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Lookup NU author(s): Dr Claire RindORCiD
This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE, 2020.
For re-use rights please refer to the publisher's terms and conditions.
Building an efficient and reliable collision perception visual system is a challenging problem for future robots and autonomous vehicles. The biological visual neural networks, which have evolved over millions of years in nature and are working perfectly in the real world, could be ideal models for designing artificial vision systems. In the locust’s visual pathways, a lobula giant movement detector (LGMD), that is, the LGMD2, has been identified as a looming perception neuron that responds most strongly to darker approaching objects relative to their backgrounds; similar situations which many ground vehicles and robots are often faced with. However, little has been done on modeling the LGMD2 and investigating its potential in robotics and vehicles. In this article, we build an LGMD2 visual neural network which possesses the similar collision selectivity of an LGMD2 neuron in locust via the modeling of biased-ON and –OFF pathways splitting visual signals into parallel ON/OFF channels. With stronger inhibition (bias) in the ON pathway, this model responds selectively to darker looming objects. The proposed model has been tested systematically with a range of stimuli including real-world scenarios. It has also been implemented in a micro-mobile robot and tested with real-time experiments. The experimental results have verified the effectiveness and robustness of the proposed model for detecting darker looming objects against various dynamic and cluttered backgrounds.
Author(s): Fu Q, Hu C, Peng J, Rind FC, Yue S
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Cybernetics
Year: 2020
Volume: 50
Issue: 12
Pages: 5074-5088
Print publication date: 01/12/2020
Online publication date: 04/12/2019
Acceptance date: 18/09/2019
Date deposited: 28/01/2020
ISSN (print): 2168-2267
ISSN (electronic): 2168-2275
Publisher: IEEE
URL: https://doi.org/10.1109/TCYB.2019.2946090
DOI: 10.1109/TCYB.2019.2946090
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