JACIII Vol.24 No.1 pp. 26-39
doi: 10.20965/jaciii.2020.p0026


A Hybrid Fuzzy System Dynamics Approach for Risk Analysis of AUV Operations

Tzu Yang Loh*1, Mario P. Brito*2, Neil Bose*3, Jingjing Xu*4, Natalia Nikolova*1,*5, and Kiril Tenekedjiev*1,*5

*1Australian Maritime College, University of Tasmania
1 Maritime Way, Launceston, Tasmania 7250, Australia

*2Southampton Business School, University of Southampton
Building 2, 12 University Road, Highfield, Southampton SO17 1BJ, United Kingdom

*3Memorial University of Newfoundland
230 Elizabeth Avenue, St. John’s, Newfoundland and Labrador A1C 5S7, Canada

*4Faculty of Business, University of Plymouth
Cookworthy Building, Drake Circus, Plymouth PL4 8AA, United Kingdom

*5Nikola Vaptsarov Naval Academy
73 Vasil Drumev Street, Varna 9026, Bulgaria

March 14, 2019
August 29, 2019
January 20, 2020
autonomous underwater vehicle, hybrid system dynamics, fuzzy set theory, risk analysis

The maturing of autonomous technology has fostered a rapid expansion in the use of Autonomous Underwater Vehicles (AUVs). To prevent the loss of AUVs during deployments, existing risk analysis approaches tend to focus on technicalities, historical data and experts’ opinion for probability quantification. However, data may not always be available and the complex interrelationships between risk factors are often neglected due to uncertainties. To overcome these shortfalls, a hybrid fuzzy system dynamics risk analysis (FuSDRA) is proposed. The approach utilises the strengths while overcoming limitations of both system dynamics and fuzzy set theory. Presented as a three-step iterative framework, the approach was applied on a case study to examine the impact of crew operating experience on the risk of AUV loss. Results showed not only that initial experience of the team affects the risk of loss, but any loss of experience in earlier stages of the AUV program have a lesser impact as compared to later stages. A series of risk control policies were recommended based on the results. The case study demonstrated how the FuSDRA approach can be applied to inform human resource and risk management strategies, or broader application within the AUV domain and other complex technological systems.

