Two‐dimensional optimal path planning for autonomous underwater vehicle using a whale optimization algorithm

2020 ◽  
Author(s):  
Zheping Yan ◽  
Jinzhong Zhang ◽  
Zewen Yang ◽  
Jialing Tang
Keyword(s):  
Path Planning ◽  
Optimization Algorithm ◽  
Autonomous Underwater Vehicle ◽  
Optimal Path ◽  
Underwater Vehicle ◽  
Whale Optimization Algorithm ◽  
Two Dimensional ◽  
Optimal Path Planning ◽  
Whale Optimization

Autonomous underwater vehicle optimal path planning method for seabed terrain matching navigation

2017 ◽  
Vol 133 ◽  
pp. 107-115 ◽  
Author(s):  
Ye Li ◽  
Teng Ma ◽  
Pengyun Chen ◽  
Yanqing Jiang ◽  
Rupeng Wang ◽  
...  
Keyword(s):  
Path Planning ◽  
Autonomous Underwater Vehicle ◽  
Optimal Path ◽  
Underwater Vehicle ◽  
Planning Method ◽  
Optimal Path Planning ◽  
Terrain Matching

Whale optimization algorithm based on lateral inhibition for image matching and vision-guided AUV docking

2020 ◽  
pp. 1-12
Author(s):  
Zheping Yan ◽  
Jinzhong Zhang ◽  
Jialing Tang
Keyword(s):  
Lateral Inhibition ◽  
Optimization Algorithm ◽  
Image Matching ◽  
Autonomous Underwater Vehicle ◽  
Optimal Solution ◽  
Search Space ◽  
Whale Optimization Algorithm ◽  
Inhibition Mechanism ◽  
Whale Optimization ◽  
Accuracy And Stability

The accuracy and stability of relative pose estimation of an autonomous underwater vehicle (AUV) and a target depend on whether the characteristics of the underwater image can be accurately and quickly extracted. In this paper, a whale optimization algorithm (WOA) based on lateral inhibition (LI) is proposed to solve the image matching and vision-guided AUV docking problem. The proposed method is named the LI-WOA. The WOA is motivated by the behavior of humpback whales, and it mainly imitates encircling prey, bubble-net attacking and searching for prey to obtain the globally optimal solution in the search space. The WOA not only balances exploration and exploitation but also has a faster convergence speed, higher calculation accuracy and stronger robustness than other approaches. The lateral inhibition mechanism can effectively perform image enhancement and image edge extraction to improve the accuracy and stability of image matching. The LI-WOA combines the optimization efficiency of the WOA and the matching accuracy of the LI mechanism to improve convergence accuracy and the correct matching rate. To verify its effectiveness and feasibility, the WOA is compared with other algorithms by maximizing the similarity between the original image and the template image. The experimental results show that the LI-WOA has a better average value, a higher correct rate, less execution time and stronger robustness than other algorithms. The LI-WOA is an effective and stable method for solving the image matching and vision-guided AUV docking problem.

Middle Node Optimization algorithm for global optimal path planning

2016 ◽  
Author(s):  
Ananya Chandra ◽  
Vibhu Jawa ◽  
Ishan Mishra ◽  
M. Sameer Khan
Keyword(s):  
Path Planning ◽  
Optimization Algorithm ◽  
Optimal Path ◽  
Optimal Path Planning ◽  
Middle Node ◽  
Global Optimal

Multicriterion Offline Path Planning of a Biomimetic Underwater Vehicle Using NSGA II

2010 ◽  
Author(s):  
Mansour Ataei ◽  
Aghil Yousefi-Koma ◽  
Masoud Shariat Panahi
Keyword(s):  
Path Planning ◽  
Pareto Front ◽  
Optimal Path ◽  
Underwater Vehicle ◽  
Pareto Optimal Solutions ◽  
Freedom Of Choice ◽  
Nsga Ii ◽  
Optimal Path Planning ◽  
Ga Algorithm ◽  
Margin Of Safety

In this paper an optimal 3-D path is generated offline for a Biomimetic Underwater Vehicle (BUV). The BUV swims forward by oscillating its body, turns by curving its body and dives by bending its head. The BUV is intended to systematically plan its path having only the initial and final points and the positions and dimensions of the obstacles. The four widely-accepted criteria of the optimal path planning of the BUV are “overall path length”, “margin of safety”, “smoothness of planar motion” and “gradient of dive”. In this study the multi-objective GA algorithm NSGA-II is employed to find a set of Pareto-optimal solutions where each solution represents a path that cannot be outrun by any other path considering all four criteria. The solution set, also called the Pareto front, gives the designer the freedom of choice when it comes to prioritizing various criteria.

scholarly journals Cognition-based hybrid path planning for autonomous underwater vehicle target following

2019 ◽  
Vol 16 (4) ◽  
pp. 172988141985755 ◽  
Author(s):  
Li Yue Ming ◽  
Huang Hai ◽  
Xu Yang ◽  
Zhang Guocheng ◽  
Li Jiyong ◽  
...  
Keyword(s):  
Path Planning ◽  
Motion Planning ◽  
Target Detection ◽  
Optimization Algorithm ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Cognitive Architecture ◽  
Underwater Vehicle ◽  
Fuzzy Rules ◽  
Mobile Target

Intelligent path planning is one of the key techniques for autonomous underwater vehicles for the purpose of target detection, environmental survey and so on. In order to realize automatic motion plan, an intelligent cognitive architecture for autonomous underwater vehicle motion planning has been proposed to realize complicated target detection and mobile target following in the disturbance environment. A novel adaptive ant colony optimization and particle swarm optimization fusion-based fuzzy rules optimization algorithm has been proposed to generate optimized fuzzy rules. Through this optimization algorithm, the preliminary fuzzy rules can be optimized to realize intelligent motion planning for complicated operation tasks. Experiments of channel following for wall detection and mobile target following in the oceanic environment have verified the validity of path planning method in the implementation of detection and operation tasks.

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