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Framework of an Evolutionary Multi-Objective Optimisation Method for Planning a Safe Trajectory for a Marine Autonomous Surface Ship

Abstract

This paper represents the first stage of research into a multi-objective method of planning safe trajectories for marine autonomous surface ships (MASSs) involved in encounter situations. Our method applies an evolutionary multi-objective optimisation (EMO) approach to pursue three objectives: minimisation of the risk of collision, minimisation of fuel consumption due to collision avoidance manoeuvres, and minimisation of the extra time spent on collision avoidance manoeuvres. Until now, a fully multi-objective optimisation has not been applied to the real-time problem of planning safe trajectories; instead, this optimisation problem has usually been reduced to a single aggregated cost function covering all objectives. The aim is to develop a method of planning safe trajectories for MASSs that is able to simultaneously pursue the three abovementioned objectives, make decisions in real time and without interaction with a human operator, handle basic types of encounters (in open or restricted waters, and in good or restricted visibility) and guarantee compliance with the International Regulations for Preventing Collisions at Sea. It should also be mentioned that optimisation of the system based on each criterion may occur at the cost of the others, so a reasonable balance is applied here by means of a configurable trade-off. This is done throughout the EMO process by means of modified Pareto dominance rules and by using a multi-criteria decision-making phase to filter the output Pareto set and choose the final solution.

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Category:
Articles
Type:
artykuły w czasopismach
Published in:
Polish Maritime Research no. 26, pages 69 - 80,
ISSN: 1233-2585
Language:
English
Publication year:
2019
Bibliographic description:
Szłapczyński R., Ghaemi M.: Framework of an Evolutionary Multi-Objective Optimisation Method for Planning a Safe Trajectory for a Marine Autonomous Surface Ship// Polish Maritime Research -Vol. 26,iss. 4(104) (2019), s.69-80
DOI:
Digital Object Identifier (open in new tab) 10.2478/pomr-2019-0068
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  1. Bechikh, S., M. Kessentini, L. Ben Said, K. Ghédira: Preference Incorporation in Evolutionary Multiobjective Optimization: A Survey of the State-of-the-Art, Adv. Comput. 98 (2015) 141-207. open in new tab
  2. Bertaska, I.R., B. Shah, K. Von Ellenrieder, P. Švec, W. Klinger, A.J. Sinisterra, M. Dhanak, S.K. Gupta: Experimental evaluation of automatically-generated behaviors for USV operations, Ocean Eng. 106 (2015) 496-514. open in new tab
  3. Branke, J., T. Kaußler, H. Schmeck: Guidance in evolutionary multi-objective optimization, Adv. Eng. Softw. 32 (2001) 499-507. open in new tab
  4. Burmeister, H.-C., W. Bruhn, Ø.J. Rødseth, T. Porathe: Autonomous Unmanned Merchant Vessel and its Contribution towards the e-Navigation Implementation: The MUNIN Perspective, Int. J. e-Navigation Marit. Econ. 1 (2014) 1-13. open in new tab
  5. Campbell, S., W. Naeem, G.W. Irwin: A review on improving the autonomy of unmanned surface vehicles through intelligent collision avoidance manoeuvres, Annu. Rev. Control. 36 (2012) 267-283. open in new tab
  6. Chroni, Dionysia & Liu, Shukui & Plessas, Timoleon & Papanikolaou, Apostolos. (2015). Simulation of the maneuvering behavior of ships under the influence of environmental forces. (2015) 111-120. DOI: 10.1201/b18855-16. open in new tab
  7. Cockcroft, A.N., Lameijer J.N.F.: A guide to the collision avoidance rules: international regulations for preventing collisions at sea, Elsevier, 2012. open in new tab
  8. Fossen, T.I.: Handbook of Marine Craft Hydrodynamics and Motion Control, John Wiley & Sons, Ltd, Chichester, UK, 2011. open in new tab
  9. Hermann, D., R. Galeazzi, J.C. Andersen, M. Blanke: Smart sensor based obstacle detection for high-speed unmanned surface vehicle, IFAC-PapersOnLine. 28 (2015) 190-197. open in new tab
  10. IMO: Resolution MSC.252(83) Adoption of the Revised Performance Standards for Integrated Navigation Systems (INS), Imo -Msc. 252 (2007) 1-49. open in new tab
  11. ITTC: Final Report and Recommendations to the 24th ITTC. 24th International Towing Tank Conference, 2005. open in new tab
  12. Jakob, W., C. Blume: Pareto optimization or cascaded weighted sum: A comparison of concepts, Algorithms. 7 (2014) 166-185. open in new tab
  13. Jingsong, Z., W. Price: Automatic collision avoidance systems: Towards 21st century, in: Dep. Sh. Sci., 2008: pp. 1-10.