Fuzzy system dynamics for risk analysis

Fuzzy system dynamics for risk analysis

Cite this article as:
T. Loh, M. Brito, N. Bose, J. Xu, N. Nikolova, and K. Tenekedjiev, “A Hybrid Fuzzy System Dynamics Approach for Risk Analysis of AUV Operations,” J. Adv. Comput. Intell. Intell. Inform., Vol.24 No.1, pp. 26-39, 2020.
Data files:
  1. [1] X. Li, D. Zhu, and Y. Qian, “A Survey on Formation Control Algorithms for Multi-AUV System,” Unmanned Systems, Vol.2, No.4, pp. 351-359, 2014.
  2. [2] A. Sgorbissa, “Integrated robot planning, path following, and obstacle avoidance in two and three dimensions: Wheeled robots, underwater vehicles, and multicopters,” Int. J. of Robotics Research, Vol.36, Issue 7, pp. 853-876, 2019.
  3. [3] M. Carreras, N. Palomeras, P. Ridao, and D. Ribas, “Design of a mission control system for an AUV,” Int. J. of Control, Vol.80, Issue 7, pp. 993-1007, 2007.
  4. [4] L. Paull, S. Saeedi, M. Seto, and H. Li, “AUV Navigation and Localization: A Review,” IEEE J. of Oceanic Engineering, Vol.39, Issue 1, pp. 131-149, 2014.
  5. [5] V. Ganesan, M. Chitre, and E. Brekke, “Robust underwater obstacle detection and collision avoidance,” Autonomous Robots, Vol.40, Issue 7, pp. 1165-1185, 2016.
  6. [6] J. Hwang, N. Bose, and S. Fan, “AUV Adaptive Sampling Methods: A Review,” Applied Sciences, Vol.9, Issue 15, doi: 10.3390/app9153145, 2019.
  7. [7] G. T. Reader, J. Potter, and J. G. Hawley, “The Evolution of AUV Power Systems,” Proc. of the OCEANS ’02 MTS/IEEE, Vol.1, pp. 191-198, 2002.
  8. [8] M.-H. Oh and J.-H. Oh, “Homing and docking control of AUV using model predictive control,” Proc. of the 5th ISOPE Pacific/Asia Offshore Mechanics Symp., pp. 138-142, 2002.
  9. [9] E. R. B. Marques, J. Pinto, S. Kragelund, P. S. Dias, L. Madureira, A. Sousa, M. Correia, H. Ferreira, R. Gonçalves, R. Martins, D. P. Horner, A. J. Healey, G. M. Gonçalves, and J. B. Sousa, “AUV Control and Communication using Underwater Acoustic Networks,” Proc. of the OCEANS 2007 – Europe, 6pp., 2007.
  10. [10] A. A. Pereira, J. Binney, G. A. Hollinger, and G. S. Sukhatme, “Risk-Aware Path Planning for Autonomous Underwater Vehicles using Predictive Ocean Models,” J. of Field Robotics, Vol.30, Issue 5, pp. 741-762, 2013.
  11. [11] J. Sattar, P. Giguére, and G. Dudek, “Sensor-based Behavior Control for an Autonomous Underwater Vehicle,” The Int. J. of Robotics Research, Vol.28, Issue 6, 2009.
  12. [12] G. Griffiths and K. Collins (Eds.), “Masterclass in AUV Technology for Polar Science,” Society for Underwater Technology, 2007.
  13. [13] M. P. Brito, G. Griffiths, and P. Challenor, “Risk Analysis for Autonomous Underwater Vehicle Operations in Extreme Environments,” Risk Analysis, Vol.30, Issue 12, pp. 1771-1788, 2010.
  14. [14] M. Brito, G. Griffiths, J. Ferguson, D. Hopkin, R. Mills, R. Pederson, and E. MacNeil, “A Behavioral Probabilistic Risk Assessment Framework for Managing Autonomous Underwater Vehicle Deployments,” J. of Atmospheric and Oceanic Technology, Vol.29, No.11, pp. 1689-1703, 2012.
  15. [15] G. Griffiths and M. Brito, “Predicting risk in missions under sea ice with Autonomous Underwater Vehicles,” Proc. of the 2008 IEEE/OES Autonomous Underwater Vehicles, 7pp., doi: 10.1109/AUV.2008.5290536, 2008.
  16. [16] M. Brito and G. Griffiths, “A Bayesian approach for predicting risk of autonomous underwater vehicle loss during their missions,” Reliability Engineering & System Safety, Vol.146, pp. 55-67, 2016.
  17. [17] M. P. Brito and G. Griffiths, “Updating Autonomous Underwater Vehicle Risk Based on the Effectiveness of Failure Prevention and Correction,” J. of Atmospheric and Oceanic Technology, Vol.35, No.4, pp. 797-808, 2018.
  18. [18] T. Y. Loh, M. P. Brito, N. Bose, J. Xu, and K. Tenekedjiev, “A Fuzzy-Based Risk Assessment Framework for Autonomous Underwater Vehicle Under-Ice Missions,” Risk Analysis, doi: 10.1111/risa.13376, 2019.
  19. [19] M. P. Brito and G. Griffiths, “A Systems Dynamics Framework for Risk Management of Multiple Autonomous Underwater Vehicles,” Proc. of the 11th Int. Probabilistic Safety Assessment and Management Conf. and the Annual European Safety and Reliability Conf. 2012 (PSAM11 ESREL 2012), pp. 2093-2101, 2012.
  20. [20] C. A. Thieme, I. B. Utne, and I. Schjølberg, “Risk modeling of autonomous underwater vehicle operation focusing on the human operator,” Safety and Reliability of Complex Engineered Systems: Proc. of the 25th European Safety and Reliability Conf. (ESREL 2015), pp. 3653-3660, 2015.
  21. [21] R. Stokey, T. Austin, C. von Alt, M. Purcell, R. Goldsborough, N. Forrester, and B. Allen, “AUV Bloopers or Why Murphy Must have been an Optimist: A Practical Look at Achieving Mission Level Reliability in an Autonomous Underwater Vehicle,” Proc. of the 11th Int. Symp. on Unmanned Untethered Submersible Technology, pp. 32-40, 1999.
