Complete coverage path planning (CCPP) is an essential issue for Autonomous Underwater Vehicles’ (AUV) tasks, such as submarine search operations and complete coverage ocean explorations. A CCPP approach based on biologically inspired neural network is proposed for AUVs in the context of completely unknown environment. The AUV path is autonomously planned without any prior knowledge of the time-varying workspace, without explicitly optimizing any global cost functions, and without any learning procedures. The simulation studies show that the proposed approaches are capable of planning more reasonable collision-free complete coverage paths in unknown underwater environment.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    An Algorithm of Complete Coverage Path Planning for Autonomous Underwater Vehicles



    Published in:

    Key Engineering Materials ; 467-469 ; 1377-1385


    Publication date :

    2011-02-21


    Size :

    9 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Case-Based Path Planning for Autonomous Underwater Vehicles

    Vasudevan, C. / Ganesan, K. | British Library Online Contents | 1996


    Three Dimensional Path Planning of Autonomous Underwater Vehicles

    Peng, Yan ;Wu, Wei Qing ;Liu, Mei | Trans Tech Publications | 2013



    Online path planning for autonomous underwater vehicles in unknown environments

    Hernandez, Juan David / Vidal, Eduard / Vallicrosa, Guillem et al. | IEEE | 2015


    3D Path Planning of Autonomous Underwater Vehicles Using a Rapidly-exploring Random Trees Algorithm

    Arifi, Ali / Lepagnot, Julien / Bouallegue, Soufiene et al. | IEEE | 2023