Abstract
This work deals with the problem of detecting and localizing methane emission sources in open spaces with a mobile robot equipped with a remote gas detector (TDLAS). To reduce the long inspection time of traditional approaches which use the ground as the natural reflector, in this work, we analyze the feasibility of a leader-follower formation, where one robot, the leader, carries the remote gas detector that scans horizontally, parallel to the ground, and a second robot, the follower, that acts as an artificial reflector. We present a visual tracking mechanism for the relative pose estimation of both mobile platforms to extend the measurement range up to 10 m. Results in a 70 \(m^2\) experimental area demonstrate that this approach is effective for a fast location of methane gas sources.
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References
Arain, M.A., Hernandez Bennetts, V., Schaffernicht, E., Lilienthal, A.J.: Sniffing out fugitive methane emissions: autonomous remote gas inspection with a mobile robot. Int. J. Robot. Res. 40(4–5), 782–814 (2021). https://doi.org/10.1177/0278364920954907
Bennetts, V.H., Schaffernicht, E., Stoyanov, T., Lilienthal, A.J. and Trincavelli, M.: Robot assisted gas tomography - localizing methane leaks in outdoor environments. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 6362–6367 (2014). https://doi.org/10.1109/ICRA.2014.6907798
Byer, R.L., Shepp, L.A.: Two-dimensional remote air-pollution monitoring via tomography. Optics Lett. 4(3), 75–77 (1979). https://doi.org/10.1364/OL.4.000075
Chen, X., Vizzo, I., Läbe, T., Behley, J., Stachniss, C.: Range image-based lidar localization for autonomous vehicles. In: IEEE Int. Conf. Robot. Autom. (ICRA)., 5802–5808 (2021.) https://doi.org/10.1109/ICRA48506.2021.9561335
Corbett, A., Smith, B.: A study of a miniature TDLAS system onboard two unmanned aircraft to independently quantify methane emissions from oil and gas production assets and other industrial emitters. Atmosphere 13(5), 804 (2022). https://doi.org/10.3390/atmos13050804
Feng, S., et al.: Review on smart gas sensing technology. Sensors (Switzerland) 19(17), 1–22 (2019). https://doi.org/10.3390/s19173760
Gålfalk, M., Nilsson Påledal, S., Bastviken, D.: Sensitive drone mapping of methane emissions without the need for supplementary ground-based measurements. ACS Earth Space Chem. 5(10), 2668–2676 (2021). https://doi.org/10.1021/acsearthspacechem.1c00106
Gongora, A., Monroy, J., Gonzalez-Jimenez, J.: Joint estimation of gas & wind maps for fast-response applications. Appl. Math. Model. (2020). https://doi.org/10.1016/j.apm.2020.06.026
Jiang, J., et al.: TDLAS-based detection of dissolved methane in power transformer oil and field application. IEEE Sens. J. 18(6), 2318–2325 (2018). https://doi.org/10.1109/JSEN.2017.2788871
Kalaitzakis, M., Cain, B., Carroll, S., Ambrosi, A., Whitehead, C., Vitzilaios, N.: Fiducial markers for pose estimation: overview, applications and experimental comparison of the ARtag, AprilTag, ArUco and STag markers. J. Intell. Robot. Syst. 101, 1–26 (2021). https://doi.org/10.1007/s10846-020-01307-9
Li, F., Cai, H., Jiheng, X., Zhang, K., Feng, Q., Wang, H.: Gas distribution mapping for indoor environments based on laser absorption spectroscopy: development of an improved tomographic algorithm. Build. Environ. 172, 106724 (2020). https://doi.org/10.1016/j.buildenv.2020.106724
Madhavan, R., Fregene, K., Parker, L.E.: Distributed heterogeneous outdoor multi-robot localization. In: IEEE Int. Conf. Robot. Autom. (ICRA), vol. 1, pp. 374–381 (2002). https://doi.org/10.1109/ROBOT.2002.1013389
Monroy, J., Blanco, J.-L., Gonzalez-Jimenez, J.: Time-variant gas distribution mapping with obstacle information. Auton. Robot. 40(1), 1–16 (2016). https://doi.org/10.1007/s10514-015-9437-0
Mønster, J., Kjeldsen, P., Scheutz, C.: Methodologies for measuring fugitive methane emissions from landfills - a review. Waste Manage. 87, 835–859 (2019). https://doi.org/10.1016/j.wasman.2018.12.047
Neumann, P.P., Kohlhoff, H., Hüllmann, D., Lilienthal, A.J., Kluge, M.: Bringing mobile robot olfaction to the next dimension - UAV-based remote sensing of gas clouds and source localization. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3910–3916 (2017). https://doi.org/10.1109/ICRA.2017.7989450
Themelis, N.J., Ulloa, P.A.: Methane generation in landfills. Renewable Energy 32(7), 1243–1257 (2007). https://doi.org/10.1016/j.renene.2006.04.020
Weiss, C., Tamimi, H., Masselli, A., Zell, A.: A hybrid approach for vision-based outdoor robot localization using global and local image features. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1047–1052 (2007). https://doi.org/10.1109/IROS.2007.4398959
Acknowledgements
This work was funded by the research projects HOUNDBOT (P20_01302) from Andalusia Regional Government, and (UMA20-FEDERJA-056), both funding by the European Regional Development FundERDF and by the grant for the formation of pre-doctoral researchers in Andalusia (24653).
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Monroy, J., Ojeda, P., Gonzalez-Jimenez, J. (2024). A Feasibility Study of a Leader-Follower Multi-robot Formation for TDLAS Assisted Methane Detection in Open Spaces. In: Marques, L., Santos, C., Lima, J.L., Tardioli, D., Ferre, M. (eds) Robot 2023: Sixth Iberian Robotics Conference. ROBOT 2023. Lecture Notes in Networks and Systems, vol 976. Springer, Cham. https://doi.org/10.1007/978-3-031-58676-7_15
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