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A Robust Collision Perception Visual Neural Network With Specific Selectivity to Darker Objects
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DOI：10.1109/TCYB.2019.2946090.png)
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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.
Keyword:
Visualization
Collision avoidance
Neurons
Visual systems
Robots
Detectors
Collision perception
darker objects selectivity
lobula giant movement detectors (LGMD)2
ON and OFF pathways
mobile robots
neuron model
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
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OAAI分享收藏Shaping the collision selectivity in a looming sensitive neuron model with parallel ON and OFF pathways and spike frequency adaptation在具有并行开和关路径和尖峰频率自适应的隐现敏感神经元模型中塑造碰撞选择性NEURAL NETWORKS
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