The optimal route planning for inspection task of autonomous underwater vehicle composed of MOPSO-based dynamic routing algorithm in currents

Yu Hsien Lin, Lin Chin Huang, Shao Yu Chen, Chao Ming Yu

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

For supporting the dynamic routing plan more efficiently, this study has been established by integrating PSO (Particle Swarm Optimization) −based dynamic routing algorithm, self-tuning fuzzy controller, a stereo-vision detection technique and 6-DOF mathematical model into the inspection system of AUV (Autonomous Underwater Vehicle). Specifically, the PSO-based dynamic routing algorithm is modified by adopting the concept of Multi-Objective Particle Swarm Optimization (MOPSO), which is able to handle different weights of objectives in parallel. Therefore, a modular structure is applied to program design of the system by using the graphical language, LabVIEW®, which is composed of 6-DOF motion module, a self-tuning fuzzy control module, a stereo-vision detection module, and a dynamic routing module. Performances resulted from the MOPSO-based dynamic routing algorithm would be discussed by conducting a series of inspection tasks in the imitated offshore wind farm. Additionally, selections of fixed weight and dynamic weight of MOPSO-based dynamic routing algorithm would be compared via Pareto frontiers for feasible solutions of both sailing time and energy consumption. Eventually, it is verified that the MOPSO-based dynamic routing algorithm in our system is not only able to estimate the feasible routes intelligently, but also identify features of underwater structures for the purpose of positioning.

Original languageEnglish
Pages (from-to)178-192
Number of pages15
JournalApplied Ocean Research
Volume75
DOIs
Publication statusPublished - 2018 Jun

All Science Journal Classification (ASJC) codes

  • Ocean Engineering

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