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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    Title :

    A brain-inspired localization system for the UAV based on navigation cells


    Contributors:
    Chao, Lijun (author) / Xiong, Zhi (author) / Liu, Jianye (author) / Yang, Chuang (author) / Chen, Yudi (author)

    Published in:

    Publication date :

    2021-08-09


    Size :

    1 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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