Ship Trajectory Planning and Optimization via Ensemble Hybrid A* and Multi-Target Point Artificial Potential Field Model
Abstract
:1. Introduction
- (1)
- In this paper, a new A*path planning algorithm is proposed, which considers the constraints of ship motion, improves the traditional A*algorithm, eliminates redundant nodes, and smoothes the path.
- (2)
- Considering the phenomenon that the APF algorithm can easily fall into local loops, an artificial potential field multi-target points (MPAPF) approach is proposed. The global path derived by the A* algorithm is divided into multi-target points, and MPAPF is used to optimize each target point locally to obtain an optimal path.
- (3)
- In route planning, the collision risk factor (CRI) is introduced, and, under the rules proposed by the International Maritime Organization (IMO), the CRI value of each segment is continuously calculated to ensure that a safe, economical, and reliable route is found.
2. Methods
2.1. Ship Motion Model
2.2. Ship Path Planning Algorithm
2.2.1. Improved A* Global Path Optimization Algorithm
2.2.2. Hybrid Improved A* and Multi-Target Point Artificial Potential Field Algorithm
3. Experiments
3.1. Static Sea Environment Path Planning
3.2. Ship Collision Avoidance Decision
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Freedom | Force and Momentum | Linearity/Angular Velocity | Position/Attitude Angle |
---|---|---|---|
Surge | X | u | x |
Sway | Y | v | y |
Yaw | N | r |
Method | Calculation Time/s | Steering Node | Path Length/n Mile |
---|---|---|---|
Traditional A* | 427.61 | 153 | 596.4 |
Improved A* | 349.23 | 11 | 490.6 |
IMPAPF* | 385.97 | 17 | 512.8 |
Method | Calculation Time/s | Steering Node | Path Length/n Mile |
---|---|---|---|
Traditional A* | 235.55 | 267 | 73.9 |
Improved A* | 133.86 | 7 | 59.4 |
IMPAPF* | 167.16 | 12 | 63.5 |
Names and Symbols | Value | Unit |
---|---|---|
Length | 116 | m |
Width | 18 | m |
Draft | 5.4 | m |
Max rudder angle | 30 | ° |
Speed | 10 | knot |
Block coefficient | 0.56 | -it |
Maneuverability index (K) | 0.31 | s−1 |
Maneuverability index (T) | 62.4 | s |
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Share and Cite
Huang, Y.; Zhao, S.; Zhao, S. Ship Trajectory Planning and Optimization via Ensemble Hybrid A* and Multi-Target Point Artificial Potential Field Model. J. Mar. Sci. Eng. 2024, 12, 1372. https://doi.org/10.3390/jmse12081372
Huang Y, Zhao S, Zhao S. Ship Trajectory Planning and Optimization via Ensemble Hybrid A* and Multi-Target Point Artificial Potential Field Model. Journal of Marine Science and Engineering. 2024; 12(8):1372. https://doi.org/10.3390/jmse12081372
Chicago/Turabian StyleHuang, Yanguo, Sishuo Zhao, and Shuling Zhao. 2024. "Ship Trajectory Planning and Optimization via Ensemble Hybrid A* and Multi-Target Point Artificial Potential Field Model" Journal of Marine Science and Engineering 12, no. 8: 1372. https://doi.org/10.3390/jmse12081372