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Retina-Based Pipe-Like Object Tracking Implemented Through Spiking Neural Network on a Snake Robot
Vision based-target tracking ability is crucial to bio-inspired snake robots for exploring unknown environments. However, it is difficult for the traditional vision modules of snake robots to overcome the image blur resulting from periodic swings. A promising approach is to use a neuromorphic vision...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6549545/ https://www.ncbi.nlm.nih.gov/pubmed/31191288 http://dx.doi.org/10.3389/fnbot.2019.00029 |
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author | Jiang, Zhuangyi Bing, Zhenshan Huang, Kai Knoll, Alois |
author_facet | Jiang, Zhuangyi Bing, Zhenshan Huang, Kai Knoll, Alois |
author_sort | Jiang, Zhuangyi |
collection | PubMed |
description | Vision based-target tracking ability is crucial to bio-inspired snake robots for exploring unknown environments. However, it is difficult for the traditional vision modules of snake robots to overcome the image blur resulting from periodic swings. A promising approach is to use a neuromorphic vision sensor (NVS), which mimics the biological retina to detect a target at a higher temporal frequency and in a 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 performances of the SNN model on CPUs and on GPUs, respectively, 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. |
format | Online Article Text |
id | pubmed-6549545 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-65495452019-06-12 Retina-Based Pipe-Like Object Tracking Implemented Through Spiking Neural Network on a Snake Robot Jiang, Zhuangyi Bing, Zhenshan Huang, Kai Knoll, Alois Front Neurorobot Neuroscience Vision based-target tracking ability is crucial to bio-inspired snake robots for exploring unknown environments. However, it is difficult for the traditional vision modules of snake robots to overcome the image blur resulting from periodic swings. A promising approach is to use a neuromorphic vision sensor (NVS), which mimics the biological retina to detect a target at a higher temporal frequency and in a 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 performances of the SNN model on CPUs and on GPUs, respectively, 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. Frontiers Media S.A. 2019-05-29 /pmc/articles/PMC6549545/ /pubmed/31191288 http://dx.doi.org/10.3389/fnbot.2019.00029 Text en Copyright © 2019 Jiang, Bing, Huang and Knoll. http://creativecommons.org/licenses/by/4.0/ 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. |
spellingShingle | Neuroscience Jiang, Zhuangyi Bing, Zhenshan Huang, Kai Knoll, Alois Retina-Based Pipe-Like Object Tracking Implemented Through Spiking Neural Network on a Snake Robot |
title | Retina-Based Pipe-Like Object Tracking Implemented Through Spiking Neural Network on a Snake Robot |
title_full | Retina-Based Pipe-Like Object Tracking Implemented Through Spiking Neural Network on a Snake Robot |
title_fullStr | Retina-Based Pipe-Like Object Tracking Implemented Through Spiking Neural Network on a Snake Robot |
title_full_unstemmed | Retina-Based Pipe-Like Object Tracking Implemented Through Spiking Neural Network on a Snake Robot |
title_short | Retina-Based Pipe-Like Object Tracking Implemented Through Spiking Neural Network on a Snake Robot |
title_sort | retina-based pipe-like object tracking implemented through spiking neural network on a snake robot |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6549545/ https://www.ncbi.nlm.nih.gov/pubmed/31191288 http://dx.doi.org/10.3389/fnbot.2019.00029 |
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