Abstract
A self-repairing robot utilising a spiking astrocyteneuronnetwork is presented in this paper. It uses the outputspike frequency of neurons to control the motor speed and robotactivation. A software model of the astrocyte-neuron networkpreviously demonstrated self-detection of faults and its selfrepairingcapability. In this paper the application demonstratorof mobile robotics is employed to evaluate the fault-tolerant capabilitiesof the astrocyte-neuron network when implemented ina hardware-based robotic car system. Results demonstrated thatwhen 20% or less synapses associated with a neuron are faulty,the robot car can maintain system performance and complete thetask of forward motion correctly. If 80% synapses are faulty, thesystem performance shows a marginal degradation, however thisdegradation is much smaller than that of conventional faulttoleranttechniques under the same levels of faults. This is thefirst time that astrocyte cells merged within spiking neuronsdemonstrates a self-repairing capabilities in the hardware systemfor a real application
| Original language | English |
|---|---|
| Title of host publication | Unknown Host Publication |
| Publisher | IEEE |
| Number of pages | 8 |
| Publication status | Accepted - 15 Mar 2016 |
| Event | International Joint Conference on Neural Networks (IJCNN) - Canda Duration: 15 Mar 2016 → … |
Conference
| Conference | International Joint Conference on Neural Networks (IJCNN) |
|---|---|
| Period | 15/03/16 → … |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 9 Industry, Innovation, and Infrastructure
Keywords
- self-repair
- adapt
- astrocyte
- glia
- robotic
- FPGAs
- hardware
- error
- fault
- detection
- adaptive
Fingerprint
Dive into the research topics of 'Self-Repairing Mobile Robotic Car using Astrocyte Neuron Networks'. Together they form a unique fingerprint.Student theses
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Self-correcting strategy for networks-on-chip interconnect
Liu, J. (Author), Harkin, J. (Supervisor), Li, Y. (Supervisor) & Maguire, L. (Supervisor), Sept 2015Student thesis: Doctoral Thesis
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