Liu, Junxiu, Harkin, Jim, McDaid, Liam et al. (3 more authors) (2016) Self-repairing mobile robotic car using astrocyte-neuron networks. In: 2016 International Joint Conference on Neural Networks, IJCNN 2016. 2016 International Joint Conference on Neural Networks, IJCNN 2016, 24-29 Jul 2016 Proceedings of International Joint Conference on Neural Networks . IEEE , CAN , pp. 1379-1386.
Abstract
A self-repairing robot utilising a spiking astrocyte-neuron network is presented in this paper. It uses the output spike frequency of neurons to control the motor speed and robot activation. A software model of the astrocyte-neuron network previously demonstrated self-detection of faults and its self-repairing capability. In this paper the application demonstrator of mobile robotics is employed to evaluate the fault-tolerant capabilities of the astrocyte-neuron network when implemented in a hardware-based robotic car system. Results demonstrated that when 20% or less synapses associated with a neuron are faulty, the robot car can maintain system performance and complete the task of forward motion correctly. If 80% synapses are faulty, the system performance shows a marginal degradation, however this degradation is much smaller than that of conventional fault-tolerant techniques under the same levels of faults. This is the first time that astrocyte cells merged within spiking neurons demonstrates a self-repairing capabilities in the hardware system for a real application.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Astrocyte,Fault-tolerant,Repair,Robot car,Self-adaptive,Spiking neural networks |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Electronic Engineering (York) |
Funding Information: | Funder Grant number EPSRC EP/N007050/1 |
Depositing User: | Pure (York) |
Date Deposited: | 15 Jan 2018 16:40 |
Last Modified: | 16 Oct 2024 10:51 |
Published Version: | https://doi.org/10.1109/IJCNN.2016.7727359 |
Status: | Published |
Publisher: | IEEE |
Series Name: | Proceedings of International Joint Conference on Neural Networks |
Identification Number: | 10.1109/IJCNN.2016.7727359 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:126291 |
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