Hostname: page-component-5d84bcc8dc-8ls8l Total loading time: 0 Render date: 2026-09-13T02:28:08.600Z Has data issue: false hasContentIssue false
  • English
  • Français

Autonomous Ship Collision Avoidance Navigation Concepts, Technologies and Techniques

Published online by Cambridge University Press:  10 December 2007

Thomas Statheros ,
Gareth Howells  and
Klaus McDonald Maier
Thomas Statheros*
Affiliation:
(University of Kent, Department of Electronics)
Gareth Howells
Affiliation:
(University of Kent, Department of Electronics)
Klaus McDonald Maier
Affiliation:
(University of Essex, Department of Computing and Electronic Systems)
*
(Email: t.statheros@kent.ac.uk)
Get access Rights & Permissions [Opens in a new window]

Abstract

This study provides both a spherical understanding about autonomous ship navigation for collision avoidance (CA) and a theoretical background of the reviewed work. Additionally, the human cognitive abilities and the collision avoidance regulations (COLREGs) for ship navigation are examined together with water based collision avoidance algorithms. The requirements for autonomous ship navigation are addressed in conjunction with the factors influencing ship collision avoidance. Humans are able to appreciate these factors and also perform ship navigation at a satisfactory level, but their critical decisions are highly subjective and can lead to error and potentially, to ship collision. The research for autonomous ship navigation may be grouped into the classical and soft computing based categories. Classical techniques are based on mathematical models and algorithms while soft-computing techniques are based on Artificial Intelligence (AI). The areas of AI for autonomous ship collision avoidance are examined in this paper are evolutionary algorithms, fuzzy logic, expert systems, and neural networks (NN), as well as a combination of them (hybrid system).

Keywords

Autonomous ShipCollision avoidanceNavigation factorsCOLREGs

Information

Type
Research Article
Information
The Journal of Navigation , Volume 61 , Issue 1 , January 2008 , pp. 129 - 142
Copyright
Copyright © The Royal Institute of Navigation 2007

