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Based on improved bio-inspired model for path planning by multi-AUV
Wu L(邬炼)1,2; Li YP(李一平)1,2; Liu J(刘健)1,2
Department水下机器人研究室
Conference Name2018 International Conference on Electronics and Electrical Engineering Technology, EEET 2018
Conference DateSeptember 19-21, 2018
Conference PlaceTianjin, China
Source PublicationProceedings of 2018 International Conference on Electronics and Electrical Engineering Technology, EEET 2018
PublisherACM
Publication PlaceNew York
2018
Pages128-134
Indexed ByEI
EI Accession number20190506448938
Contribution Rank1
ISBN978-1-4503-6541-3
KeywordBio-inspired model Collision avoidance Multi-AUV Path planning
AbstractAiming at path planning and collision avoidance of multiple autonomous underwater vehicle (AUV) system under complex environment, an improved neural network algorithm based on biological inspired model is proposed. Firstly, establishing an improved bio-inspired neural network model, the two-dimensional working area is rasterized, and each grid and neuron are one-to-one correspondence, stipulating that the interest area and the obstacle area of the grid correspond to the excitatory and inhibition of neurons respectively, affecting the neurons activity in the whole working area by the transversal function of the adjacent neurons each other. Secondly, AUV plans a safe and collision-free path by comparing the size of the activity of neighbor neurons. Then, Aiming at the problem that AUV moves clinging to the edge of obstacles, adding lateral inhibitory effects of the obstacles on the neural network and greatly improving the safety and rationality of the path planning. Finally, changing the property of grid positions of each AUV in real time to realize collision avoidance between multi-AUV. Simulation experiments prove that the improved algorithm is valid about the path planning in this thesis and the large allowance collision avoidance problem in a complex environment with single-AUV and multi-AUV. © 2018 Association for Computing Machinery.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/24132
Collection水下机器人研究室
Corresponding AuthorWu L(邬炼)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, CAS, Shenyang, China
2.College of Information Science and Engineering, Northeastern University, Shenyang, China
Recommended Citation
GB/T 7714
Wu L,Li YP,Liu J. Based on improved bio-inspired model for path planning by multi-AUV[C]. New York:ACM,2018:128-134.
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