TY - GEN
T1 - Sniffy Bug
T2 - 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
AU - Duisterhof, Bardienus P.
AU - Li, Shushuai
AU - Burgues, Javier
AU - Reddi, Vijay Janapa
AU - de Croon, Guido C.H.E.
PY - 2021
Y1 - 2021
N2 - 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 ‘Sniffy Bug', 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, ‘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. Videos: https://bit.ly/37MmtdL
AB - 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 ‘Sniffy Bug', 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, ‘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. Videos: https://bit.ly/37MmtdL
UR - http://www.scopus.com/inward/record.url?scp=85124111718&partnerID=8YFLogxK
U2 - 10.1109/IROS51168.2021.9636217
DO - 10.1109/IROS51168.2021.9636217
M3 - Conference contribution
SN - 978-1-6654-1715-0
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 9099
EP - 9106
BT - IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
PB - IEEE
Y2 - 27 September 2021 through 1 October 2021
ER -