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

REFERENCES

Abril, J., Salom, J. & Calvo, O. (1997) Fuzzy control of a sailboat. International Journal Of Approximate Reasoning, 16, 359–375.CrossRefGoogle Scholar
Anderson, J. A. (1995) An Introduction to Neural Networks, Cambridge, MIT Press.CrossRefGoogle Scholar
Back, T. (1996) Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms, Oxford University Press.CrossRefGoogle Scholar
Bandler, W. & Kohout, L. J. (1980) Semantics Of Implication Operators And Fuzzy Relational Products. International Journal Of Man-Machine Studies, 12, 89–116.CrossRefGoogle Scholar
Belcher, P. (2002) A sociological interpretation of the COLREGS. Journal Of Navigation, 55, 213–224.CrossRefGoogle Scholar
Borenstein, J. & Koren, Y. (1989) Real-time obstacle avoidance for fast mobile robots. Systems, Man and Cybernetics, IEEE Transactions on, 19, 1179.CrossRefGoogle Scholar
Braspenning, P. J., Thuijsman, F. & Weijters, A. J. M. M. (1995) Artificial Neural Networks: An introduction to ANN Theory and Practice, Berlin, Springer.CrossRefGoogle Scholar
Browning, A. W. (1991) A Mathematical-Model To Simulate Small Boat Behavior. Simulation, 56, 329–336.CrossRefGoogle Scholar
Burns, R. S., Blackwell, G. & Calvert, S. (1988) An automatic guidance, navigation and collision avoidance system for ships at sea. IEE Colloquium on Control in Marine Industry, 3/1-3/3.Google Scholar
Cahill, R. A. (1983) Collision and their causes, London: Fairplay.Google Scholar
Chohra, A., Farah, A. & Belloucif, M. (1999) Neuro-fuzzy expert system E_S_CO_V for the obstacle avoidance behavior of intelligent autonomous vehicles. Advanced Robotics, 12, 629–649.CrossRefGoogle Scholar
Cockroft, A. N. (1984) Collision at Sea. Safety at Sea, 17–19.Google Scholar
Colley, B. A., Curtis, R. G. & Stockel, C. T. (1984) A Marine Traffic Flow And Collision Avoidance Computer-Simulation. Journal Of Navigation, 37, 232–250.CrossRefGoogle Scholar
Curtis, R. G. (1978) Determination of mariners' reaction times. Journal Of Navigation, 31, 408–417.CrossRefGoogle Scholar
Curtis, R. G. (1986) A Ship Collision Model For Overtaking. Journal Of The Operational Research Society, 37, 397–406.CrossRefGoogle Scholar
Dmitriev, S. P., Kolesov, N. V., Osipov, A. V. & Romanycheva, G. N. (2003) System of intelligent support of a ship navigator for collision avoidance. Journal Of Computer And Systems Sciences International, 42, 256–263.Google Scholar
EFstathiou, J. (1988) Expert systems, fuzzy logic and rule-based control explained at last. transactions Of The Institute Of Measurement And Control? (Trans Inst MC), 4, 198–206.CrossRefGoogle Scholar
El-Kader, F. A., El-Soud, M. S. A., El-Serafy, K. & Hassan, E. A. (2003) An integrated navigation system for Suez Canal (SCINS). Journal Of Navigation, 56, 241–255.CrossRefGoogle Scholar
Glick, T. F. & Kohn, D. (1996) Darwin on Evolution, Indianapolis, Indiana, USA, Hackett Publishing Company, Inc.Google Scholar
Graczyk, T., Jarzebski, C. A., Brebbia, C. A. & Burns, R. S. (1995) Methods to assign the safe maneuver and trajectory avoiding collision at sea. Proc. 1st Int. Conf. Marine Technol., 495–502.Google Scholar
Gung, W. S. (1990) The Research of Safely Coursing and Collision Avoidance between Ships in Traffic Separation Schemes under the Situation of Crossing and Overtaking. Taiwan, National Taiwan Marine University.Google Scholar
Hammer, A. & , K., , H. (1990) Knowledge Acquisition for Collision Avoidance Maneuver by Ship handling Simulator. International Conference on Marine Simulation and Ship Maneuverability, 245–252.Google Scholar
Harris, C. J., Hong, X. & Wilson, P. A. (1999) An intelligent guidance and control system for ship obstacle avoidance. Proceedings Of The Institution Of Mechanical Engineers Part I- Journal Of Systems And Control Engineering, 213, 311–320.Google Scholar
Hong, X., Harris, C. J. & Wilson, P. A. (1999) Autonomous ship collision free trajectory navigation and control algorithms. 2, 923.Google Scholar
Hwang, C. N., Yang, J. M. & Chiang, C. Y. (2001) The Design of Fuzzy Collision Avoidance Expert System Implemented by H-Infinity autopilot. Journal of Marine Science and Technology, 9, 25–37.CrossRefGoogle Scholar
Im, K.-Y. & Oh, S.-Y. (2000) An extended virtual force field based behavioral fusion with neural networks and evolutionary programming for mobile robot navigation. Proc. of the 2000 Congress on Evolutionary Computations, 2, 1238–1244.Google Scholar
Imazu, H., Sugisaki, A. M. & Min, Q. (1979) The Possible Collision Risk in Radar Navigation. Journal of Navigation in Japan, 65, 75–81.Google Scholar
Ito, M., Zhang, F. & Yoshida, N. (1999) Collision avoidance control of ship with genetic algorithm. Proceedings of the IEEE International Conference on Control Applications, 2, 1791–1796.Google Scholar
James, M. K. (1994) The Timing Of Collision-Avoidance Maneuvers – Descriptive Mathematical-Models. Journal Of Navigation, 47, 259–272.CrossRefGoogle Scholar
Jing, X., Michalewicz, Z., Lixin, Z. & Trojanowski, K. (1997) Adaptive evolutionary planner/navigator for mobile robots. Evolutionary Computation, IEEE Transactions, 1, 18–28.CrossRefGoogle Scholar
Jones, K. D. (1978) Decision making when using collision avoidance system. Journal of Navigation, 31, 173–180.Google Scholar
Kemp, J. (2002) Collision regulations – Discussion. Journal Of Navigation, 55, 145–146.CrossRefGoogle Scholar
Khanna, T. (1990) Foundations of neural networks, New York, Addison-Wesley.Google Scholar
