Based on improved bio-inspired model for path planning by multi-AUV

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 水下机器人研究室

	Based on improved bio-inspired model for path planning by multi-AUV
	Li YP(李一平)1,2 ; Liu J(刘健)1,2 ; Wu L(邬炼)1,2
	2018
会议日期	September 19-21, 2018
会议地点	Tianjin, China
关键词	Bio-inspired model Collision avoidance Multi-AUV Path planning
页码	128-134
英文摘要	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.
产权排序	1
会议录	Proceedings of 2018 International Conference on Electronics and Electrical Engineering Technology, EEET 2018
会议录出版者	ACM
会议录出版地	New York
语种	英语
ISBN号	978-1-4503-6541-3
WOS记录号	WOS:000482814400024
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/24132]
专题	沈阳自动化研究所_水下机器人研究室
通讯作者	Wu L(邬炼)
作者单位	1.College of Information Science and Engineering, Northeastern University, Shenyang, China 2.State Key Laboratory of Robotics, Shenyang Institute of Automation, CAS, Shenyang, China
推荐引用方式 GB/T 7714	Li YP,Liu J,Wu L. Based on improved bio-inspired model for path planning by multi-AUV[C]. 见:. Tianjin, China. September 19-21, 2018.