  14. Kazimierski, W., A. Stateczny: Radar and Automatic Identification System Track Fusion in an Electronic Chart Display and Information System, J. Navig. 68 (2015) 1141-1154. open in new tab
  15. Kazimierski, W., G. Zaniewicz, A. Stateczny: Verification of multiple model neural tracking filter with ship's radar, in: 2012 13th Int. Radar Symp., IEEE, 2012: pp. 549-553. open in new tab
  16. Krata, P., J. Szlapczynska: Ship weather routing optimization with dynamic constraints based on reliable synchronous roll prediction, Ocean Eng. 150 (2018) 124-137. open in new tab
  17. Lazarowska, A.: A new deterministic approach in a decision support system for ship's trajectory planning, Expert Systems with Applications, Volume 71, 2017, Pages 469-478, ISSN 0957-4174 open in new tab
  18. Lee, H.-Y., S.-S. Shin: The Prediction of ship's manoeuvring performance In initial design stage, PRADS Pr. Deisgn Ships Mob. Units. (1998) 666-639. open in new tab
  19. Li, K., K. Deb, X. Yao: R-Metric: Evaluating the Performance of Preference-Based Evolutionary Multi-Objective Optimization Using Reference Points, IEEE Trans. Evol. Comput. 22 (2017) 821-835. open in new tab
  20. Li, W., W. Ma: SIMULATION ON VESSEL INTELLIGENT COLLISION AVOIDANCE, Polish Marit. Res. 23 (2016) 138-143. open in new tab
  21. Lisowski, J.: Optimization-Supported Decision-Making in the Marine Game Environment, in: Mechatron. Syst. Mech. Mater. II, Trans Tech Publications, 2014: pp. 215-222. open in new tab
  22. Lisowski, J.: Analysis of Methods of Determining the Safe Ship Trajectory, TransNav, Int. J. Mar. Navig. Saf. Sea Transp. 10 (2016) 223-228. open in new tab
  23. Man, Y., M. Lundh, T. Porathe, S. MacKinnon: From Desk to Field -Human Factor Issues in Remote Monitoring and Controlling of Autonomous Unmanned Vessels, Procedia Manuf. 3 (2015) 2674-2681. open in new tab
  24. Naeem, W., S.C. Henrique, L. Hu: A Reactive COLREGs- Compliant Navigation Strategy for Autonomous Maritime Navigation, IFAC-PapersOnLine. 49 (2016) 207-213. open in new tab
  25. Olszewski, H., H. Ghaemi: New concept of numerical ship motion modelling for total ship operability analysis by integrating ship and Environment Under One Overall System, Polish Marit. Res. 25 (2018) 36-41. open in new tab
  26. Papanikolaou, A., N. Fournarakis, D. Chroni, S. Liu: Simulation of the Maneuvering Behavior of Ships in Adverse Weather Conditions, 212 (2016) 11-16.
