Intelligent Path Planning for AUVs in Dynamic Environments: An EDA-Based Learning Fixed Height Histogram Approach

Liu, Run-Dong; Chen, Zong-Gan; Wang, Zi-Jia; Zhan, Zhi‐Hui

doi:10.1109/access.2019.2960859

Cited by 19 publications

(7 citation statements)

References 42 publications

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“…In 2018, Sun et al established a three-dimensional Glasius bionic neural network model to represent the three-dimensional underwater working environment, which independently planned the path according to the activity of neurons [119]. In 2019, Liu et al proposed a learning fixed-height histogram (LFHH) method based on the estimation of distribution algorithm to solve the path planning in the 3D environment with current and moving obstacles [120]. Sometimes, the 3D underwater environment can be mapped to the horizontal plane and vertical plane to solve 3D path planning.…”

Section: Direction E: 3d Path Planning Algorithmsmentioning

confidence: 99%

Research Progress of Path Planning Methods for Autonomous Underwater Vehicle

Guo

Liu

Fan

et al. 2021

Mathematical Problems in Engineering

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Path planning is a key technology for autonomous underwater vehicle (AUV) navigation. With the emphasis and research on AUV, AUV path planning technology is continuously developing. Path planning techniques generally include environment modelling methods and path planning algorithms. Based on a brief description of the environment modelling methods, this paper focuses on the path planning algorithms commonly used by AUV. According to the basic principles of the algorithm, the AUV path planning algorithms are divided into four categories: artificial potential field methods, geometric model search methods, random sampling methods, and intelligent bionic methods. In this review, we summarize in detail the development and application of various path planning algorithms in recent years. Meanwhile, we analyse the advantages and disadvantages of various algorithms and their improvement methods. Obstacles, ocean currents, and undersea terrain have an impact on AUV path planning. Therefore, how to deal with the complex underwater environment adds some limits to AUV path planning algorithms. In addition to the external environment, path planning algorithms also need to consider AUV’s physical constraints, such as energy constraints and motion constraints. Then, we analyse the motion constraints in AUV path planning. Finally, we discuss the development direction of AUV path planning algorithm. Time-varying ocean currents, special obstacles, multiobjective constraints, and practicability will be the problems that AUV path planning algorithms need to solve.

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Section: Direction E: 3d Path Planning Algorithmsmentioning

confidence: 99%

Research Progress of Path Planning Methods for Autonomous Underwater Vehicle

Guo

Liu

Fan

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…In response to this issue, many scholars have focused on the development of underwater path planning algorithms. At present, they can be categorized into graph search-based approaches, such as Dijsktra algorithm [ 1 , 2 , 3 ],

algorithm [ 4 , 5 ]; sample planning-based approaches, such as PRM algorithm [ 6 , 7 ],

algorithm [ 8 , 9 ]; artificial potential field (APF)-based approaches [ 10 , 11 ]; evolutionary algorithms (EAs)-based approaches, such as distribution estimation algorithm (EDA) [ 12 ], particle swarm optimization (PSO) [ 13 , 14 ], genetic algorithm (GA) [ 15 , 16 ], differential evolution algorithm (DE) [ 17 ]; heuristic algorithms (HAs)-based approaches, such as ant colony algorithm (ACO) [ 18 , 19 ], simulated annealing algorithm (SA) [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Underwater Submarine Path Planning Based on Artificial Potential Field Ant Colony Algorithm and Velocity Obstacle Method

2022

Sensors

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Navigating safely in complex marine environments is a challenge for submarines because proper path planning underwater is difficult. This paper decomposes the submarine path planning problem into global path planning and local dynamic obstacle avoidance. Firstly, an artificial potential field ant colony algorithm (APF-ACO) based on an improved artificial potential field algorithm and improved ant colony algorithm is proposed to solve the problem of submarine underwater global path planning. Compared with the Optimized ACO algorithm proposed based on a similar background, the APF-ACO algorithm has a faster convergence speed and better path planning results. Using an inflection point optimization algorithm greatly reduces the number and length of inflection points in the path. Using the Clothoid curve fitting algorithm to optimize the path results, a smoother and more stable path result is obtained. In addition, this paper uses a three-dimensional dynamic obstacle avoidance algorithm based on the velocity obstacle method. The experimental results show that the algorithm can help submarines to identify threatening dynamic obstacles and avoid collisions effectively. Finally, we experimented with the algorithm in the submarine underwater semi-physical simulation system, and the experimental results verified the effectiveness of the algorithm.

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“…To solve this problem，Cheng et al added the speed synthesis algorithm of AUV to the APF algorithm, which improved the convergence speed of the algorithm while avoiding obstacles accurately [16]. Jantapremjit et al not only realize automatic obstacle avoidance by applying the APF algorithm, but also introduced the state-dependent Riccati equation method to optimize the optimal high-order sliding mode control, which improved the robustness of the AUV motion [17].…”

Section: Introductionmentioning

confidence: 99%