Evolving Spiking Neurocontrollers for UAVs

Spiking neural networks (SNNs) are neuroscience-inspired computational systems that carry out computation based on the biological modeling of neuron interactions. Current SNN studies have shown their ability to solve a wide variety of machine learning problems. The temporal dynamics and future low-power neuromorphic implementations of SNNs also make them suitable controller candidates for embedded applications, especially for robotic platforms with very low payload and power budgets (e.g. Micro Air Vehicles). In this paper, we present a solution to simulate full control of a hexacopter UAV in 6 degrees of freedom using SNNs. By decomposing the neurocontroller into modules, we demonstrate that the development of UAV Bight control can be accomplished by an incremental evolutionary approach using a modified NEAT algorithm.