  27. Perera, L.P., J.P. Carvalho, C.. Guedes Soares: Autonomous guidance and navigation based on the COLREGs rules and regulations of collision avoidance, Adv. Sh. Des. Pollut. Prev. (2010) 205-216. open in new tab
  28. Perera, L.P., L. Moreira, F.P. Santos, V. Ferrari, S. Sutulo, C. Guedes Soares: A navigation and control platform for real- time manoeuvring of autonomous ship models, IFAC, 2012. open in new tab
  29. Perera, L.P., C.G. Soares: Weather routing and safe ship handling in the future of shipping, Ocean Eng. 130 (2017) 684-695. open in new tab
  30. Polvara, R., S. Sharma, J. Wan, A. Manning, R. Sutton: Obstacle Avoidance Approaches for Autonomous Navigation of Unmanned Surface Vehicles, J. Navig. (2017) 1-16. open in new tab
  31. Praczyk, T.: Neural anti-collision system for Autonomous Surface Vehicle, Neurocomputing. 149 (2015) 559-572. open in new tab
  32. Stateczny, A.: Neural Manoeuvre Detection of the Tracked Target in ARPA Systems, IFAC Proc. Vol. 34 (2001) 209-214. open in new tab
  33. Szłapczynska, J.: Multi-objective Weather Routing with Customised Criteria and Constraints, J. Navig. 68 (2015) 338-354. open in new tab
  34. Szlapczynski, R.: A new method of planning collision avoidance manoeuvres for multi-target encounter situations, J. Navig. 61 (2008) 307-321. open in new tab
  35. Szlapczynski, R.: Evolutionary planning of safe ship tracks in restricted visibility, J. Navig. 68 (2015) 39-51. open in new tab
  36. Szlapczynski, R., J. Szlapczynska: A Simulative Comparison of Ship Domains and Their Polygonal Approximations, TransNav, Int. J. Mar. Navig. Saf. Sea Transp. 9 (2015) 135-141. open in new tab
  37. Szlapczynski, R., J. Szlapczynska: A Target Information Display for Visualising Collision Avoidance Manoeuvres in Various Visibility Conditions, J. Navig. 68 (2015) 1041-1055. Brought to you by | Gdansk University of Technology open in new tab
  38. Szlapczynski, R., J. Szlapczynska: A method of determining and visualizing safe motion parameters of a ship navigating in restricted waters, Ocean Eng. 129 (2017) 363-373. open in new tab
  39. Tsou, M.C.: Integration of a geographic information system and evolutionary computation for automatic routing in coastal navigation, J. Navig. 63 (2010) 323-341. open in new tab
  40. Tsou, M.C.: Multi-target collision avoidance route planning under an ECDIS framework, Ocean Eng. 121 (2016) 268-278. open in new tab
  41. Utyuzhnikov, S. V., P. Fantini, M.D. Guenov: A method for generating a well-distributed Pareto set in nonlinear multiobjective optimization, J. Comput. Appl. Math. 223 (2009) 820-841. open in new tab
  42. Woerner, K., M.R. Benjamin, M. Novitzky, J.J. Leonard: Quantifying protocol evaluation for autonomous collision avoidance: Toward establishing COLREGS compliance metrics, Auton. Robots. (2018) 1-25. open in new tab
  43. Wrobel, K., P. Krata, J. Montewka, T. Hinz: Towards the Development of a Risk Model for Unmanned Vessels Design and Operations, Int. J. Mar. Navig. Saf. Sea Transp. 10 (2016) 267-274. open in new tab
  44. Wróbel, K., J. Montewka, P. Kujala: Towards the assessment of potential impact of unmanned vessels on maritime transportation safety, Reliab. Eng. Syst. Saf. 165 (2017) 155-169. open in new tab
  45. Zeraatgar, H., M.H. Ghaemi: The Analysis of Overall Ship Fuel Consumption in Acceleration Manoeuvre Using Hull- Propeller-Engine Interaction Principles and Governor Features, Polish Marit. Res. 26 (2019) 162-173. open in new tab
  46. Zhang, Z., C. Lee: Multiobjective Approaches for the Ship Stowage Planning Problem Considering Ship Stability and Container Rehandles, IEEE Trans. Syst. Man, Cybern. Syst. 46 (2016) 1374-1389. open in new tab
  47. Zhao-Lin, W.: Quantification of Action to Avoid Collision, J. Navig. 37 (1984) 420-430. open in new tab
  48. Zhou, K., J. Chen, X. Liu: Optimal Collision-Avoidance Manoeuvres to Minimise Bunker Consumption under the Two-Ship Crossing Situation, J. Navig. (2019) 151-168. open in new tab
  49. Zitzler, E., M. Laumanns, L. Thiele: {SPEA2}: Improving the {S}trength {P}areto {E}volutionary {A}lgorithm, EUROGEN 2001. Evol. Methods Des. Optim. Control with Appl. to Ind. Probl. (2002) 95-100.
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