Autonomous greenhouse gas sampling using multiple robotic boats

Dunbabin, Matthew (2016) Autonomous greenhouse gas sampling using multiple robotic boats. In Wettergreen, David S. & Barfoot, Timothy D. (Eds.) Field and Service Robotics: Results of the 10th International Conference, Springer International Publishing, Toronto, Candada, pp. 17-30.

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Accurately quantifying total greenhouse gas emissions (e.g. methane) from natural systems such as lakes, reservoirs and wetlands requires the spatial-temporal measurement of both diffusive and ebullitive (bubbling) emissions. Traditional, manual, measurement techniques provide only limited localised assessment of methane flux, often introducing significant errors when extrapolated to the whole-of-system. In this paper, we directly address these current sampling limitations and present a novel multiple robotic boat system configured to measure the spatiotemporal release of methane to atmosphere across inland waterways. The system, consisting of multiple networked Autonomous Surface Vehicles (ASVs) and capable of persistent operation, enables scientists to remotely evaluate the performance of sampling and modelling algorithms for real-world process quantification over extended periods of time. This paper provides an overview of the multi-robot sampling system including the vehicle and gas sampling unit design. Experimental results are shown demonstrating the system’s ability to autonomously navigate and implement an exploratory sampling algorithm to measure methane emissions on two inland reservoirs.

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ID Code: 94410
Item Type: Conference Paper
Refereed: Yes
Keywords: Autonomous Surface Vehicles, Greenhouse gas, Multi-robot
DOI: 10.1007/978-3-319-27702-8_2
ISBN: 9783319277028
ISSN: 1610-742X
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Adaptive Agents and Intelligent Robotics (080101)
Divisions: Current > Research Centres > ARC Centre of Excellence for Robotic Vision
Current > Schools > School of Electrical Engineering & Computer Science
Current > Institutes > Institute for Future Environments
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2016 Springer International Publishing Switzerland
Deposited On: 05 Apr 2016 04:51
Last Modified: 17 Aug 2016 02:32

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