Bio-inspired collision detector with enhanced selectivity for ground robotic vision system

Fu, Qinbing and Yue, Shigang and Hu, Cheng (2016) Bio-inspired collision detector with enhanced selectivity for ground robotic vision system. In: 27th British Machine Vision Conference, 19-22 September 2016, York, UK.

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Abstract

There are many ways of building collision-detecting systems. In this paper, we propose a novel collision selective visual neural network inspired by LGMD2 neurons in the juvenile locusts. Such collision-sensitive neuron matures early in the first-aged or even hatching locusts, and is only selective to detect looming dark objects against bright background in depth, represents swooping predators, a situation which is similar to ground robots or vehicles. However, little has been done on modeling LGMD2, let alone its potential applications in robotics and other vision-based areas. Compared to other collision detectors, our major contributions are first, enhancing the collision selectivity in a bio-inspired way, via constructing a computing efficient visual sensor, and realizing the revealed specific characteristic sofLGMD2. Second, we applied the neural network to help rearrange path navigation of an autonomous ground miniature robot in an arena. We also examined its neural properties through systematic experiments challenged against image streams from a visual sensor of the micro-robot.

Keywords:LGMD2, collision-detecting system, collision selectivity, ground robotic application, juvenile locusts
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
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ID Code:24941
Deposited On:11 Nov 2016 09:28

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