Advances of Complex Marine Environmental Influences on Underwater Vehicles
Abstract
1. Introduction
2. Influences of Surface Waves
2.1. Near-Surface Navigation in Surface Waves
2.2. Water-Exit in Surface Waves
2.3. Water Entry in Surface Waves
3. Influences of Ocean Currents
3.1. Influences on Navigation State
3.2. Influences on Path Planning
- (1)
- A* algorithm: The A* algorithm is an effective method for solving the shortest path in static road networks. Perdomo proposed a CTS-A* (Constant-Time Surfacing A*) algorithm based on the A* algorithm [74]. The advantage of this algorithm lies in its ability to handle current information effectively, but its limitations are severe, and the algorithm fails in dynamic current settings. Redondo et al. [75] further improved this algorithm by introducing time into the algorithm and integrating the time-varying trajectory of the underwater vehicle using the time-varying 2D current field provided by the regional ocean model. The improved method ensures the optimality of the paths obtained when uncertainties such as current eddies are taken into account. Also in the 2D case, Josep Isem et al. [76] proposed a low-energy underwater path planning method based on the A* and ND algorithms. The advantage of this method is that it is suitable for complex environments such as strong currents and can effectively avoid static or dynamic obstacles to obtain an optimal path. All of the above algorithms are limited to two-dimensional conditions. They are not applicable to three-dimensional situations. In contrast, Zhou et al. [77] applied the CTS-A* iterative algorithm to the path planning of an underwater vehicle based on the constructed current field. This has been demonstrated in sea trials with single and multi-path currents.
- (2)
- Wavefront Algorithm: This method is based on the graph search theory. It is a blind search algorithm for solving the shortest path problem from the start point to the end point on a known map. Soulignac et al. [78] proposed a sliding wavefront expansion method based on the wave matrix algorithm. The advantage of this algorithm is that it ensures accuracy, efficiency, and global optimality of the path by combining an appropriate cost function with successive optimization techniques, but the algorithm is not applied to the 3D case. Chinmay and Pierre [79] proposed a wavefront algorithm for time-varying current path planning in a current environment. This algorithm takes into account the unknown and real-time nature of currents in the real ocean environment, which provides better accuracy and adaptability.
- (3)
- Level set algorithm: Level set is a numerical technique for interface tracking and shape modeling, which has the advantage of allowing the numerical computation of evolving curved surfaces on Cartesian grids without parameterizing the curved surfaces. Tomaszewski et al. [80] investigated the problem of efficient path planning for an underwater vehicle traversing a current field in a real ocean by using the level set method. The method can effectively reduce the time and energy consumption of traversing paths. However, the traditional level set method is slow, so Liu [81] improved it and proposed an improved narrowband level set method suitable for path planning. Based on the improved level set, a two-dimensional and three-dimensional path planning method considering the influence of ocean currents is developed. It is characterized by its ability to effectively utilize the downstream currents, avoid the strong currents and obstacle areas in the reverse direction, and reach the target point in the shortest time. But the limitations of this algorithm are clearer: Its planned route considers only the time-optimal route without considering the path length and energy consumption, while the time-varying characteristics of the current are not considered in the three-dimensional path planning.
- (4)
- Artificial potential field method: This method was first proposed by Khatib [82]. He applied it to the field of path planning for mobile robots. The underwater vehicle moves to the target point under the joint action of gravitational force and repulsive force. The gravitational field is generated by the target point and the repulsive field is generated by the obstacles. However, the traditional artificial potential field methods are not easy to obtain the optimal path; for example, such methods suffer from the local extreme value problem and are ineffective or even invalid in complex current environments [83]. Therefore, for the path planning problem under the influence of sea currents, most of them adopt the improved or hybrid artificial potential field method. Cao et al. [84] combined the artificial potential field method and ant colony algorithm and then proposed a path planning algorithm under a sea current environment. This algorithm can effectively avoid the local extreme value problem of the artificial potential field method. Yang et al. [85] introduced the velocity potential field function into the artificial potential field method, transforming the static potential field into a dynamic potential field, and combined with the kinematic properties and constraints of the underwater vehicle to obtain an expression for the obstacle influence radius, overcame the limitations of local extremes and target unreachability.
