  Flying insects are capable of vision-based navigation in cluttered
environments, reliably avoiding obstacles through fast and agile maneuvers,
while being very efficient in the processing of visual stimuli. Meanwhile,
autonomous micro air vehicles still lag far behind their biological
counterparts, displaying inferior performance at a much higher energy
consumption. In light of this, we want to mimic flying insects in terms of
their processing capabilities, and consequently show the efficiency of this
approach in the real world. This letter does so through evolving spiking neural
networks for controlling landings of micro air vehicles using optical flow
divergence from a downward-looking camera. We demonstrate that the resulting
neuromorphic controllers transfer robustly from a highly abstracted simulation
to the real world, performing fast and safe landings while keeping network
spike rate minimal. Furthermore, we provide insight into the resources required
for successfully solving the problem of divergence-based landing, showing that
high-resolution control can be learned with only a single spiking neuron. To
the best of our knowledge, this work is the first to integrate spiking neural
networks in the control loop of a real-world flying robot. Videos of the
experiments can be found at https://bit.ly/neuro-controller .
