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Complete Coverage Path Planning of Autonomous Underwater Vehicle Based on GBNN Algorithm
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Complete Coverage Path Planning of Autonomous Underwater Vehicle Based on GBNN Algorithm
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Journal Article
Complete Coverage Path Planning of Autonomous Underwater Vehicle Based on GBNN Algorithm
2019
Overview
For the shortcomings of biologically inspired neural network algorithm in the path planning of robots, such as high computational complexity, long path planning time etc Glasius Bio-inspired Neural Network (GBNN) algorithm is proposed to improve the algorithm, and applied to the complete coverage path planning of autonomous underwater vehicle (AUV). Firstly, the grid map is constructed by discretizing the two-dimensional underwater environment. Secondly, the corresponding dynamic neural network is built on the grid map. Finally, complete coverage path of AUV is planned based on the GBNN strategy and the path of AUV at the edge of obstacles is optimized by some typical path templates. The simulation results show that the AUV can completely cover the entire workspace and immediately escape from deadlocks without any waiting. Meanwhile, the efficiency of complete coverage path planning is high with short path planning time and low overlapping coverage rate by using the algorithm proposed in this paper.
Publisher
Springer Netherlands,Springer,Springer Nature B.V
Subject
