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

Scott Nowicki,Karissa Rosenberger,Rafael Fierro,Melanie E Moses,John Ericksen,Julie C Hayes,Tobias P Fischer,G Matthew Fricke,Samantha R Wolf

doi:10.3389/fcteg.2022.836720

Scott Nowicki, Karissa Rosenberger + Show 7 more

Open Access

https://doi.org/10.3389/fcteg.2022.836720

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Abstract

We present methods for autonomous collaborative surveying of volcanic CO2 emissions using aerial robots. CO2 is a useful predictor of volcanic eruptions and an influential greenhouse gas. However, current CO2 mapping methods are hazardous and inefficient, as a result, only a small fraction of CO2 emitting volcanoes have been surveyed. We develop algorithms and a platform to measure volcanic CO2 emissions. The Dragonfly Unpiloted Aerial Vehicle (UAV) platform is capable of long-duration CO2 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.

Highlights

Distributed mobile sensing has many application areas, such as monitoring of industrial gas leaks, hazardous material releases, and agricultural monitoring (Rossi et al, 2014; Gomez and Purdie, 2016; Radoglou-Grammatikis et al, 2020).1 Often the materials we are interested in sensing can only be directly sampled, as the signal of CO2 emissions relative to background is low
This provided us with a likely location for CO2 emissions; CO2 emissions change frequently, and measurements are affected by wind and temperature
The field study at Valles Caldera volcano demonstrated that the swarm could produce rasterized surveys and flock to a suspected source of CO2 under difficult field conditions but where there is uncertainty in true sources and concentrations of CO2

Summary

Introduction

Distributed mobile sensing has many application areas, such as monitoring of industrial gas leaks, hazardous material releases, and agricultural monitoring (Rossi et al, 2014; Gomez and Purdie, 2016; Radoglou-Grammatikis et al, 2020). Often the materials we are interested in sensing can only be directly sampled, as the signal of CO2 emissions relative to background is low. Often the materials we are interested in sensing can only be directly sampled, as the signal of CO2 emissions relative to background is low. Remote sensing methods such as satellite imaging are capable of measuring total column integrated CO2 on a global scale, but specific eruptions and volcanic plumes must be spatially and temporally targeted in order to capture events (Johnson et al, 2020). Relatively small instruments exist that can make very accurate point-measurements of CO2. This requires that the measurement instrument be moved through the area of interest. In addition to the risk involved, are biased by surveyors’ inability to survey areas of unstable rock, sheer cliffs, scalding mud-pots, or (without specialized breathing equipment) areas with poisonous gas

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