Constrained path planning of autonomous underwater vehicle using selectively-hybridized particle swarm optimization algorithms

Lim, Hui Sheng; Fan, Shuangshuang; Chin, Christopher; Chai, Shuhong; Bose, Neil; Kim, Eonjoo

doi:10.1016/j.ifacol.2019.12.326

Cited by 26 publications

(9 citation statements)

References 10 publications

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“…Heuristic searching methods have become very popular in recent years. The inspiration of heuristic searching methods mostly comes from the working mechanism or foraging behavior of kinds of substances or animals in nature, e.g., the A* algorithm [15], the Dijkstra algorithm [16], the genetic algorithm [17], particle swarm optimization [18], etc. The principle of this type of method is to search for a relatively optimal location within a range that is free of danger and generate the optimal path with the lowest total cost (path length, energy consumption, etc.).…”

Section: Global Path Planningmentioning

confidence: 99%

Path Planning for Autonomous Ships: A Hybrid Approach Based on Improved APF and Modified VO Methods

Zhang

Mou

Chen

et al. 2021

JMSE

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In this research, a hybrid approach for path planning of autonomous ships that generates both global and local paths, respectively, is proposed. The global path is obtained via an improved artificial potential field (APF) method, which makes up for the shortcoming that the typical APF method easily falls into a local minimum. A modified velocity obstacle (VO) method that incorporates the closest point of approach (CPA) model and the International Regulations for Preventing Collisions at Sea (COLREGS), based on the typical VO method, can be used to get the local path. The contribution of this research is two-fold: (1) improvement of the typical APF and VO methods, making up for previous shortcomings, and integrated COLREGS rules and good seamanship, making the paths obtained more in line with navigation practice; (2) the research included global and local path planning, considering both the safety and maneuverability of the ship in the process of avoiding collision, and studied the whole process of avoiding collision in a relatively entirely way. A case study was then conducted to test the proposed approach in different situations. The results indicate that the proposed approach can find both global and local paths to avoid the target ship.

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Section: Global Path Planningmentioning

confidence: 99%

Path Planning for Autonomous Ships: A Hybrid Approach Based on Improved APF and Modified VO Methods

Zhang

Mou

Chen

et al. 2021

JMSE

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“…e algorithm can generate a smooth and feasible path under hard constraints of boundary conditions and soft constraints of obstacle avoidance [111]. In 2020, Wang et al combined PSO with cubic spline interpolation to achieve obstacle avoidance and path optimization.…”

Section: Direction B: Fusion Of Multiple 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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“…Zhuang et al proposed a two‐stage cooperative path planner for multiple AUVs operating in a dynamic environment, and the method avoided moving obstacles 29 . Lim et al tried to solve the constrained path planning of AUVs using a selectively hybridized PSO algorithm, and the proposed algorithm offered good stability and computational efficiency 30 . Miao et al combined a symbiotic organism search algorithm with the simplex method for solving the route planning problem, and the proposed algorithm provided faster convergence speed, higher precision, and stronger robustness 31 .…”

Section: Introductionmentioning

confidence: 99%

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

Yan

Zhang

Yang

et al. 2020

Concurrency and Computation

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This paper presents a whale optimization algorithm (WOA) based on forward looking sonar to achieve two‐dimensional optimal path planning for an autonomous underwater vehicle. The purpose of path planning is not only to effectively avoid threat regions and safely reach the intended target with minimum fuel cost but also to obtain an optimal or near‐optimal path in a complex ocean battlefield environment. The WOA, based on the bubble‐net attacking behavior of humpback whales, mimics encircling the prey, attacks with a bubble‐net method, and search for prey to effectively determine the global optimal solution in the search space. The WOA not only has fast convergence speed and high calculation accuracy but can also effectively balance exploration and exploitation to avoid falling into a local optimum and obtain the global optimal solution. Five sets of experiments are applied to verify the superiority and stability of the WOA. Compared with other algorithms, such as artificial bee colony, bat algorithm, cuckoo search, flower pollination algorithm, moth‐flame optimization algorithm, particle swarm optimization, and water wave optimization, the WOA exhibits better optimization performance and stronger robustness. The experimental results reveal that the WOA can find the shortest path compared with all the other algorithms, and it is an effective and feasible method for solving the path planning problem.

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Constrained path planning of autonomous underwater vehicle using selectively-hybridized particle swarm optimization algorithms

Cited by 26 publications

References 10 publications

Path Planning for Autonomous Ships: A Hybrid Approach Based on Improved APF and Modified VO Methods

Path Planning for Autonomous Ships: A Hybrid Approach Based on Improved APF and Modified VO Methods

Research Progress of Path Planning Methods for Autonomous Underwater Vehicle

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

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