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A Discrete Artificial Potential Field for Ship Trajectory Planning

Published online by Cambridge University Press:  13 June 2019

Agnieszka Lazarowska Open the ORCID record for Agnieszka Lazarowska [Opens in a new window]
Agnieszka Lazarowska*
Affiliation:
(Department of Ship Automation, Faculty of Electrical Engineering, Gdynia Maritime University, Morska St. 81-87, 81-225 Gdynia, Poland)
*
(E-mail: a.lazarowska@we.umg.edu.pl)
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Abstract

This paper introduces an approach for solving a safe ship trajectory planning problem. The algorithm, utilising the concept of a discrete artificial potential field and a path optimisation algorithm, calculates an optimised collision-free trajectory for a ship. The method was validated by simulation tests with the use of real navigational data registered on board the research and training ship Horyzont II. Results of simulation studies demonstrate that the approach is capable of finding a collision-free trajectory in near-real time, and this proves its applicability in commercial collision avoidance systems for ships. The paper contributes to the development of decision support systems for ships and autonomous navigation.

Keywords

NavigationShip collisionTrajectory planning

Information

Type
Research Article
Information
The Journal of Navigation , Volume 73 , Issue 1 , January 2020 , pp. 233 - 251
Copyright
Copyright © The Royal Institute of Navigation 2019 

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References

REFERENCES

Chen, X. and Tan, W. W. (2013). Tracking control of surface vessels via fault-tolerant adaptive backstepping interval type-2 fuzzy control. Ocean Engineering, 70, 97–109.Google Scholar
Chu, Z., Zhu, D. and Jan, G. E. (2016). Observer-based adaptive neural network control for a class of remotely operated vehicles. Ocean Engineering, 127, 82–89.Google Scholar
Cockcroft, A. and Lameijer, J. (2012). A Guide to the Collision Avoidance Rules. Butterworth-Heinemann.Google Scholar
Fossen, T. I. (2011). Handbook of Marine Craft Hydrodynamics and Motion Control. John Wiley & Sons Ltd.Google Scholar
Ge, S. S. and Cui, Y. J. (2002). Dynamic Motion Planning for Mobile Robots Using Potential Field Method. Autonomous Robots, 13, 207–222.Google Scholar
Huang, L. (2009). Velocity planning for a mobile robot to track a moving target—a potential field approach. Robotics and Autonomous Systems, 57, 55–63.Google Scholar
Hui, N. B. and Pratihar, D. K. (2009). A comparative study on some navigation schemes of a real robot tackling moving obstacles. Robotics and Computer-Integrated Manufacturing, 25, 810–828.Google Scholar
International Maritime Organization (2019). Convention on the International Regulations for Preventing Collisions at Sea, 1972 (COLREGs). http://www.imo.org. Accessed 29 April 2019.Google Scholar
Khatib, O. (1986). Real-Time Obstacle Avoidance for Manipulators and Mobile Robots. The International Journal of Robotics Research, 5(1), 90–98.Google Scholar
Lee, S.-M., Kwon, K.-Y. and Joh, J. (2004). A Fuzzy Logic for Autonomous Navigation of Marine Vehicles Satisfying COLREG Guidelines. International Journal of Control, Automation and Systems, 2(2), 171–181.Google Scholar
Lisowski, J. (2017). Game Control of maritime objects. Journal of Marine Engineering & Technology, 16(4), 165–176.Google Scholar
Liu, Y., Bucknall, R. and Zhang, X. (2017). The fast marching method based intelligent navigation of an unmanned surface vehicle. Ocean Engineering, 142, 363–376.Google Scholar
Mohamed-Seghir, M. (2014). The Branch-and-bound Method, Genetic Algorithm, and Dynamic Programming to Determine a Safe Ship Trajectory in Fuzzy Environment. Procedia Computer Science, 35, 348–357.Google Scholar
Naeem, W., Henrique, S. C. and Hu, L. (2016). A Reactive COLREGs-Compliant Navigation Strategy for Autonomous Maritime Navigation. IFAC-PapersOnLine, 49(23), 207–213.Google Scholar
Rybczak, M. (2018). Improvement of control precision for ship movement using a multidimensional controller. Automatika, 59(1), 63–70.Google Scholar
Smierzchalski, R. and Michalewicz, Z. (2000). Modeling of ship trajectory in collision situations by an evolutionary algorithm. IEEE Transactions On Evolutionary Computation, 4, 227–241.Google Scholar
Szlapczynski, R. and Szlapczynska, J. (2017). A method of determining and visualizing safe motion parameters of a ship navigating in restricted waters. Ocean Engineering, 129, 363–373.Google Scholar
Tam, C. and Bucknall, R. (2010). Path-planning algorithm for ships in close-range encounters. Journal of Marine Science and Technology, 15(4), 395–407.Google Scholar
Tomera, M. (2017). Hybrid switching controller design for the maneuvering and transit of a training ship. International Journal of Applied Mathematics and Computer Science, 27(1), 63–77.Google Scholar
Valbuena, L. and Tanner, H. G. (2012). Hybrid Potential Field Based Control of Differential Drive Mobile Robots, Journal of Intelligent and Robotic Systems, 68, 307–322.Google Scholar
Wang, T. F., Yan, X. P. and Wang, Y. (2017). Ship Domain Model for Multi-ship Collision Avoidance Decision-making with COLREGs Based on Artificial Potential Field. TransNav, The International Journal on Marine Navigation and Safety of Sea Transportation, 11(1), 85–92.Google Scholar
Witkowska, A. and Smierzchalski, R. (2018). Adaptive dynamic control allocation for dynamic positioning of marine vessel based on backstepping method and sequential quadratic programming. Ocean Engineering, 163, 570–582.Google Scholar