Self-repairing mobile robotic car using astrocyte-neuron networks

Junxiu Liu, Jim Harkin, Liam McDaid, David M. Halliday, Andy M. Tyrrell, Jon Timmis

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

23 Citations (SciVal)


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.

Original languageEnglish
Title of host publication2016 International Joint Conference on Neural Networks, IJCNN 2016
PublisherIEEE Press
Number of pages8
ISBN (Electronic)9781509006199
Publication statusPublished - 31 Oct 2016
Event2016 International Joint Conference on Neural Networks, IJCNN 2016 - Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016

Publication series

NameProceedings of the International Joint Conference on Neural Networks


Conference2016 International Joint Conference on Neural Networks, IJCNN 2016
Period24 Jul 201629 Jul 2016


  • Astrocyte
  • Fault-tolerant
  • Repair
  • Robot car
  • Self-adaptive
  • Spiking neural networks


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