Advertisement

Sensing Water Properties at Precise Depths from the Air

  • John-Paul OreEmail author
  • Carrick Detweiler
Conference paper
Part of the Springer Proceedings in Advanced Robotics book series (SPAR, volume 5)

Abstract

Water properties critical to our understanding and managing of freshwater systems change rapidly with depth. This work presents an Unmanned Aerial Vehicle (UAV) based method of keeping a passive, cable-suspended sensor payload at a precise depth, with \(95\%\) of submerged sensor readings within \(\pm 8.4\,\text {cm}\) of the target depth, helping dramatically increase the spatiotemporal resolution of water science datasets. We use a submerged depth altimeter attached at the terminus of a \(3.5\,\text {m}\) semi-rigid cable as the sole input to a depth controller actuated by the UAV’s motors. First, we simulate the system and common environmental disturbances of wind, water, and GPS drift and then use parameters discovered during simulation to guide implementation. In field experiments, we compare the depth precision of our new method to previous methods that used the UAV’s altitude as a proxy for submerged sensor depth, specifically: (1) only using the UAV’s air-pressure altimeter; and (2) fusing UAV-mounted ultrasonic sensors with the air-pressure altimeter. Our new method reduces the standard deviation of depth readings by \(75\%\) in winds up to \(8\,\text {m/s}\). We show the step response of the depth-altimeter method when transitioning between target depths and show that it meets the precision requirements. Finally, we explore a longer, \(8.0\,\text {m}\) cable and show that our depth-altimeter method still outperforms previous methods and allows scientists to increase the spatiotemporal resolution of water property datasets.

Keywords

Water Properties Unmanned Aerial Vehicles (UAV) Sensor Payload Target Depth Depth Readings 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

Thanks to Dr. Sebastian Elbaum and Dr. Justin Bradley for insightful discussions. Becca Horzewski helped design and build the underwater sensor. For help with field experiments we thank Ajay Shankar, Ashraful Islam, Adam Plowcha, Chandima Fernando, and Nishant Sharma. This work partially supported by NSF NRI-1638099, USDA-NIFA 2013-67021-20947 and USDA-NIFA 2017-67021-25924.

