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
N1 - Funding Information:
The authors would like to express their thanks to the National Science Council for a grant under Contract No. MOST 105-2218-E-006-013. The authors also thank the great support for this work provided by Research Center for Energy Technology (RCETS), National Cheng Kung University. The partial support coming from the International Wave Dynamics Research Center (IWDRC), National Cheng Kung University, for a grant under Contract No. MOST 105-2911-I-006-301 is very appreciated. The research was, in part, supported by the Ministry of Education, Taiwan, R.O.C. The aim for the Top University Project to the National Cheng Kung University. Besides, we would like to appreciate the precious comments from Prof. Ming-Chung Fang during the research period.
Publisher Copyright:
Copyright © 2017 ASME.
PY - 2017
Y1 - 2017
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.
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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)
T2 - ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2017
Y2 - 25 June 2017 through 30 June 2017
ER -
