Print Email Facebook Twitter Sniffy Bug: A fully autonomous and collaborative swarm of gas-seeking nano quadcopters in cluttered environments Title Sniffy Bug: A fully autonomous and collaborative swarm of gas-seeking nano quadcopters in cluttered environments Author Duisterhof, Bart (TU Delft Aerospace Engineering) Contributor de Croon, G.C.H.E. (mentor) Verhoeven, C.J.M. (graduation committee) de Wagter, C. (graduation committee) Degree granting institution Delft University of Technology Programme Aerospace Engineering Date 2020-12-15 Abstract Nano quadcopters are ideal for gas source localization (GSL) as they are cheap, safe and agile. However, previous algorithms are unsuitable for nano quadcopters, as they rely on heavy sensors, require too large computational resources, or only solve simple scenarios without obstacles. In this work, we propose a novel bug algorithm named `Sniffy Bug', that allows a swarm of gas-seeking nano quadcopters to localize a gas source in an unknown, cluttered and GPS-denied environment. Sniffy Bug is capable of efficient GSL with extremely little sensory input and computational resources, operating within the strict resource constraints of a nano quadcopter. The algorithm foresees in the avoidance of obstacles and other swarm members, while pursuing desired waypoints. The waypoints are 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, with a novel automated end-to-end simulation and benchmark platform, AutoGDM. This platform enables fully automated end-to-end environment generation and gas dispersion modelling (GDM), not only allowing for learning in simulation but also providing the first GSL benchmark. We show that evolved Sniffy Bug outperforms manually selected parameters in challenging, cluttered environments in the real world. To this end, we show that a lightweight and mapless bug algorithm can be evolved to complete a complex task, and enable the first fully autonomous swarm of collaborative gas-seeking nano quadcopters. To reference this document use: http://resolver.tudelft.nl/uuid:7dd7edc9-0037-4c3e-a667-aac7476f272f Embargo date 2021-05-01 Bibliographical note Best Graduate Faculteit L&R 2021 Part of collection Student theses Document type master thesis Rights © 2020 Bart Duisterhof Files PDF report_compressed.pdf 7.81 MB Close viewer /islandora/object/uuid:7dd7edc9-0037-4c3e-a667-aac7476f272f/datastream/OBJ/view