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2019 | 26 | 4 |

Tytuł artykułu

Framework of an evolutionary multi-objective optimisation method for planning a safe trajectory for a marine autonomous surface ship

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
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 multiobjective 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

Słowa kluczowe

Wydawca

-

Rocznik

Tom

26

Numer

4

Opis fizyczny

p.69-79,fig.,ref.

Twórcy

  • Gdansk University of Technology, 11/12 Narutowicza St., 80-233 Gdansk, Poland
autor
  • Gdansk University of Technology, 11/12 Narutowicza St., 80-233 Gdansk, Poland

Bibliografia

  • 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.
  • 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.
  • 3. Branke, J., T. Kaußler, H. Schmeck: Guidance in evolutionary multi-objective optimization, Adv. Eng. Softw. 32 (2001) 499–507.
  • 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.
  • 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.
  • 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.
  • 7. Cockcroft, A.N., Lameijer J.N.F.: A guide to the collision avoidance rules: international regulations for preventing collisions at sea, Elsevier, 2012.
  • 8. Fossen, T.I.: Handbook of Marine Craft Hydrodynamics and Motion Control, John Wiley & Sons, Ltd, Chichester, UK, 2011.
  • 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.
  • 10. IMO: Resolution MSC.252(83) Adoption of the Revised Performance Standards for Integrated Navigation Systems (INS), Imo - Msc. 252 (2007) 1–49.
  • 11. ITTC: Final Report and Recommendations to the 24th ITTC. 24th International Towing Tank Conference, 2005.
  • 12. Jakob, W., C. Blume: Pareto optimization or cascaded weighted sum: A comparison of concepts, Algorithms. 7 (2014) 166–185.
  • 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.
  • 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.
  • 16. Krata, P., J. Szlapczynska: Ship weather routing optimization with dynamic constraints based on reliable synchronous roll prediction, Ocean Eng. 150 (2018) 124–137.
  • 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
  • 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.
  • 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.
  • 20. Li, W., W. Ma: SIMULATION ON VESSEL INTELLIGENT COLLISION AVOIDANCE, Polish Marit. Res. 23 (2016) 138–143.
  • 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.
  • 22. Lisowski, J.: Analysis of Methods of Determining the Safe Ship Trajectory, TransNav, Int. J. Mar. Navig. Saf. Sea Transp. 10 (2016) 223–228.
  • 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.
  • 24. Naeem, W., S.C. Henrique, L. Hu: A Reactive COLREGsCompliant Navigation Strategy for Autonomous Maritime Navigation, IFAC-PapersOnLine. 49 (2016) 207–213.
  • 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.
  • 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.
  • 28. Perera, L.P., L. Moreira, F.P. Santos, V. Ferrari, S. Sutulo, C. Guedes Soares: A navigation and control platform for realtime manoeuvring of autonomous ship models, IFAC, 2012.
  • 29. Perera, L.P., C.G. Soares: Weather routing and safe ship handling in the future of shipping, Ocean Eng. 130 (2017) 684–695.
  • 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.
  • 31. Praczyk, T.: Neural anti-collision system for Autonomous Surface Vehicle, Neurocomputing. 149 (2015) 559–572.
  • 32. Stateczny, A.: Neural Manoeuvre Detection of the Tracked Target in ARPA Systems, IFAC Proc. Vol. 34 (2001) 209–214.
  • 33. Szłapczynska, J.: Multi-objective Weather Routing with Customised Criteria and Constraints, J. Navig. 68 (2015) 338–354.
  • 34. Szlapczynski, R.: A new method of planning collision avoidance manoeuvres for multi-target encounter situations, J. Navig. 61 (2008) 307-321.
  • 35. Szlapczynski, R.: Evolutionary planning of safe ship tracks in restricted visibility, J. Navig. 68 (2015) 39-51.
  • 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.
  • 37. Szlapczynski, R., J. Szlapczynska: A Target Information Display for Visualising Collision Avoidance Manoeuvres in Various Visibility Conditions, J. Navig. 68 (2015) 1041–1055
  • 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.
  • 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.
  • 40. Tsou, M.C.: Multi-target collision avoidance route planning under an ECDIS framework, Ocean Eng. 121 (2016) 268–278.
  • 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.
  • 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.
  • 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.
  • 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.
  • 45. Zeraatgar, H., M.H. Ghaemi: The Analysis of Overall Ship Fuel Consumption in Acceleration Manoeuvre Using HullPropeller-Engine Interaction Principles and Governor Features, Polish Marit. Res. 26 (2019) 162–173.
  • 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.
  • 47. Zhao-Lin, W.: Quantification of Action to Avoid Collision, J. Navig. 37 (1984) 420–430.
  • 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.
  • 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.

Typ dokumentu

Bibliografia

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Identyfikator YADDA

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