References

  1. 1.
    Higgins, J., Detweiler, C.: The Waterbug sub-surface sampler: design, control and analysis. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 330–337 (2016)Google Scholar
  2. 2.
    Dodds, W.K., Bouska, W.W., Eitzmann, J.L., Pilger, T.J., Pitts, K.L., Riley, A.J., Schloesser, J.T., Thornbrugh, D.J.: Eutrophication of U.S. freshwaters: analysis of potential economic damages. Environ. Sci. Technol. 43(1), 12–19 (2009)CrossRefGoogle Scholar
  3. 3.
    Sanseverino, I., Conduto, D., Pozzoli, P., Dobricic, S., Lettieri, T.: Algal bloom and its economic impact. EUR 27905 EN 660478 (2016)Google Scholar
  4. 4.
    Fischer, H.B., List, J.E., Koh, C.R., Imberger, J., Brooks, N.H.: Mixing in Inland and Coastal Waters. Elsevier (2013)Google Scholar
  5. 5.
    Ore, J.P., Elbaum, S., Burgin, A., Zhao, B., Detweiler, C.: Autonomous aerial water sampling. In: Proceedings of the 9th International Conference on Field and Service Robots (FSR), Brisbane, Australia, vol. 5, pp. 137–151 (2013)Google Scholar
  6. 6.
    Schwarzbach, M., Laiacker, M., Mulero-Pazmany, M., Kondak, K.: Remote water sampling using flying robots. In: 2014 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 72–76 (2014)Google Scholar
  7. 7.
    Ore, J.P., Elbaum, S., Burgin, A., Detweiler, C.: Autonomous aerial water sampling. J. Field Robot. 32(8), 1095–1113 (2015)CrossRefGoogle Scholar
  8. 8.
    Rodrigues, P., Marques, F., Pinto, E., Pombeiro, R., Lourenço, A., Mendonça, R., Santana, P., Barata, J.: An open-source watertight unmanned aerial vehicle for water quality monitoring. In: OCEANS’15 MTS/IEEE Washington, pp. 1–6 (2015)Google Scholar
  9. 9.
    Bae, J.H., Matson, E.T., Min, B.-C.: Towards an autonomous water monitoring system with an unmanned aerial and surface vehicle team. In: 2015 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pp. 1–2 (2015)Google Scholar
  10. 10.
    Ribeiro, M., Ferreira, A.S., Gonçalves, P., Galante, J., de Sousa, J.B.: Quadcopter platforms for water sampling and sensor deployment. In: OCEANS 2016 MTS/IEEE Monterey, pp. 1–5 (2016)Google Scholar
  11. 11.
    Koparan, C., Koc, A.B.: Unmanned aerial vehicle (UAV) assisted water sampling. In: 2016 American Society of Agricultural and Biological Engineers ASABE Annual International Meeting, p. 1 (2016)Google Scholar
  12. 12.
    DeMario, A., Lopez, P., Plewka, E., Wix, R., Xia, H., Zamora, E., Gessler, D., Yalin, A.P.: Water plume temperature measurements by an unmanned aerial system (UAS). Sensors 17(2), 306 (2017)CrossRefGoogle Scholar
  13. 13.
    Chung, M., Detweiler, C., Hamilton, M., Higgins, J., Ore, J.P., Thompson, S.: Obtaining the thermal structure of lakes from the air. Water 7(11), 6467–6482 (2015)CrossRefGoogle Scholar
  14. 14.
    Eubank, R., Atkins, E., Macy, D.: Autonomous guidance and control of the Flying Fish ocean surveillance platform. In: AIAA Infotech@ Aerospace Conference, pp. 2009–2021 (2009)Google Scholar
  15. 15.
    Bershadsky, D., Haviland, S., Valdez, P.E., Johnson, E.: Design considerations of submersible unmanned flying vehicle for communications and underwater sampling. In: OCEANS 2016 MTS/IEEE Monterey, pp. 1–8 (2016)Google Scholar
  16. 16.
    Dunbabin, M.: Autonomous greenhouse gas sampling using multiple robotic boats. In: Field and Service Robotics, pp. 17–30. Springer (2016)Google Scholar
  17. 17.
    Cruz, N.A., Matos, A.C.: The MARES AUV, a modular autonomous robot for environment sampling. In: OCEANS 2008, pp. 1–6 (2008)Google Scholar
  18. 18.
    Zhang, F., En-Nasr, O., Litchman, E., Tan, X.: Autonomous sampling of water columns using gliding robotic fish: control algorithms and field experiments. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 517–522 (2015)Google Scholar
  19. 19.
    Jain, S., Nuske, S., Chambers, A., Yoder, L., Cover, H., Chamberlain, L., Scherer, S., Singh, S.: Autonomous river exploration. In: Field and Service Robotics, pp. 93–106. Springer (2015)Google Scholar
  20. 20.
    Burri, M., Oleynikova, H., Achtelik, M.W., Siegwart, R.: Real-time visual-inertial mapping, re-localization and planning onboard MAVs in unknown environments. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1872–1878 (2015)Google Scholar
  21. 21.
    Tang, S., Kumar, V.: Mixed integer quadratic program trajectory generation for a quadrotor with a cable-suspended payload. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 2216–2222 (2015)Google Scholar
  22. 22.
    Goodarzi, F., Lee, D., Lee, T., et al.: Geometric stabilization of a quadrotor UAV with a payload connected by flexible cable. In: 2014 American Control Conference (ACC), pp. 4925–4930 (2014)Google Scholar
  23. 23.
    Flying Qualities of Piloted Airplanes, U.S. Military, 11 1980Google Scholar
  24. 24.
    Pressure Sensor MS5803-01BA. http://www.te.com/usa-en/product-CAT-BLPS0038.html. Accessed 21 March 2017
  25. 25.
    Jiang, H., Elbaum, S., Detweiler, C.: Inferring and monitoring invariants in robotic systems. Auton. Robots 41(4), 1027–1046 (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Computer Science and EngineeringUniversity of Nebraska-LincolnLincolnUSA

Personalised recommendations