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Aerial Survey Robotics in Extreme Environments: Mapping Volcanic CO2 Emissions With Flocking UAVs

We present methods for autonomous collaborative surveying of volcanic CO 2 emissions using aerial robots. CO 2 is a useful predictor of volcanic eruptions and an influential greenhouse gas. However, current CO 2 mapping methods are hazardous and inefficient, as a result, only a small fraction of CO...

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Bibliographic Details
Published in:Frontiers in control engineering 2022-03, Vol.3
Main Authors: Ericksen, John, Fricke, G. Matthew, Nowicki, Scott, Fischer, Tobias P., Hayes, Julie C., Rosenberger, Karissa, Wolf, Samantha R., Fierro, Rafael, Moses, Melanie E.
Format: Article
Language:English
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Summary:We present methods for autonomous collaborative surveying of volcanic CO 2 emissions using aerial robots. CO 2 is a useful predictor of volcanic eruptions and an influential greenhouse gas. However, current CO 2 mapping methods are hazardous and inefficient, as a result, only a small fraction of CO 2 emitting volcanoes have been surveyed. We develop algorithms and a platform to measure volcanic CO 2 emissions. The Dragonfly Unpiloted Aerial Vehicle (UAV) platform is capable of long-duration CO 2 collection flights in harsh environments. We implement two survey algorithms on teams of Dragonfly robots and demonstrate that they effectively map gas emissions and locate the highest gas concentrations. Our experiments culminate in a successful field test of collaborative rasterization and gradient descent algorithms in a challenging real-world environment at the edge of the Valles Caldera supervolcano. Both algorithms treat multiple flocking UAVs as a distributed flexible instrument. Simultaneous sensing in multiple UAVs gives scientists greater confidence in estimates of gas concentrations and the locations of sources of those emissions. These methods are also applicable to a range of other airborne concentration mapping tasks, such as pipeline leak detection and contaminant localization.
ISSN:2673-6268
2673-6268
DOI:10.3389/fcteg.2022.836720