Three-Dimensional Underwater Path Planning of Submarine Considering the Real Marine Environment

Fu, Jun; Lv, Teng; Bao, Li; Ning, Zhiwen; Chang, Yang

doi:10.1109/access.2022.3164175

Cited by 3 publications

(3 citation statements)

References 27 publications

(33 reference statements)

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“…The use of ACO for path planning has also achieved good results [38][39][40][41][42]. The authors of [38] combined fuzzy logic with an improved ACO algorithm to optimize more effective and safer paths.…”

Section: Introductionmentioning

confidence: 99%

“…In [40], time windows were combined with ACO to achieve path planning in complex three-dimensional environments. The authors of [41] used the artificial potential field method to improve ACO to enhance the convergence speed of the algorithm, thereby improving the efficiency of path planning. The authors of [42] constructed a two-level optimization architecture based on the combination of the A* algorithm and the ACO algorithm, using two strategies to improve the speed of path planning.…”

Section: Introductionmentioning

confidence: 99%

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A Path-Planning Method for UAV Swarm under Multiple Environmental Threats

Fan,

Li,

Chen

et al. 2024

Drones

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To weaken or avoid the impact of dynamic threats such as wind and extreme weather on the real-time path of a UAV swarm, a path-planning method based on improved long short-term memory (LSTM) network prediction parameters was constructed. First, models were constructed for wind, static threats, and dynamic threats during the flight of the drone. Then, it was found that atmospheric parameters are typical time series data with spatial correlation. The LSTM network was optimized and used to process time series parameters to construct a network for predicting atmospheric parameters. The state of the drone was adjusted in real time based on the prediction results to mitigate the impact of wind or avoid the threat of extreme weather. Finally, a path optimization method based on an improved LSTM network was constructed. Through simulation, it can be seen that compared to the path that does not consider atmospheric effects, the optimized path has significantly improved flightability and safety.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Path-Planning Method for UAV Swarm under Multiple Environmental Threats

Fan,

Li,

Chen

et al. 2024

Drones

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“…In 2012, Subramanian et al [15] used APF for underwater path search and found that the basic APF could not fulfill the requirements of AUV path planning. the literature on APF improvement includes [16], [17]. Besides, rapidly exploring random tree (RRT), fast marching (FM), and D* algorithm also belong to the traditional path planning algorithm.…”

Section: Introductionmentioning

confidence: 99%

Path Planning for Autonomous Underwater Vehicles Based on an Improved Artificial Jellyfish Search Algorithm in Multi-Obstacle Ocean Current Environment

2023

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Path planning for autonomous underwater vehicles (AUVs) is a key research focus in the marine domain, requiring consideration of the underwater environment's complexity and the efficiency of the planning algorithms. Firstly, this paper uses the grid method to construct a realistic ocean terrain for the underwater environment and incorporates obstacles and ocean currents. Also, the AUV's movement strategy and posture are illustrated and constrained by a visual search strategy. Secondly, an improved artificial jellyfish search (IJS) algorithm is to overcome the drawbacks of low convergence accuracy and adaptability of the basic algorithm. The IJS algorithm determines the optimal path point by optimizing the objective function. This objective function not only takes into account factors such as path navigation time and path safety but also designs an ocean current disturbance model, which facilitates the AUV to successfully reach the target point while avoiding obstacles and strong side currents. Finally, the optimal smoothed path is output using a cubic spline midpoint interpolation method. Multiple simulation experiments are carried out on the constructed environment model. The comparison results show that the IJS algorithm with a short running time has the optimal time cost and ocean current penalty cost for the planned path. In addition, the IJS algorithm is also shown to be adaptable in the field of multi-AUV movements.

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Underwater dynamic modeling and experiments of flexible structure with harmonic actuation of macro fiber composites

Jin,

Lou,

Chen

et al. 2024

Mechanics of Advanced Materials and Structures

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Three-Dimensional Underwater Path Planning of Submarine Considering the Real Marine Environment

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 3 publications

References 27 publications

A Path-Planning Method for UAV Swarm under Multiple Environmental Threats

A Path-Planning Method for UAV Swarm under Multiple Environmental Threats

Path Planning for Autonomous Underwater Vehicles Based on an Improved Artificial Jellyfish Search Algorithm in Multi-Obstacle Ocean Current Environment

Underwater dynamic modeling and experiments of flexible structure with harmonic actuation of macro fiber composites

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