Path planning method for UUV homing and docking in movement disorders environment.

Zheping Yan,Dongnan Chi,Tao Chen,Shuping Hou,Chao Deng

doi:10.1155/2014/246469

Zheping Yan, Dongnan Chi + Show 3 more

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https://doi.org/10.1155/2014/246469

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Journal: TheScientificWorldJournal	Publication Date: Jan 1, 2014
Citations: 27	License type: CC BY 3.0

Affiliation: Harbin Engineering University

Abstract

Path planning method for unmanned underwater vehicles (UUV) homing and docking in movement disorders environment is proposed in this paper. Firstly, cost function is proposed for path planning. Then, a novel particle swarm optimization (NPSO) is proposed and applied to find the waypoint with minimum value of cost function. Then, a strategy for UUV enters into the mother vessel with a fixed angle being proposed. Finally, the test function is introduced to analyze the performance of NPSO and compare with basic particle swarm optimization (BPSO), inertia weight particle swarm optimization (LWPSO, EPSO), and time-varying acceleration coefficient (TVAC). It has turned out that, for unimodal functions, NPSO performed better searching accuracy and stability than other algorithms, and, for multimodal functions, the performance of NPSO is similar to TVAC. Then, the simulation of UUV path planning is presented, and it showed that, with the strategy proposed in this paper, UUV can dodge obstacles and threats, and search for the efficiency path.

Highlights

Unmanned underwater vehicles (UUV) were first designed for military purposes
The comparison results elucidate that the searching accuracy and stability arranged from low to high are basic particle swarm optimization (BPSO), linear decreasing inertia weight PSO (LWPSO), EPSO, time-varying acceleration coefficient (TVAC), and novel particle swarm optimization (NPSO) for unimodal function
With regard to high multimodal function, the performance of TVAC is superior to NPSO

Summary

Introduction

Unmanned underwater vehicles (UUV) were first designed for military purposes. Li et al [3] proposed a novel artificial bee colony (ABC) algorithm for unmanned combat aerial vehicles (UCAVs) path planning. Mansury et al [4] have proposed a solution to the problem of path planning using artificial bee colony (ABC) algorithm and cubic Ferguson splines. Khelchandra and Jie [5] proposed a path planning method that is based on random sampling. Their proposed method has shown a high probability to find collision-free paths in short time. FernándezPerdomo et al [6] proposed a novel path planning algorithm

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Conclusion