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.
| Original language | English |
|---|---|
| Title of host publication | Ocean Engineering |
| Publisher | American Society of Mechanical Engineers (ASME) |
| ISBN (Electronic) | 9780791857731 |
| DOIs | |
| Publication status | Published - 2017 Jan 1 |
| Event | ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2017 - Trondheim, Norway Duration: 2017 Jun 25 → 2017 Jun 30 |
Publication series
| Name | Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE |
|---|---|
| Volume | 7A-2017 |
Other
| Other | ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2017 |
|---|---|
| Country | Norway |
| City | Trondheim |
| Period | 17-06-25 → 17-06-30 |
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All Science Journal Classification (ASJC) codes
- Ocean Engineering
- Energy Engineering and Power Technology
- Mechanical Engineering
Cite this
}
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; Vol. 7A-2017).Research output: Chapter in Book/Report/Conference proceeding › 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.
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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
T3 - Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE
BT - Ocean Engineering
PB - American Society of Mechanical Engineers (ASME)
ER -