Reconfigurable analogue hardware evolution of adaptive spiking neural network controllers
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Investigating the Suitability of FPAAs for Evolved Hardware Spiking Neural Networks
ICES '08 Proceedings of the 8th international conference on Evolvable Systems: From Biology to Hardware
Genetic Programming and Evolvable Machines
Reconfigurable hardware evolution platform for a spiking neural network robotics controller
ARC'07 Proceedings of the 3rd international conference on Reconfigurable computing: architectures, tools and applications
Fast damage recovery in robotics with the T-resilience algorithm
International Journal of Robotics Research
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We present a method for evolving and implementing artificial neural networks (ANNs) on Field Programmable Analog Arrays (FPAAs). These FPAAs offer the small size and low power usage desirable for space applications. We use two cascaded FPAAs to create a two layer ANN. Then, starting from a population of random settings for the network, we are able to evolve an effective controller for several different robot morphologies. We demonstrate the effectiveness of our method by evolving two types of ANN controllers: one for biped locomotion and one for restoration of mobility to a damaged quadruped. Both robots exhibit non-linear properties, making them difficult to control. All candidate controllers are evaluated in hardware; no simulation is used.