计算机科学
稳健性(进化)
职位(财务)
网格
欧拉角
人工智能
计算机视觉
实时计算
数学
几何学
财务
生物化学
基因
经济
化学
作者
Lijun Chao,Zhi Xiong,Jianye Liu,Chuang Yang,Yudi Chen
出处
期刊:Aircraft Engineering and Aerospace Technology
[Emerald (MCB UP)]
日期:2021-08-09
卷期号:93 (7): 1221-1228
标识
DOI:10.1108/aeat-09-2020-0194
摘要
Purpose To solve problems of low intelligence and poor robustness of traditional navigation systems, the purpose of this paper is to propose a brain-inspired localization method of the unmanned aerial vehicle (UAV). Design/methodology/approach First, the yaw angle of the UAV is obtained by modeling head direction cells with one-dimension continuous attractor neural network (1 D-CANN) and then inputs into 3D grid cells. After that, the motion information of the UAV is encoded as the firing of 3 D grid cells using 3 D-CANN. Finally, the current position of the UAV can be decoded from the neuron firing through the period-adic method. Findings Simulation results suggest that continuous yaw and position information can be generated from the conjunctive model of head direction cells and grid cells. Originality/value The proposed period-adic cell decoding method can provide a UAV with the 3 D position, which is more intelligent and robust than traditional navigation methods.
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