Multi-AUV Complete Coverage Path Planning Based on Improved Neural Network

doi:10.16182/j.issn1004731x.joss.19-0012

Abstract

Abstract: Aiming at the working space search task of multiple AUVs (Autonomous Underwater Vehicle) in 3-dimensional underwater environments, a complete coverage path planning algorithm based on an improved neural network-Glasius Bio-inspired Neural Network (GBNN) is presented in this paper. A discrete 3-D grid map of the underwater environment is constructed. A 3-D GBNN model is established topologically according to the map. Based on the dynamic activities of GBNN model, each AUV plans its own coverage path independently, and covers the whole working space collaboratively. The simulation results show that the multiple AUVs can collaboratively cover the working space completely, automatically avoid the obstacle and escape from the deadlock in the path.

Key words: multi-AUV, 3-D environment, complete coverage path planning, Glasius Bio-inspired Neural Network

CLC Number:

TP273

Zhu Daqi, Zhu Tingting, Yan Mingzhong. Multi-AUV Complete Coverage Path Planning Based on Improved Neural Network[J]. Journal of System Simulation, 2020, 32(8): 1505-1514.

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