Paper Info
Title
Sensor-Fusion In Spiking Neural Network That Generates Autonomous Behavior In Real Mobile Robot
Abstract
We here introduce a novel adaptive controller for autonomous mobile robot that binds N types of sensory information. For each sensory modality, sensory-motor connection is made by a three-layered spiking neural network (SNN). The synaptic weights in the model have the property of spike timing-dependent plasticity (STDP) and regulated by presynaptic modulation signal from the sensory neurons. Each synaptic weight is incrementally adapted depending upon the firing rate of the presynaptic modulation signal and that of the hidden-layer neuron(s). Information from different types of sensors are bound at the motor neurons. A real mobile robot Khepera with the SNN controller quickly adapted into an open environment and performed the desired task successfully. This approach could be applicable to a robot with inputs of various sensory modalities and various types of motor outputs.
Year
DOI
Venue
2008
10.1109/IJCNN.2008.4634102
2008 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-8
Keywords
Field
DocType
computer networks,mobile robots,spiking neural network,spike timing dependent plasticity,tin,mobile robot,robots,sensor fusion,neural networks,sensors,adaptive control,modulation
Computer science,Sensor fusion,Artificial intelligence,Spike-timing-dependent plasticity,Artificial neural network,Sensory system,Spiking neural network,Stimulus modality,Synaptic weight,Mobile robot,Machine learning
Conference
ISSN
Citations 
PageRank 
2161-4393
8
0.71
References 
Authors
3
2
Name
Order
Citations
PageRank
Fady Alnajjar16612.23
Kazuyuki Murase2103875.66