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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Neurorobot. | doi: 10.3389/fnbot.2019.00029

Retina-based Pipe-like Object Tracking Implemented through Spiking Neural Network on a Snake Robot

  • 1Technische Universität München, Germany
  • 2Sun Yat-sen University, China

Target tracking ability based on vision is crucial to a bio-inspired snake robot for exploring
an unknown environment. However, the traditional vision modules of snake robots are difficult
to overcome the image blur resulting from the periodic swing. A promising approach is to use
the neuromorphic vision sensor (NVS) mimicking the biological retina to detect a target at the
higher temporal frequency and in the wider dynamic range. In this study, an NVS and a spiking
neural network (SNN) were performed on a snake robot for the first time to achieve pipe-like
object tracking. An SNN based on Hough Transform was designed to detect a target with an
asynchronous event stream fed by the NVS. Combining the state of snake motion analyzed by
the joint position sensors, a tracking framework was proposed. The experimental results obtained
from the simulator demonstrated the validity of our framework and the autonomous locomotion
ability of our snake robot. Comparing the SNN model performances respectively on CPUs, GPUs,
the SNN model showed the best performance on a GPU under a simplified and synchronous
update rule while it possessed higher precision on a CPU in an asynchronous way.

Keywords: Neuromorphic vision, Spiking Neuromorphic Network, Hough transform, Snake robot, target tracking

Received: 01 Jan 2019; Accepted: 07 May 2019.

Edited by:

Guanghua Xu, Xi'an Jiaotong University, China

Reviewed by:

Guoyuan Li, NTNU Ålesund, Norway
Zonghua Gu, Zhejiang University, China  

Copyright: © 2019 Jiang, Bing, Huang and Knoll. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mr. Zhenshan Bing, Technische Universität München, Munich, 80333, Bavaria, Germany, bing@in.tum.de