Evolving Spiking Networks for Turbulence-Tolerant Quadrotor Control
CSIRO, Autonomous Systems Program, 1 Technology Court Pullenvale, QLD 4069 Australia, David Howard, Alberto Elfes · 2014
We investigate the automatic development of robust quadrotor neurocontrollers based on spiking neural networks. A self-adaptive evolutionary algorithm is used to generate highutility 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 waypointholding 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.