DOI QR코드

DOI QR Code

The Effectiveness of MOOS-IvP based Design of Control System for Unmanned Underwater Vehicles

MOOS-IvP를 이용한 무인잠수정 제어기 개발의 효용성

  • Received : 2013.11.29
  • Accepted : 2014.02.03
  • Published : 2014.06.30

Abstract

This paper demonstrates the benefit of using MOOS-IvP in the development of control system for Unmanned Underwater Vehicles(UUV). The demand for autonomy in UUVs has significantly increased due to the complexity in missions to be performed. Furthermore, the increased number of sensors and actuators that are interconnected through a network has introduced a need for a middleware platform for UUVs. In this context, MOOS-IvP, which is an open source software architecture, has been developed by several researchers from MIT, Oxford University, and NUWC. The MOOS software is a communication middleware based on the publish-subscribe architecture allowing each application to communicate through a MOOS database. The IvP Helm, which is one of the MOOS modules, publishes vehicle commands using multi-objective optimization in order to implement autonomous decision making. This paper explores the benefit of MOOS-IvP in the development of control software for UUVs by using a case study with an auto depth control system based on self-organizing fuzzy logic control. The simulation results show that the design and verification of UUV control software based on MOOS-IvP can be carried out quickly and efficiently thanks to the reuse of source codes, modular-based architecture, and the high level of scalability.

Keywords

References

  1. X.Q. Chen, Y.Q. Chen, J.G. Chase, Mobile Robots - State of the Art in Land, Sea, and Collaborative Missions, I-Tech Education and Publishing, Austria, 2009.
  2. Y. Song, S. Byun, B. Choi, H. Kim, J. Seo, D. Kim, "Architecture design of ASW UUV simulator based on MOOS-IVP," Proceedings of Fall Workshop on Korea Unmanned Underwater Vehicle, pp.12-15, 2012 (in Korean).
  3. M.R. Benjamin, H. Schmidt, P.M. Newman, J.J. Leonard, "Nested autonomy for unmanned marine vehicles with MOOS-IvP," Journal of Field Robotics, Vol. 27, No. 6, pp.834-875, 2010. https://doi.org/10.1002/rob.20370
  4. M.R. Benjamin, H. Schmidt, P.M. Newman, J.J. Leonard, "An overview of MOOS- IvP and a users guide to the IvP Helm," MIT CSAIL Technical Report, 2013.
  5. D. Lee, D. Kwak, J. Choi, "An intelligent control system design for autonomous underwater vehicle," Journal of Control, Automation and System Engineering, Vol. 3, No. 3, pp.227-237, 1997 (in Korean).
  6. M.R. Benjamin, "The interval programming model for multi-objective decision making," Technical Report AIM-2004-021, Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, 2004.
  7. S. Yasunobu, T. Hasegawa, "Evaluation of an automatic container crane operation system based on predictive fuzzy control," Control Theory and Advanced Technology, Vol. 2, No. 3, pp.419-432, 1986.
  8. M.C.M. Teixeira and S. H. Zak, "Stabilizing contoller design for uncertain nonlinear systems using fuzzy models," IEEE Transactions on Fuzzy Systems, Vol. 7, No. 2, pp.133-142, 1999. https://doi.org/10.1109/91.755395
  9. T. Tagaki, M. Sugeno, "Fuzzy identification of systems and its applications to modeling and control," IEEE Transactions on Systems, Man, and Cybernetics Society, Vol. 15, No. 1, pp.116-132, 1985.
  10. T.J. Procyk, E.H. Mamdani, "A linguistic self-organising process controller," Automatica, Vol. 15, pp.15-30, 1979. https://doi.org/10.1016/0005-1098(79)90084-0
  11. J. Shieh, M. Fu, S. Huang, M. Kao, "Comparison of the applicability of rulebased and self-organizing fuzzy logic controllers for sedation control of intracranial pressure pattern in a neurosurgical intensive care unit," IEEE Transaction on Biomedical Engineering, Vol. 53, No. 8, pp.1700-1705, 2006. https://doi.org/10.1109/TBME.2006.873757
  12. H. Ying, F. Lin, R.D. MacArthur, J.A. Cohn, D.C. Barth-Jones, H. Ye, L.R. Crane, "A self- learning fuzzy discrete event system for HIV/AIDS treatment regimen selection," IEEE Transactions on Systems, Man, and Cybernetics, Vol. 37, No. 4, pp.966-979, 2007. https://doi.org/10.1109/TSMCB.2007.895360
  13. D.A. Linkens, S.B. Hasnain, "Self- organising fuzzy logic control and application to muscle relaxant anaesthesia," IEE Proceedings-D, Vol. 138, No. 3, pp.274-284, 1991. https://doi.org/10.1049/ip-d.1991.0038
  14. C. Lin, C. Hsu, "Self-learining fuzzy sliding-mode control for antilock braking systems," IEEE Transactions on Control Systems Technology, Vol. 11, No. 2, pp.273-278, 2003. https://doi.org/10.1109/TCST.2003.809246
  15. N. Khaehintung, C. Kangsajian, P. Sirisuk, and A. Kunakorn, "Grid-connected photovoltaic system with maximum power point tracking using self-organizing fuzzy logic controller," Power Electronics and Drives Systems, pp.517-521, 2005.