An energy-saving path planning method using adaptive ant colony system for marine surface vehicles

Given the need for ship emission reduction and the energy constraints of unmanned surface vehicles, it is imperative to develop an efficient ship path planning method that incorporates energy-saving considerations. In this paper, considering the challenges posed by the complexity of the marine envir...

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Veröffentlicht in:Soft computing (Berlin, Germany) Jg. 28; H. 9-10; S. 6637 - 6656
Hauptverfasser: Dong, Liangxiong, Gan, Xinhua
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2024
Springer Nature B.V
Schlagworte:
Algorithms
Application of Soft Computing
Artificial Intelligence
B spline functions
Computational Intelligence
Control
Energy consumption
Engineering
Entropy (Information theory)
Marine environment
Mathematical Logic and Foundations
Mechatronics
Methods
Optimization
Path planning
Performance indices
Pheromones
Planning
Robotics
Surface vehicles
Unmanned vehicles
Wind effects
Path planning
Energy saving
Marine surface vehicle
Ant colony system
ISSN:1432-7643, 1433-7479
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Zusammenfassung:Given the need for ship emission reduction and the energy constraints of unmanned surface vehicles, it is imperative to develop an efficient ship path planning method that incorporates energy-saving considerations. In this paper, considering the challenges posed by the complexity of the marine environment, an adaptive ant colony system is proposed for ship energy-saving path planning. Firstly, a ship energy consumption estimation model is presented by analyzing the influence of wind, wave, and current on energy consumption. Secondly, two process evaluation indexes are designed for the quality and diversity of the paths in the optimization process. Based on these indexes, three improvements are presented to enhance the performance of the ant colony system. These improvements include (a) an adaptive state transition rule adjusted dynamically based on the information entropy, (b) the number of ants contributing to the global updating rule and the corresponding update value are controlled dynamically based on the quality of the path, and (c) an improved recombination mechanism. Besides, an improved turning point optimization method and B-spline curve are introduced for path post-processing. Finally, the effectiveness of the proposed method is verified by simulation experiments.
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-023-09513-x