  22. [22] G. Ho, N. Pavlovic, and R. Arrabito, “Human Factors Issues with Operating Unmanned Underwater Vehicles,” Proc. of the Human Factors and Ergonomics Society Annual Meeting, Vol.55, pp. 429-433, 2011.
  23. [23] I. B. Utne and I. Schjølberg, “A Systematic Approach to Risk Assessment: Focusing on Autonomous Underwater Vehicles and Operations in Arctic Areas,” Proc. of the 33rd Int. Conf. on Ocean, Offshore and Arctic Engineering (ASME 2014), Vol.10, 10pp., 2014.
  24. [24] C. A. Thieme, I. B. Utne, and I. Schjølberg, “A Risk Management Framework for Unmanned Underwater Vehicles Focusing on Human and Organizational Factors,” Proc. of the 34th Int. Conf. on Ocean, Offshore and Arctic Engineering (ASME 2015), Vol.3, 10pp., 2015.
  25. [25] E. H. Schein, “Organizational Culture and Leadership,” 5th Edition. John Wiley & Sons, 2016.
  26. [26] J. Reason, “Human Error,” Cambridge University Press, 1990.
  27. [27] N. G. Leveson, “Engineering a Safer World: Systems Thinking Applied to Safety,” The MIT Press, 2011.
  28. [28] T. Y. Loh, M. P. Brito, N. Bose, J. Xu, and K. Tenekedjiev, “Fuzzy system dynamics risk analysis (FuSDRA) of autonomous underwater vehicle operations in the Antarctic,” University of Tasmania (UTAS) Open Access Repository, 2019, [accessed March 15, 2019]
  29. [29] T. Y. Loh, M. Brito, N. Bose, J. Xu, and K. Tenekedjiev, “Policy recommendations for autonomous underwater vehicle operations through hybrid Fuzzy System Dynamics Risk Analysis (FuSDRA),” Proc. of the Int. Association of Maritime Universities Conf. (IAMUC 2019) (in press).
  30. [30] M. Khanzadi, F. Nasirzadeh, and M. Alipour, “Integrating system dynamics and fuzzy logic modeling to determine concession period in BOT projects,” Automation in Construction, Vol.22, pp. 368-376, 2012.
  31. [31] F. Nasirzadeh, M. Khanzadi, and M. Rezaie, “Dynamic modeling of the quantitative risk allocation in construction projects,” Int. J. of Project Management, Vol.32, Issue 3, pp. 442-451, 2014.
  32. [32] M. Mutingi and C. Mbohwa, “Fuzzy System Dynamics of Manpower Systems,” P. M. Vasant (Ed.), “Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications,” pp. 913-930, 2014.
  33. [33] International Organization for Standardization, “ISO 31000 Risk Management,” [accessed June 19, 2018]
  34. [34] Intermational Organization for Standardization, “ISO 45001 Occupational Health and Safety,” [accessed September 3, 2018]
  35. [35] J. M. Mendel, “Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions,” Prentice Hall, 2001.
  36. [36] F. M. McNeil and E. Thro, “Fuzzy Logic: A Pratical Approach,” Morgan Kaufmann, 1994.
  37. [37] R. Zhao and R. Govind, “Defuzzification of Fuzzy Intervals,” Fuzzy Sets and Systems, Vol.43, Issue 1, pp. 45-55, 1991.
  38. [38] W. V. Leekwijck and E. E. Kerre, “Defuzzification: criteria and classification,” Fuzzy Sets and Systems, Vol.108, Issue 2, pp. 159-178, 1999.
  39. [39] W. Z. Chmielowski, “Fuzzy Control in Environmental Engineering,” Springer, 2015.
  40. [40] Ventana Systems, Inc., “Vensim User Guide,” [accessed April 24, 2018]
  41. [41] The MathWorks, Inc., “Fuzzy Logic Toolbox™ User’s Guide Release 2017b,” [accessed January 5, 2018]
  42. [42] “Simulink® User’s Guide,” The MathWorks, Inc., 2015, [accessed March 17, 2018]
  43. [43] E. D. Brown and N. J. J. Gaskell, “The Operation of Autonomous Underwater Vehicle – Vol.2: Report on the Law,” Society for Underwater Technology, 2000.
  44. [44] M. P. Brito and G. Griffiths, “A Markov Chain State Transition Approach to Establishing Critical Phases for AUV Reliability,” IEEE J. of Oceanic Engineering, Vol.36, Issue 1, pp. 139-149, 2011.
  45. [45] G. Griffiths, N. W. Millard, S. D. McPhail, P. Stevenson, and P. G. Challenor, “On the Reliability of the Autosub Autonomous Underwater Vehicle,” Underwater Technology, Vol.25, No.4, pp. 175-184, 2000.
  46. [46] M. P. Brito, “Reliability Case Notes No.8. Risk and Reliability Analysis of Autosub 6000 autonomous underwater vehicle,” National Oceanography Centre Southampton Research and Consultancy Report No.50, 2015, [accessed August 12, 2018]
  47. [47] J. E. Strutt, “Report of the Inquiry into the Loss of Autosub2 under the Fimbulisen,” National Oceanography Centre Southampton Research and Consultancy Report No.12, 2006, [accessed December 11, 2017]
  48. [48] J. D. Sterman, “Business Dynamics: Systems Thinking and Modeling for a Complex World,” McGraw-Hill Education, 2000.
  49. [49] A. Saltelli, M. Ratto, T. Andres, F. Campolongo, J. Cariboni, D. Gatelli, M. Saisana, and S. Tarantola, “Global Sensitivity Analysis: The Primer,” John Wiley & Sons, 2008.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jul. 12, 2024