- (5)
- Ant colony algorithm: Italian scientists Dorigo et al. [86] proposed this algorithm inspired by the characteristics of foraging behavior of ant colonies in nature, which is a kind of intelligent bionic optimization algorithm. Liu et al. [87] improved the ant colony algorithm by considering the role of factors such as sea currents and used the energy consumption of sailing to guide colony evolution. Compared to the unimproved method, the navigation time was significantly improved. The traditional ant colony path planning method has the disadvantage of slow convergence speed. To overcome this disadvantage, Fu et al. [88] introduced artificial potential field heuristic factors into the ant colony algorithm to obtain the Artificial Potential Field Ant Colony Algorithm (APF-ACO). The method uses the cell to reflect the regional physical elements and quantifies the physical ocean phenomena in the form of a cost function to realize the extensive use of the ocean sound velocity environment, the ocean density environment, and the current environment, on the basis of which the optimal path is designed. As a result of this improvement, the convergence speed of the algorithm is effectively enhanced. Furthermore, the integration of the Delaunay triangle decompositions into the ACO optimization algorithm can also achieve the effective use of current information [89] and has a faster convergence rate.
- (6)
- Particle Swarm Optimization Algorithm: This is a simulation algorithm based on the flock aggregation model used to solve the problems of how birds avoid collision and adjust neighboring birds’ speed. Compared with the particle swarm algorithm, the quantum particle swarm algorithm shows better ability in the ocean current environment [90]. Based on the quantum particle swarm algorithm principle, Zou et al. [91] proposed an ocean current path planning method suitable for high-dimensional situations. However, the method fails when there are complex obstacles and currents in the environment. In fact, the marine environment is constantly changing, so there is a high probability that the underwater vehicle will encounter new obstacles while tracking the global path. To solve the multi-objective path planning problem for underwater vehicles under uncertain sea currents and uncertain hazard sources, Gu [92] proposed an interval-based multi-objective quantum particle swarm algorithm, which works well for route planning by interval optimization under constant current conditions, changing current conditions, and dynamic obstacle conditions.
4. Influences of Ocean Stratification
4.1. Influences on Navigation State
4.2. Influences of Stealth Performance
5. Influences of Oceanic Internal Waves
5.1. Internal Wave Theory
5.2. Experiments
5.3. Numerical Simulation
- (1)
- The motion response of a submarine located above and at the pycnocline is more affected under the action of internal solitary waves. The effect on the loading characteristics of the submarine increases as it moves closer to the pycnocline.
- (2)
- The speed of response of the submerged body motion and the peak size of the load applied are mainly affected by the amplitude of the wave. The larger the amplitude of the internal solitary waves, the faster the response of the submerged body motion and the larger the peak value of the load applied.
- (3)
- Under the influence of internal solitary waves, a self-propelled body is more susceptible to runaway phenomena and force surge compared to a suspended body. Additionally, the peak load on the submerged body increases at higher speeds.
6. Conclusions
- (1)
- The effects of waves on underwater vehicles cover near-surface stabilization, out-of-water motion, and in-water motion. Wave forces, drift effects, and attitude perturbations together affect the stability of near-surface vehicles. Waves change the shape of the evacuation, the vehicle trajectory, and the variables at the entry point, which increase the complexity and risk of exiting the water; in the presence of waves, the entry process will be accompanied by complex flow phenomena, such as the evolution of the cavitation, splashing, and vortex distribution, which leads to increased difficulty in entering the water. The current research mainly focuses on the principle of the influence of waves, the load and motion characteristics of the underwater vehicle, and the evolution of the flow field, and there are fewer studies on how to reduce the influence of waves, the control strategy in the waves, and the rapid selection of suitable exit and entry positions, which can be further researched in the future.
- (2)
- The presence of currents mainly affects parameters such as course direction, speed, pressure distribution, and hydrodynamic characteristics of the underwater vehicle, which can interfere with positioning and navigation performance, and ultimately reduce the accuracy of its path planning. At present, scholars have established a complete motion model of the underwater vehicle in the sea current environment, on the basis of which they have summarized the influence law of the sea current on the operation state, and improved the existing path planning algorithms and put forward some path planning methods which are applicable to the ocean current environment. With the rapid development of artificial intelligence in recent years, the integration of multiple algorithms and the introduction of deep learning and other technologies can significantly improve the path planning ability; therefore, the development of a multi-algorithm collaborative model based on machine learning is the main direction for the development of the path planning problem.
