Paper Info
Title
Sniffy Bug: A Fully Autonomous Swarm of Gas-Seeking Nano Quadcopters in Cluttered Environments
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 '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
Year
DOI
Venue
2021
10.1109/IROS51168.2021.9636217
2021 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
DocType
ISSN
Citations 
Conference
2153-0858
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Bardienus P. Duisterhof100.34
Shushuai Li201.01
Javier Burgués300.68
Vijay Janapa Reddi42931140.26
Guido de Croon5266.66