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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
作者部门水下机器人研究室
会议名称2018 International Conference on Electronics and Electrical Engineering Technology, EEET 2018
会议日期September 19-21, 2018
会议地点Tianjin, China
会议录名称Proceedings of 2018 International Conference on Electronics and Electrical Engineering Technology, EEET 2018
出版者ACM
出版地New York
2018
页码128-134
收录类别EI
EI收录号20190506448938
产权排序1
ISBN号978-1-4503-6541-3
关键词Bio-inspired model Collision avoidance Multi-AUV Path planning
摘要Aiming 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.
语种英语
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/24132
专题水下机器人研究室
通讯作者Wu L(邬炼)
作者单位1.State Key Laboratory of Robotics, Shenyang Institute of Automation, CAS, Shenyang, China
2.College of Information Science and Engineering, Northeastern University, Shenyang, China
推荐引用方式
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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