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PDF 381 KB
Pages 431-438
First published 30 July 2014

Evolving Spiking Networks for Turbulence-Tolerant Quadrotor Control

David Howard and Alberto Elfes

Abstract (Excerpt)

We investigate the automatic development of robust quadrotor neurocontrollers based on spiking neural networks. A self-adaptive evolutionary algorithm is used to generate high-utility topology/weight combinations in the networks, and a simple synaptic plasticity mechanism provides some degree of in-trial adaptation. Incremental evolution gradually increases the severity of environmental conditions that the agent can successfully handle. Results compare the spiking networks to tuned Proportional/Integral/Derivative controllers and feedforward neural networks for waypoint-holding experiments in varied atmospheric conditions. It is shown that the spiking controllers are able to maintain a closer distance to the waypoint than the comparative controllers, and more effectively deal with more challenging environmental conditions.