- (3)
- Density stratification can lead to the phenomenon of “sea cliffs”, which can have a serious impact on the loads on the vehicles, as well as cause anomalies in the wake of the vehicle and affect the shape of the free surface, which greatly increases the risk of exposure for the vehicle. After more than a century of research, scholars now have a clear understanding of the influence law of density stratification on the loading of the vehicle and the evolution process of the wake field. However, current research focuses on simplified or ideal environments and models. Further research and development of numerical, computational, and experimental methods are needed in the future to study the actual density jump layer and complex vehicles.
- (4)
- At present, the research on the influence of internal waves on underwater vehicles is relatively systematic, and scholars have conducted deep research in theoretical studies, physical experiments, and numerical simulations. Using experimental and CFD methods, the mechanism of internal wave effects on the underwater vehicle is revealed based on various internal wave models, such as KdV, MCC, DJL, etc. The influence of various parameters is explored. However, there are still some aspects that deserve further exploration. In terms of internal wave experiments, it is now possible to generate stably propagating internal waves in the laboratory based on a variety of methods, and to conduct some experiments on the interaction of internal waves with simple structural objects. Due to the limitations of scale, equipment, and other conditions, there is a lack of experimental studies on the interaction of internal waves with complex structures or self-propelled bodies, and further research work is still needed. In terms of numerical simulation studies, most of the studies are carried out on the underwater vehicle itself, and there are few studies on the interaction between internal waves and the attachments, which deserves the attention of scholars.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Method Name | Principle or Technology | Advantages | Disadvantages |
---|---|---|---|
A* algorithm | Maintenance of actual and predicted distances | Fast convergence and high stability | May fail under dynamic conditions |
Wavefront Algorithm | Graph search theory | Good accuracy and adaptability | Requires known maps |
Level set algorithm | Categorize and compare | Interface tracking and shape modeling | Slow convergence |
Artificial potential field method | Gravitational and repulsive field interactions | Simple principle | Existence of localized extremes |
Ant colony algorithm | Foraging behavior of ants | More Intelligent | Slow convergence |
Particle Swarm Optimization Algorithm | Bird Aggregation Modeling | Simple structure, easy to implement | May fail under dynamic conditions |
Model Name | Creator | Fluid Type | Range of Application |
---|---|---|---|
KdV | Korteweg G. de.Vries | two-layer fluid | weak nonlinearity shallow |
eKdV | Helfrich Melville | two-layer fluid | medium nonlinear shallow |
mKdV | Michallet Barthelemy | two-layer fluid | medium nonlinear shallow |
MCC | Miyata Choi Camassa | two-layer fluid three-layer fluid | strongly nonlinear deepwater |
DJL | Dubreil Jacotin Long | continuously stratified fluid | strongly nonlinear shallow/deepwater |
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Zhao, S.; Hu, H.; Ouahsine, A.; Lu, H.; Li, Z.; Yuan, Z.; Du, P. Advances of Complex Marine Environmental Influences on Underwater Vehicles. J. Mar. Sci. Eng. 2025, 13, 1297. https://doi.org/10.3390/jmse13071297
Zhao S, Hu H, Ouahsine A, Lu H, Li Z, Yuan Z, Du P. Advances of Complex Marine Environmental Influences on Underwater Vehicles. Journal of Marine Science and Engineering. 2025; 13(7):1297. https://doi.org/10.3390/jmse13071297
Chicago/Turabian StyleZhao, Sen, Haibao Hu, Abdellatif Ouahsine, Haochen Lu, Zhuoyue Li, Zhiming Yuan, and Peng Du. 2025. "Advances of Complex Marine Environmental Influences on Underwater Vehicles" Journal of Marine Science and Engineering 13, no. 7: 1297. https://doi.org/10.3390/jmse13071297
APA StyleZhao, S., Hu, H., Ouahsine, A., Lu, H., Li, Z., Yuan, Z., & Du, P. (2025). Advances of Complex Marine Environmental Influences on Underwater Vehicles. Journal of Marine Science and Engineering, 13(7), 1297. https://doi.org/10.3390/jmse13071297