Autonomous greenhouse gas sampling using multiple robotic boats


Autoria(s): Dunbabin, Matthew
Contribuinte(s)

Wettergreen, David S.

Barfoot, Timothy D.

Data(s)

2016

Resumo

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.

Identificador

http://eprints.qut.edu.au/94410/

Publicador

Springer International Publishing

Relação

DOI:10.1007/978-3-319-27702-8_2

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.

Direitos

Copyright 2016 Springer International Publishing Switzerland

Fonte

ARC Centre of Excellence for Robotic Vision; School of Electrical Engineering & Computer Science; Institute for Future Environments; Science & Engineering Faculty

Palavras-Chave #080101 Adaptive Agents and Intelligent Robotics #Autonomous Surface Vehicles #Greenhouse gas #Multi-robot
Tipo

Conference Paper