Khatib, O. (1985) Real-time obstacle avoidance for manipulators and mobile robots. 2, 500.Google Scholar
Lamb, W. G. P. (1985) The Calculation Of Marine Collision Risks. Journal Of Navigation, 38, 365–374.CrossRefGoogle Scholar
Lee, J. D., & Sanquist, T. F. (1996) Maritime Automation. Automation and human performance: Theory and applications, 365–384.Google Scholar
Lee, S. M., Kwon, K. Y. & Joh, J. (2004) A fuzzy logic for autonomous navigation of marine vehicles satisfying COLREG guidelines. International Journal Of Control Automation And Systems, 2, 171–181.Google Scholar
Lee, Y. I. & Kim, Y. G. (2004) A collision avoidance system for autonomous ship using fuzzy relational products and COLREGs. Intelligent Data Engineering And Automated Learning Ideal 2004, Proceedings. Berlin, Springer-Verlag Berlin.Google Scholar
Lewis, F. L. (1986) Optimal Estimation: With an Introduction to Stochastic Control Theory, John Wiley & Sons.Google Scholar
Lin, H. S., , J, , X. & Michalewicz, Z. (1994) Evolutionary algorithm for path planning in mobile robot environment. IEEE World Congress on Computational Intelligence., Proceedings of the First IEEE Conference, 211–216.Google Scholar
Lisowski, J. (1985) A simulation study of various approximate models of ship dynamics in collision avoidance problem. Found. Contr. Eng., 10, 176–183.Google Scholar
Lisowski, J. & Smierzchalski, R. (1994) Computer simulation of safe path maneuver and trajectory avoiding collision at sea. In Joint Proceedings, Gdynia Maritime Academy Housechule Bremerhaven.Google Scholar
Lisowski, J. & Smierzchalski, R. (1995) Assigning of safe and optimal trajectory avoiding collision at sea. in Proc. 3rd IFAC Workshop Contr. Appl. Marine Syst., 346–350.Google Scholar
Lord, W. (1955) A Night to Remember, New York: Holt, Rinehart & Winston.Google Scholar
Patterson, D. W. (1996) Artificial Neural Networks: Theory and Applications, Singapore, Prentice Hall.Google Scholar
Pedersen, E. (1999) On the effect of plotting performance by the errors of pointing targets in the ARPA system. Journal Of Navigation, 52, 119–125.CrossRefGoogle Scholar
Perrow, C. (1984) Normal accidents, Basic Books.Google Scholar
Rasmussen, J. (1983) Skills, Rules, And Knowledge – Signals, Signs, And Symbols, And Other Distinctions In Human-Performance Models. Ieee Transactions On Systems Man And Cybernetics, 13, 257–266.CrossRefGoogle Scholar
Robert, G., Hockey, J., Healey, A., Crawshaw, M., Wastell, D. G. & Sauer, J. (2003) Cognitive demands of collision avoidance in simulated ship control. Human Factors, 45, 252–265.CrossRefGoogle Scholar
Salinas, C. F. (2002) Collision regulations discussions. Journal Of Navigation, 55, 501–505.CrossRefGoogle Scholar
Skjong, R. & Mjelde, K. M. (1982) Optimal evasive manoeuvre for ship in an environment of fixed installations and other ships. Identification and Control, 3, 211–222.CrossRefGoogle Scholar
Smierzchalski, R. & Michalewicz, Z. (1998) Adaptive modeling of a ship trajectory in collision situations at sea. 342.Google Scholar
Smierzchalski, R. & Michalewicz, Z. (2000) Modeling of ship trajectory in collision situations by an evolutionary algorithm. Ieee Transactions On Evolutionary Computation, 4, 227–241.CrossRefGoogle Scholar
Stefik, M. (1995) Introduction to Knowleds Systems, San Francisco, Morgan Kaufmann Publishers, Inc.Google Scholar
Stewart, B. S., Liaw, C. F. & White, C. C. (1994) A Bibliography Of Heuristic-Search Research Through 1992. Ieee Transactions On Systems Man And Cybernetics, 24, 268–293.CrossRefGoogle Scholar
Vonk, E., Jain, L. C. & Johnson, R. P. (1997) Automatic Generation of Neural Network Architecture Using Evolutionary Computation, World Scientific.CrossRefGoogle Scholar
Wagenaar, W. A. & Groeneweg, J. (1987) Accidents At Sea – Multiple Causes And Impossible Consequences. International Journal Of Man-Machine Studies, 27, 587–598.CrossRefGoogle Scholar
Wilson, P. A., Harris, C. J. & Hong, X. (2003) A line of sight counteraction navigation algorithm for ship encounter collision avoidance. Journal Of Navigation, 56, 111–121.CrossRefGoogle Scholar
Xianyi, Y. (1999) A Neural Network Approach to Real-Time Collision-Free Navigation of 3-D.O.F. Robots in 2D. ICRA, 23–28.Google Scholar
Xiao-Ming, Z. & Ito, M. (2001) Planning a collision avoidance model for ship using genetic algorithm. 4, 2355.Google Scholar
Yavin, Y., Frangos, C., Miloh, T. & Zilman, G. (1997) Collision avoidance by a ship with a moving obstacle: Computation of feasible command strategies. Journal Of Optimization Theory And Applications, 93, 53–66.CrossRefGoogle Scholar
Yavin, Y., Miloh, T. & Zilman, G. (1995) Parametric Study Of Ship Maneuverability In Laterally Restricted Waters – Stochastic-Control Approach. Journal Of Optimization Theory And Applications, 85, 59–74.CrossRefGoogle Scholar
Yavin, Y., Zilman, G. & Miloh, T. (1994) A Feasibility Study Of Ship Maneuverability In The Vicinity Of An Obstacle – A Stochastic-Control Approach. Computers & Mathematics With Applications, 28, 63–76.CrossRefGoogle Scholar
Zadeh, L. A. (1965) Fuzzy Sets. Information and Control, 8, 338–353.CrossRefGoogle Scholar
Zeng, X. M. (2003) Evolution of the safe path for ship navigation. Applied Artificial Intelligence, 17, 87–104.CrossRefGoogle Scholar
Zeng, X. M., Ito, M. & Shimizu, E. (2000) Collision avoidance of moving obstacles for ship with genetic algorithm. 513.Google Scholar
Zhao, J. (1996) Maritime Collision and Liability. Dept of Ship Science. University of Southampton.Google Scholar