Self-repairing mobile robotic car using astrocyte-neuron networks

TY - GEN

T1 - Self-repairing mobile robotic car using astrocyte-neuron networks

AU - Liu, Junxiu

AU - Harkin, Jim

AU - McDaid, Liam

AU - Halliday, David M.

AU - Tyrrell, Andy M.

AU - Timmis, Jon

N1 - (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.

PY - 2016/10/31

Y1 - 2016/10/31

N2 - 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.

AB - 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.

KW - Astrocyte

KW - Fault-tolerant

KW - Repair

KW - Robot car

KW - Self-adaptive

KW - Spiking neural networks

UR - http://www.scopus.com/inward/record.url?scp=85007154321&partnerID=8YFLogxK

U2 - 10.1109/IJCNN.2016.7727359

DO - 10.1109/IJCNN.2016.7727359

M3 - Conference contribution

AN - SCOPUS:85007154321

VL - 2016-October

T3 - Proceedings of International Joint Conference on Neural Networks

SP - 1379

EP - 1386

BT - 2016 International Joint Conference on Neural Networks, IJCNN 2016

PB - IEEE

T2 - 2016 International Joint Conference on Neural Networks, IJCNN 2016

Y2 - 24 July 2016 through 29 July 2016

ER -