  The great promises of neuromorphic sensing and processing for robotics have
led researchers and engineers to investigate novel models for robust and
reliable control of autonomous robots (navigation, obstacle detection and
avoidance, etc.), especially for quadrotors in challenging contexts such as
drone racing and aggressive maneuvers. Using spiking neural networks, these
models can be run on neuromorphic hardware to benefit from outstanding update
rates and high energy efficiency. Yet, low-level controllers are often
neglected and remain outside of the neuromorphic loop. Designing low-level
neuromorphic controllers is crucial to remove the standard PID, and therefore
benefit from all the advantages of closing the neuromorphic loop. In this
paper, we propose a parsimonious and adjustable neuromorphic PID controller,
endowed with a minimal number of 93 neurons sparsely connected to achieve
autonomous, onboard altitude control of a quadrotor equipped with Intel's Loihi
neuromorphic chip. We successfully demonstrate the robustness of our proposed
network in a set of experiments where the quadrotor is requested to reach a
target altitude from take-off. Our results confirm the suitability of such
low-level neuromorphic controllers, ultimately with a very high update
frequency.
