The concept of automated environmental monitoring intellectual system based on compact autonomous robots

 Y.E. Shishkin1,2, A.N. Grekov1

 1 Institute of Natural and Technical Systems, Russian Federation, Sevastopol, Lenin St., 28

2 Federal State Educational Institution of Higher Education «Sevastopol State University», Russian Federation, Sevastopol, Universitetskaya St., 33

Email: yourockpro@gmail.com

DOI: 10.33075/2220-5861-2018-4-63-69

UDC 004.9:004.41

Abstract:

     This article proposes an intelligent system concept for automatic monitoring of the aquatic environment main parameters, in order to detect their anomalies and assess quantitative and qualitative indicators, including the determination of spatial and temporal characteristics of the field under study. The system is built on the basis of a network of small autonomous surface robots. A conceptual model of the monitoring system for implementation of automated integrated monitoring of the environment parameters throughout the entire observation field is proposed.

     A software model has been developed and simulation experiments have been conducted to calculate the main indicators and assess environment spatial and temporal variability. According to the results of the simulation, control maps of the stations optimal density were formed. The proposed approach to solving the problem of monitoring the aquatic environment in comparison with the traditional has such advantages as scalability, flexibility, speed of deployment and clotting, self-organization, the ability to create a wide field of view by changing the number of robots.

     The work of the algorithm for obtaining maps of optimal intervals on the stations grid where measurements of the aquatic environment parameters are made has been experimentally tested using the example of constructing a dissolved oxygen heat map in the surface layer of the bay of Sevastopol. This can serve as confirmation of its validity as information support for an intelligent decision-making support system in the organization of the optimal station location process.

     The proposed model was developed for the use in decision support systems for continuous automated control of the monitoring process by autonomous surface robots without operator participation, solving the problem of minimizing the time of taking field parameters and increasing autonomy by building an energy efficient route. The use of systematic data collection will provide fundamentally new scientific results at minimum cost.

Keywords: monitoring, detection of anomalies, environmental monitoring system, mathematical modeling, Big Data, cloud computing, clustering, critical systems, data mining.

Full text in PDF (RUS)

LIST OF REFERENCES

  1. Venkatesan R. Observing the Oceans in Real Time. Springer International Publishing, 2018.
  2. Marc L.M. Instrumentation and Metrology in Oceanography, ISBN: 978-1-848-21379-1, Sep 2012, Wiley-ISTE, 393 p.
  3. Bellingham J.B. New oceanographic uses of autonomous underwater vehicles // Mar. Technol. Soc. J., 1997. Vol. 31, no. 3. P. 34–47.
  4. Curtin T.B., Bellingham J.G., Catipovic J. Autonomous oceanographic sampling networks // Oceanography. 1993. Vol. 6 (3). P. 86–94.
  5. Minaev D. D. Principles of building a regional automated information system for environmental monitoring of marine areas using Autonomous technical means and robotic systems // Underwater research and robotics. 2011. No. 2 (12). P. 64-68.
  6. Emelyanova V. P., Lobchenko E. E. RD 52.24.643-2002. Method for complex assessment of surface water pollution by hydrochemical indicators. Depon. M., 2004. P. 20.
  7. Mezentsev I. V., Malchenko J. A. An Integrated approach to monitoring marine water pollution in the coastal waters of Sevastopol // Proceedings of the state Oceanographic Institute. 2015. No. 216. P. 326-339.
  8. Trusevich V. V., Gaisky P. V., Kuzmin K. A. Automated biomonitoring of the aquatic environment using bivalve mollusk reactions // Marine hydrophysical journal. 2010. № 3. P. 75–83.
  9. Gaisky P. V., Trusevich V. V., Zaburdaev V. I. Automatic bioelectronic complex designed for early detection of toxic pollutants in fresh and marine waters // Marine hydrophysical journal. 2014. no. 2. P. 44-53.
  10. Benjamin M.R., Schmidt H., Newman P.M. Nested autonomy for unmanned marine vehicles with MOOS-IvP // J. Field Robot. 2010. 27 (6). P. 834–875. DOI: 10.1002/rob.20370
  11. Schmid, H., Benjamin M.R., Petillo S.M. Nested autonomy for distributed ocean sensing // Springer Handbook of Ocean Engineering / eds. M.R. Dhanak, N.I. Xiros. New York: Springer, 2016. P. 459–480.
  12. Mahmoudian N., Woolsey C. Underwater glider motion control, in 2008 IEEE Conference on Decision and Control, 552–557. doi: 10.1109/CDC.2008.4739432
  13. Leonard J.J., Bahr A. Autonomous underwater vehicle navigation // Springer Handbook of Ocean Engineering. New York: Springer. 2016. P. 341–358.
  14. Kostenko V. V., Lvov O. Yu. Combined communication and navigation system of an Autonomous underwater robot with a float module // Underwater research and robotics. 2017. P.31–43.
  15. Heidemann J., Stojanovic M., Zorzi M. Underwater sensor networks: applications, advances and challenges // Phil. Trans. R. Soc. A. 2012. Т. 370, № 1958. P. 158–175.
  16. Lermusiaux P.F., Subramani D.N., Lin J. A future for intelligent autonomous ocean observing systems // Journal of Marine Research. 2017. 75 (6). P. 765–813.
  17. Atlas of Oceanographic characteristics of the Sevastopol Bay / S. K. Konovalov, A. S. Romanov, O. G. Moiseenko [et al.]. Sevastopol: “EKOSI-HYDROPHYSICS”, 2010. P. 320.
  18. Seabird SBE 43 and SBE 43F individually calibrated, high-accuracy oxygen sensor to assist in critical hypoxia and ocean stoichiometric oxygen chemistry research on a variety of profiling and moored platforms Datasheet (Available from http://seabird.com/oxygen-sensors/sbe-43-dissolved-oxygen-sensor).

If you have found a spelling error, please, notify us by selecting that text and pressing Ctrl+Enter.

Translate »

Spelling error report

The following text will be sent to our editors: