Skip to main content
Log in

A Dispatcher for a Multi-Agent Control System of an Autonomous Underwater Vehicle: Structure, Algorithms, and Simulation Results

  • Published:
Gyroscopy and Navigation Aims and scope Submit manuscript

Abstract

Algorithms for a dispatcher of a multi-agent control system for an autonomous underwater vehicle (AUV) are described. The algorithms are designed on a modular basis, which provides for the control of a wide range of tasks assigned to the AUV, and, in addition, makes the implementation of each algorithm simple.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.

Similar content being viewed by others

REFERENCES

  1. Ageyev, M.D. et al., Avtonomnye podvodnye roboty. Sistemy i tekhnologii (Autonomous Underwater Robots. Systems and Technologies, Moscow: Nauka, 2005.

  2. Inzartsev, A.V. et al., The use of an autonomous underwater vehicle for scientific research in the Arctic, Podvodnye issledovaniya i robototekhnika, 2007, no. 2 (4), pp. 5–14.

  3. Bozhenov,Yu.A., The use of autonomous underwater vehicles for the scientific research in the Arctic and Antarctic, Fundamental’naya i prikladnaya gidrofizika, 2011, vol. 4, no. 1, pp. 4–68.

  4. Millar, G. and Mackay, L., Maneuvering Under the Ice, Sea Technology, 2015, vol. 56, no. 4, pp. 35–38.

    Google Scholar 

  5. Gizitdinova, M.R. and Kuz’mitskii, M.A., Mobile underwater robots in modern oceanography and hydrophysics, Fundamental’naya i prikladnaya gidrofizika, 2010, vol. 3, no. 1, pp. 4–13.

  6. Illarionov, G.Yu.,Sidenko, K.S., and Bocharov, L.Yu., Ugrozaizglubiny: XXI vek (Threat from the Depth: XXI Century), Khabarovsk: KGUP, 2011.

  7. Belousov, I., Modern and prospective US Navy underwater vehicles, in: Zarubezhnoye voyennoe obozrenie, 2013, no. 5, pp. 79–88.

  8. Kuz’mitskii, M.A. and Gizitdinova, M.R., Mobile underwater robots in solving the Navy problems: Modern technologies and prospects, Fundamental’naya i prikladnaya gidrofizika, 2011, vol. 4, no. 3, pp. 37–48.

  9. Cebrowski, A.K. and Garstka, J.J., Network-centric warfare: its origins and future, U.S. Naval Institute Proceedings, 1998, no. 1.

  10. Baulin, V. and Kondrat’yev, A., Implementation of the network-centric warfare in the US Navy, Zarubezhnoe voennoe obozrenie, 2009, no. 6, pp. 61–67.

  11. Burenok, V.M., Organizational and scientific-and-technical basis of network-centric wars, Voennyi parad, 2010, no. 1, pp. 14–17.

  12. Butler, H., Daly, M., Doyle, A., Gillies, S., Hagen, S., and Schaub, T., The GeoJSON Format, RFC 7946, The Internet Engineering Task Force. URL: https://tools.ietf.org/html/rfc7946.

  13. Procedural Reasoning System User’s Guide. A Manual for Version 2.0, SRI International, 2001. URL: http://www.ai.sri.com/~prs/prs-manual.pdf.

  14. Boreiko, A.A., Inzartsev, A.V., Mashoshin, A.I., Pavin, A.M., and Pashkevich, I.V., A control system for a highly autonomous AUV based on a multi-agent approach, Podvodnye issledovaniya i robototekhnika, 2019, no. 2 (28), pp. 23–31.

  15. Pshikhopov, V.Kh. and Sirotenko, M.Yu., Structural and algorithmic implementation of a control system for an autonomous mobile robot with a neural network motion planner, Izvestiya TRTU. Intellektual’nye SAPR, 2004, no. 3 (38), pp. 185–190.

  16. Filaretov, V.F., Lebedev, A.V., and Yukhimets, D.A., Ustroistva I sistemy upravleniya podvodnykh robotov (Devices and control systems for underwater robots), Moscow: Nauka, 2005.

  17. Pshikhopov, V.Kh.,Sirotenko, M.Yu., and Gurenko, B.V., Structural organization of automatic control systems for underwater vehicles for a priori nonformalized media, Informatsionno-izmeritel’nye i upravlyayushchie sistemy. Intellektual’nye i adaptivnye roboty, 2006, vol. 4, no. 1–3, pp. 73–79.

