An Algorithm of Complete Coverage Path Planning for Autonomous Underwater Vehicles

Abstract:

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.

• Periodical: Key Engineering Materials (Volumes 467-469)
• Pages: 1377-1385
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.467-469.1377
• Online since: February 2011
• Authors: Ming Zhong Yan, Da Qi Zhu
• Keywords: Autonomous Underwater Vehicle (AUV), Complete Coverage Path Planning (CCPP), Neural Network (NN)

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