  Nano quadcopters are ideal for gas source localization (GSL) as they are
safe, agile and inexpensive. However, their extremely restricted sensors and
computational resources make GSL a daunting challenge. In this work, we propose
a novel bug algorithm named `Sniffy Bug', which allows a fully autonomous swarm
of gas-seeking nano quadcopters to localize a gas source in an unknown,
cluttered and GPS-denied environments. The computationally efficient, mapless
algorithm foresees in the avoidance of obstacles and other swarm members, while
pursuing desired waypoints. The waypoints are first set for exploration, and,
when a single swarm member has sensed the gas, by a particle swarm
optimization-based procedure. We evolve all the parameters of the bug (and PSO)
algorithm, using our novel simulation pipeline, `AutoGDM'. It builds on and
expands open source tools in order to enable fully automated end-to-end
environment generation and gas dispersion modeling, allowing for learning in
simulation. Flight tests show that Sniffy Bug with evolved parameters
outperforms manually selected parameters in cluttered, real-world environments.
