A bioinspired angular velocity decoding neural network model for visually guided flights
Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:136 |
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| Enthalten in: |
Neural networks : the official journal of the International Neural Network Society - 136(2021) vom: 22. Apr., Seite 180-193 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Huatian [VerfasserIn] |
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| Links: |
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| Themen: |
Angular velocity |
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| Anmerkungen: |
Date Completed 04.05.2021 Date Revised 04.05.2021 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.neunet.2020.12.008 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM320560775 |
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| 245 | 1 | 2 | |a A bioinspired angular velocity decoding neural network model for visually guided flights |
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| 500 | |a Date Revised 04.05.2021 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved. | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Angular velocity | |
| 650 | 4 | |a Flight control | |
| 650 | 4 | |a Insect vision | |
| 650 | 4 | |a Motion perception | |
| 650 | 4 | |a Terrain following | |
| 650 | 4 | |a Tunnel centering | |
| 700 | 1 | |a Fu, Qinbing |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Hongxin |e verfasserin |4 aut | |
| 700 | 1 | |a Baxter, Paul |e verfasserin |4 aut | |
| 700 | 1 | |a Peng, Jigen |e verfasserin |4 aut | |
| 700 | 1 | |a Yue, Shigang |e verfasserin |4 aut | |
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