Path Plan of Unmanned Underwater Vehicle Using Particle Swarm Optimization

Sun, Jian; Liu, Xing

doi:10.2991/isrme-15.2015.358

Cited by 6 publications

(7 citation statements)

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“…The basic idea of finding the optimal path involves iterative methods in the process of bird movement via the individual cooperation mechanism in the group. Cao et al [20] and Sun and Liu [146] have adopted the PSO algorithm for obstacle avoidance and trajectory optimization of AUV path searching functions, and simulation experiments show that this algorithm is simple, easily implemented and is not very sensitive to the population size and has excellent robustness and a fast convergence speed. However, the particle swarm optimization algorithm also finds that the optimal solution is the local optimal solution.…”

Section: ) Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Path Planning Technologies for Autonomous Underwater Vehicles-A Review

Li¹,

Wang²,

Du³

2019

IEEE Access

139

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An autonomous underwater vehicle (AUV) is an economical and safe tool that is well-suited for search, investigation, identification, and salvage operations on the sea floor. Path planning technology, which primarily includes modeling methods and path search algorithms, is an important technology for AUVs. In recent years, the AUV path planning technology has rapidly developed. Compared with land robots, AUVs must endure complex underwater environments and consider various factors, such as currents, water pressure, and topography. Challenges exist in terms of online obstacle avoidance, three-dimensional environment path planning, and the robustness of the algorithms. Adapting a complex environment and finding a suitable path planning method comprise the main problem that must be solved. In this paper, we summarize the principles, advantages, and disadvantages of modeling and path search technologies for AUVs. The most prominent feature of this paper is to summarize the improvement methods of various technical shortcomings and improve the original methods, such as dynamic obstacle avoidance, optimization path, coverage, and processing speed. In addition to summarizing the characteristics of each algorithm, this paper intuitively demonstrates the experimental environment, the real-time nature, the path planning range of the AUV, and so on. We also discuss the application scenarios of various modeling and path search technologies for AUVs. In addition, we discuss the challenges of AUVs and the direction of future research. INDEX TERMS AUV, path planning, model building, path search.

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Section: ) Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Path Planning Technologies for Autonomous Underwater Vehicles-A Review

Li¹,

Wang²,

Du³

2019

IEEE Access

139

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“…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%

“…Liu et al used the PSO algorithm for AUV path planning. Simulation experiments show that the algorithm is simply easy to implement, not sensitive to the population size, and has a faster convergence speed [ 13 ]. Ma et al introduced alarm pheromones in the ACO algorithm (AP-ACO) for path planning of underwater vehicles.…”

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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“…But the algorithm is prone to fall into premature convergence [18]. The PSO algorithm performs path planning operations by simulating the individual cooperation mechanism in the group [19]. It has the advantages of easy implementation and rapid convergence, but it is easy to fall into the local optimal solution [20].…”

Section: Introductionmentioning

confidence: 99%

“…Liu et al used the PSO algorithm for AUV path planning. Simulation experiments show that the algorithm is simple easy to implement, not sensitive to the population size, and has a faster convergence speed [21]. GA is a computational model that simulates genetic selection and natural elimination.…”

Section: Introductionmentioning

confidence: 99%