  18. Kiselev, L.V., Inzartsev, A.V., and Matvienko, Yu.V., On some problems of dynamics and control of AUV spatial motion, Podvodnye issledovaniya i robototekhnika, 2006, no. 2, pp. 13–26.

  19. Inzartsev, A.V., L’vov, O.Yu., Sidorenko, A.V., and Khmel’kov, D.B., Architectural configurations of AUV control systems, Podvodnye issledovaniya i robototekhnika, 2006, no. 1, pp. 18–30.

  20. Pshikhopov, V.Kh., Pozitsionno-traektornoe upravlenie podvizhnymi ob’’yektami (Positional-Trajectory Control of Moving Objects), Taganrog: Izd-vo TRTI YuFU, 2009.

  21. Ermolov, I.L., Expanding the functionality of mobile technological robots by increasing the level of their autonomy with the use of hierarchical integrated processing of onboard data, Dr. Sci. Dissertation, Moscow, 2012.

  22. Mashoshin, A.I., Pashkevich, I.V., and Sokolov, A.I., Integrated control system of an autonomous underwater vehicle, Materialy 7-oi Rossiyskoi konferentsii po problemam upravleniya (Proc. 7th Russian Multi-conference on Control Problems), St. Petersburg, 2014, pp. 855–858.

  23. Martynova, L.A., Mashoshin, A.I., Pashkevich, I.V., and Sokolov, A.I., The control system is the most complicated part of autonomous underwater vehicles, Morskaya radioelektronika, 2015, no. 4 (54), pp. 23–32.

  24. Martynova, L.A., Mashoshin, A.I., Pashkevich, I.V., and Sokolov, A.I., Algorithms implemented by the AUV integrated control system, Izvestiya YuFU. Tekhnicheskie nauki, 2015, no.1, pp. 50–58.

  25. Rajan, K. et al. Remote agent: an autonomous control system for the new millennium, Proc. of Prestigious Applications of Intelligent Systems, European Conference on Artificial Intelligence(ECAI), Berlin, 2000.

  26. Innocenti, B., A multi-agent architecture with distributed coordination for an autonomous robot. Ph.D. dissertation, Universitat de Girona, 2009.

  27. Kim, T.W. and Yuh, J. Development of a real-time control architecture for a semiautonomous underwater vehicle for intervention missions, Autonomous Systems Laboratory, Department of Mechanical Engineering, University of Hawaii, 2003, pp. 1521–1530.

    Google Scholar 

  28. Sutarto, H. and Budiyono, A., Development of linear parameter varying control system for autonomous underwater vehicle, Indian J. Geo-Marine Sci., 2011, vol. 40, pp. 275–286.

    Google Scholar 

  29. Sarhadi, P., Noei, A.R., and Khosravi, A., Model reference adaptive autopilot with anti-windup compensator for an autonomous underwater vehicle: Design and hardware in the loop implementation results, Appl. Ocean Res., 2017, vol. 62, pp. 27–36.

    Article  Google Scholar 

  30. Geranmehr, B. and Nekoo, S.R., Nonlinear suboptimal control of fully coupled non-affine six-DOF autonomous underwater vehicle using the state-dependent Riccati equation, Ocean Eng., 2015, vol. 96, pp. 248–257.

    Article  Google Scholar 

  31. Fischer, N., Hughes, D., et al., Nonlinear RISE-Based control of an autonomous underwater vehicle, IEEE Trans. Robot, 2014, vol. 30, pp. 845–852.

    Article  Google Scholar 

  32. Narasimhan, M. and Singh, S.N., Adaptive optimal control of an autonomous underwater vehicle in the dive plane using dorsal fins, Ocean Eng., 2006, vol. 33, pp. 404–416.

    Article  Google Scholar 

  33. Zhu, D. and Sun, B., The bio-inspired model based hybrid sliding-mode tracking control for unmanned underwater vehicles, Eng. Appl. Artif. Intell., 2013, vol. 26, pp. 2260–2269.

    Article  Google Scholar 

  34. Raeisy, B., Safavi, A.A., and Khayatian, A.R., Optimized fuzzy control design of an autonomous underwater vehicle, Iran. J. Fuzzy Syst., 2012, vol. 9, pp. 25–41.

