@conference {1606275,
	title = {Sniffy Bug: A Fully Autonomous Swarm of Gas-Seeking Nano Quadcopters in Cluttered Environments},
	booktitle = {International Conference on Intelligent Robots and Systems},
	year = {2021},
	address = {IROS 2021, Prague, Czech Republic (Virtual)},
	abstract = {
	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. We propose a novel bug algorithm named {\textquoteleft}Sniffy Bug{\textquoteright}, which allows a fully autonomous swarm of gas-seeking nano quadcopters to localize a gas source in 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 (PSO) procedure. We evolve all the parameters of the bug (and PSO) algorithm using our novel simulation pipeline, {\textquoteleft}AutoGDM{\textquoteright}. 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. Videos: https://bit.ly/37MmtdL

},
	url = {https://arxiv.org/abs/2107.05490},
	author = {Bardienus P. Duisterhof and Shushuai Li and Javier Burgu{\'e}s and Reddi, Vijay Janapa and Guido C. H. E. de Croon}
}
