Mission-driven path planning and design of submersible unmanned ship with multiple navigation states
Mission-driven path planning and design of submersible unmanned ship with multiple navigation states
The path planning of unmanned ships at the lowest energy consumption is of great significance for energy savings. At present, the path planning of unmanned ships based on the idea of energy savings is usually oriented toward a single navigation state, and there are few research projects on the path planning of ships that sail across multiple navigation states. The navigation states of submersible unmanned ships of path planning in this paper involves underwater navigation states with various diving depths and water's surface navigation state. The improved genetic algorithm is applied to carry out the path planning for a single navigation state and task-driven multi-navigation states of the submersible unmanned ship at energy-saving velocity and non-energy-saving velocity, respectively. The energy-saving velocities are obtained by establishing optimization models. The results show that, at energy-saving velocity, the energy consumption of the same type of navigation paths increase with the diving depth. There is an obvious velocity demarcation point at non-energy-saving velocity range, which makes the sequence of energy consumption of the three types of paths classed by navigation tasks reversed before and after that point. The research can provide technical support for path planning of cross-domain unmanned ships.
Energy consumption, Improved genetic algorithm, Multi-navigation state, Navigation state transition, Path planning, Submersible unmanned ship
1-14
Guo, Jia
4c491e1e-2cdf-48b1-82e8-b6151fe8da80
Hou, Yuanhang
ae2e7244-177c-4d9b-86ee-28a48dcdef66
Liang, Xiao
7febdcad-0791-4c6e-b6eb-044d495a6188
Yang, Hongyu
cd32dc7d-65c1-4877-a80a-d85628435444
Xiong, Yeping
51be8714-186e-4d2f-8e03-f44c428a4a49
5 September 2022
Guo, Jia
4c491e1e-2cdf-48b1-82e8-b6151fe8da80
Hou, Yuanhang
ae2e7244-177c-4d9b-86ee-28a48dcdef66
Liang, Xiao
7febdcad-0791-4c6e-b6eb-044d495a6188
Yang, Hongyu
cd32dc7d-65c1-4877-a80a-d85628435444
Xiong, Yeping
51be8714-186e-4d2f-8e03-f44c428a4a49
Guo, Jia, Hou, Yuanhang, Liang, Xiao, Yang, Hongyu and Xiong, Yeping
(2022)
Mission-driven path planning and design of submersible unmanned ship with multiple navigation states.
Ocean Engineering, 263, , [112363].
(doi:10.1016/j.oceaneng.2022.112363).
Abstract
The path planning of unmanned ships at the lowest energy consumption is of great significance for energy savings. At present, the path planning of unmanned ships based on the idea of energy savings is usually oriented toward a single navigation state, and there are few research projects on the path planning of ships that sail across multiple navigation states. The navigation states of submersible unmanned ships of path planning in this paper involves underwater navigation states with various diving depths and water's surface navigation state. The improved genetic algorithm is applied to carry out the path planning for a single navigation state and task-driven multi-navigation states of the submersible unmanned ship at energy-saving velocity and non-energy-saving velocity, respectively. The energy-saving velocities are obtained by establishing optimization models. The results show that, at energy-saving velocity, the energy consumption of the same type of navigation paths increase with the diving depth. There is an obvious velocity demarcation point at non-energy-saving velocity range, which makes the sequence of energy consumption of the three types of paths classed by navigation tasks reversed before and after that point. The research can provide technical support for path planning of cross-domain unmanned ships.
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More information
Accepted/In Press date: 19 August 2022
e-pub ahead of print date: 5 September 2022
Published date: 5 September 2022
Additional Information:
Funding Information:
This work is supported by the National Natural Science Foundation of China ( 51879023 ) and the Central Government Guides Local Science and Technology Funds .
Publisher Copyright:
© 2022 Elsevier Ltd
Keywords:
Energy consumption, Improved genetic algorithm, Multi-navigation state, Navigation state transition, Path planning, Submersible unmanned ship
Identifiers
Local EPrints ID: 469158
URI: http://eprints.soton.ac.uk/id/eprint/469158
ISSN: 0029-8018
PURE UUID: ea39f714-41ff-4843-ad7f-688dd03d8fab
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Date deposited: 08 Sep 2022 16:31
Last modified: 17 Mar 2024 02:51
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Contributors
Author:
Jia Guo
Author:
Yuanhang Hou
Author:
Xiao Liang
Author:
Hongyu Yang
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