    MathSciNet  Google Scholar 

  35. Esfahani, H.N., Azimirad, V., and Danesh, M., A time delay controller included terminal sliding mode and fuzzy gain tuning for underwater vehicle-manipulator systems, Ocean Eng., 2015, vol. 107, pp. 97–107.

    Article  Google Scholar 

  36. Parhi, D.R., and Kundu, S., Review on guidance, control and navigation of autonomous underwater mobile robot, Int. J. Artif. Intell. Comput. Res., 2012, vol. 4, pp. 21–31.

    Google Scholar 

  37. El-Fakdi, A. and Carreras, M., Two-step gradient-based reinforcement learning for underwater robotics behavior learning, Robotics and Autonomous Systems, 2013, vol. 61, no.3, pp. 271–282.

    Article  Google Scholar 

  38. Zhang, L., Jiang, D., Zhao, J., and Shan, M., An AUV for ocean exploring and its motion control system architecture, Open Mechanical Engineering Journal, 2013, vol. 7, pp. 40–47.

    Article  Google Scholar 

  39. Hasankashefi, M., Bolouri, F., and Bolouri, K., Path planning and open-loop control algorithms for a differential thrust autonomous underwater vehicle, IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE), 2016, vol. 11, issue 4, pp. 151–158.

  40. Pinto, J., Borges de Sousa, J., Py, F., and Rajan, K., Experiments with deliberative planning on autonomous underwater vehicles, IROS Workshop on Robotics for Environmental Monitoring, Vila Moura, Portugal, 2012.

  41. Petillot, Y.R., Antonelli, G., Casalino, G., and Ferreira, F., Underwater Robots: From Remotely Operated Vehicles to Intervention-Autonomous Underwater Vehicles, IEEE Robot. Autom. Mag., 2019, vol. 26, pp. 94–101.

    Article  Google Scholar 

  42. Melo, J. and Matos, A., Survey on advances on terrain based navigation for autonomous underwater vehicles, Ocean Eng., 2017, vol. 139, pp. 250–264.

    Article  Google Scholar 

  43. Galarza, C., Masmitja, I., Prat, J., and Gomariz, S., Design of obstacle detection and avoidance system for Guanay II AUV, Appl. Sci., 2020, vol. 10, pp. 32–37.

    Google Scholar 

  44. Lin, C., Wang, H., Yuan, J., Yu, D., and Li, C., An improved recurrent neural network for unmanned underwater vehicle online obstacle avoidance, IEEE J. Ocean. Eng., 2019, vol. 44, pp. 120–133.

    Google Scholar 

  45. Sarda, E.I. and Dhanak, M.R., Launch and recovery of an autonomous underwater vehicle from a station-keeping unmanned surface vehicle, IEEE J. Ocean. Eng., 2019, vol. 44, pp. 290–299.

    Article  Google Scholar 

  46. Zhang, T., Wang, Z., Li, Y., and Tong, J., A passive acoustic positioning algorithm based on virtual long baseline matrix window, J. Navig., 2019, vol. 72, pp. 193–206.

    Article  Google Scholar 

  47. Wei, E., Dong, C., Yang, Y., Tang, S., Liu, J., Gong, G., and Deng, Z., A Robust Solution of Integrated SITAN with TERCOM Algorithm: Weight-Reducing Iteration Technique for Underwater Vehicles’ Gravity-Aided Inertial Navigation System, Navig., J. Inst. Navig.,2017, vol. 64, pp. 111–122.

    Article  Google Scholar 

  48. Salavasidis, G., Munafò, A., et al., Terrain-aided navigation for long-endurance and deep-rated autonomous underwater vehicles, J. Field Robot, 2019, vol. 36, pp. 447–474.

    Google Scholar 

  49. Eren, F., Pe’Eri, S., et al., Position, orientation and velocity detection of unmanned underwater vehicles using an optical detector array, Sensors, 2017, vol. 17, p. 1741.

    Article  Google Scholar 

  50. Zhong, L., Li, D., et al., A fast binocular localization method for AUV docking, Sensors, 2019, vol. 19, p. 1735.

    Article  Google Scholar 

  51. Liu, S., Xu, H., Lin, Y., and Gao, L., Visual navigation for recovering an AUV by another AUV in shallow water, Sensors, 2019, vol. 19, p. 1889.

