A bioinspired angular velocity decoding neural network model for visually guided flights

Wang, Huatian, Fu, Qinbing, Wang, Hongxin , Baxter, Paul, Peng, Jigen and Yue, Shigang (2020) A bioinspired angular velocity decoding neural network model for visually guided flights. Neural Networks . ISSN 0893-6080

Full content URL: https://doi.org/10.1016/j.neunet.2020.12.008

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Abstract

Efficient and robust motion perception systems are important pre-requisites for achieving visually guided flights in future micro air vehicles. As a source of inspiration, the visual neural networks of flying insects such as honeybee and Drosophila provide ideal examples on which to base artificial motion perception models. In this paper, we have used this approach to develop a novel method that solves the fundamental problem of estimating angular velocity for visually guided flights. Compared with previous models, our elementary motion detector (EMD) based model uses a separate texture estimation pathway to effectively decode angular velocity, and demonstrates considerable independence from the spatial frequency and contrast of the gratings. Using the Unity development platform the model is further tested for tunnel centering and terrain following paradigms in order to reproduce the visually guided flight behaviors of honeybees. In a series of controlled trials, the virtual bee utilizes the proposed angular velocity control schemes to accurately navigate through a patterned tunnel, maintaining a suitable distance from the undulating textured terrain. The results are consistent with both neuron spike recordings and behavioral path recordings of real honeybees, thereby demonstrating the model’s potential for implementation in micro air vehicles which have only visual sensors.

Keywords:Motion perception, Angular velocity, Flight control, Tunnel centering, Terrian following
Subjects:G Mathematical and Computer Sciences > G730 Neural Computing
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:43627
Deposited On:12 Jan 2021 10:17

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