorithm would be in comparison with those from SOPSO (Single-Objective Particle Swarm Optimization) -based dynamic routing algorithm. In addition, selections of fixed weight and dynamic weight of MOPSO-based dynamic routing algorithms would be discussed in the environment with or without ocean currents. Eventually, the image inspection mode is not only beneficial for optimizing feasible routes but it can also identify features of obstacles for positioning.

UR - http://www.scopus.com/inward/record.url?scp=85032003147&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85032003147&partnerID=8YFLogxK

U2 - 10.1115/OMAE2017-61124

DO - 10.1115/OMAE2017-61124

M3 - Conference contribution

AN - SCOPUS:85032003147

T3 - Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE

BT - Ocean Engineering

PB - American Society of Mechanical Engineers (ASME)

ER -

Lin Y-H, Huang LC, Chen SY. The development of mopso-based dynamic routing algorithm for the inspection of autonomous underwater vehicle. 於 Ocean Engineering. American Society of Mechanical Engineers (ASME). 2017. (Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE). https://doi.org/10.1115/OMAE2017-61124