    Article  Google Scholar 

  52. Monroy-Anieva, J.A., Rouviere, C., et al., Modeling and control of a micro AUV: Objects follower approach, Sensors, 2018, vol. 18, p. 2574.

    Article  Google Scholar 

  53. Wang, R., Wang, X., Zhu, M., and Lin, Y., Application of a Real-Time Visualization Method of AUVs in Underwater Visual Localization, Appl. Sci., 2019, vol. 9, p. 1428.

    Article  Google Scholar 

  54. Manzanilla, A., Reyes, S., et al., Autonomous navigation for unmanned underwater vehicles: Real-time experiments using computer vision, IEEE Robot, 2019, vol. 4, pp. 1351–1356.

    Google Scholar 

  55. Yan, Z., Wang, L., et al., Polar cooperative navigation algorithm for multi-unmanned underwater vehicles considering communication delays, Sensors, 2018, vol. 18, p. 1044.

    Article  Google Scholar 

  56. Cui, J., Zhao, L., Ma, Y., and Yu, J., Adaptive consensus tracking control for multiple autonomous underwater vehicles with uncertain parameters, ICIC Express Lett., 2019, vol. 13, pp. 191–200.

    Google Scholar 

  57. Baylog, J.G. and Wettergren, T.A., A ROC-based approach for developing optimal strategies in UUV search planning, IEEE J. Ocean. Eng., 2018, vol. 43, pp. 843–855.

    Article  Google Scholar 

  58. Li, J., Zhang, J., Zhang, G., and Zhang, B., An adaptive prediction target search algorithm for multi-AUVs in an unknown 3D environment, Sensors, 2018, vol. 18, p. 3853.

    Article  Google Scholar 

  59. Bing Sun, D.Z., et al., Complete coverage autonomous underwater vehicles path planning based on Glasiusbio-inspired neural network algorithm for discrete and centralized programming, IEEE Trans. Cogn. Dev. Syst., 2019, pp. 73–84.

  60. Yan, Z., Liu, X., Zhou, J., and Wu, D., Coordinated Target Tracking Strategy for Multiple Unmanned Underwater Vehicles with Time Delays, IEEE Access, 2018, vol. 6, pp. 10348–10357.

    Article  Google Scholar 

  61. Simetti, E., Wanderlingh, F., et al., Autonomous underwater intervention: Experimental results of the MARIS project, IEEE J. Ocean. Eng., 2018, vol. 43, pp. 620–639.

    Article  Google Scholar 

  62. Cataldi, E., Chiaverini, S., and Antonelli, G., Cooperative object transportation by two underwater vehicle-manipulator systems, Proc. 26th Mediterranean Conference on Control and Automation (MED), Zadar, Croatia. 19–22 June 2018, pp. 161–166.

  63. García-Valdovinos, L.G., Fonseca-Navarro, F., et al., Neuro-Sliding Control for Underwater ROV’s Subject to Unknown Disturbances, Sensors, 2019, vol. 19, p. 2943.

    Article  Google Scholar 

  64. Gorodetskii, V.I., Grushinskii, M.S., and Khabalov, A.V., Multi-agent systems (overview), Novosti iskusstvennogointellekta, 1998, no. 2, pp. 64–116.

  65. Rzhevski, G. and Skobelev, P., Kak upravlyat’ slozhnymi sistemami? Mul’tiagentnye tekhnologii dlya sozdaniya intellektualnykh system upravleniya predpriyatiyami (How to control complex systems? Multi-agent technologies for creating intelligent systems to manage enterprises), Samara: Ofort, 2015.Translated from English: George Rzevski, Petr Skobelev Managing Complexity, Ashurst: WIT Press Ashurst Lodge, UK.

Download references

ACKNOWLEDGMENTS

This work was supported by the Russian Foundation for Basic Research, project no. 19-08-00253.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. I. Mashoshin.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bykova, V.S., Martynova, L.A., Mashoshin, A.I. et al. A Dispatcher for a Multi-Agent Control System of an Autonomous Underwater Vehicle: Structure, Algorithms, and Simulation Results. Gyroscopy Navig. 11, 341–349 (2020). https://doi.org/10.1134/S2075108720040033

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S2075108720040033

Keywords:

Navigation