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2019 | 26 | 2 |

Tytuł artykułu

Method of emergency collision avoidance for unmanned surface vehicle (USV) based on motion ability database

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
The unmanned surface vehicles (USV) are required to perform a dynamic obstacle avoidance during fulfilling a task. This is essential for USV safety in case of an emergency and such action has been proved to be difficult. However, little research has been done in this area. This study proposes an emergency collision avoidance algorithm for unmanned surface vehicles (USVs) based on a motion ability database. The algorithm is aimed to address the inconsistency of the existing algorithm. It is proposed to avoid collision in emergency situations by sharp turning and treating the collision avoidance process as a part of the turning movement of USV. In addition, the rolling safety and effect of speed reduction during the collision avoidance process are considered. First, a USV motion ability database is established by numerical simulation. The database includes maximum rolling angle, velocity vector, position scalar, and steering time data during the turning process. In emergency collision avoidance planning, the expected steering angle is obtained based on the International Regulations for Preventing Collisions at Sea (COLREGs), and the solution space, with initial velocity and rudder angle taken as independent variables, is determined by combining the steering time and rolling angle data. On the basis of this solution space, the objective function is solved by the particle swarm optimization (PSO) algorithm, and the optimal initial velocity and rudder angle are obtained. The position data corresponding to this solution is the emergency collision avoidance trajectory. Then, the collision avoidance parameters were calculated based on the afore mentioned model of motion. With the use of MATLAB and Unity software, a semi-physical simulation platform was established to perform the avoidance simulation experiment under emergency situation. Results show the validity of the algorithm. Hence results of this research can be useful for performing intelligent collision avoidance operations of USV and other autonomous ships

Słowa kluczowe

Wydawca

-

Rocznik

Tom

26

Numer

2

Opis fizyczny

p.55-67,fig.,ref.

Twórcy

autor
  • Key Laboratory of High Performance Ship Technology, Wuhan University of Technology, Ministry of Education, Heping Avenue, 430063 Wuhan, China
  • School of Transportation, Wuhan Unioversity of Technology, Heping Avenue, 430063 Wuhan, China
autor
  • Key Laboratory of High Performance Ship Technology, Wuhan University of Technology, Ministry of Education, Heping Avenue, 430063 Wuhan, China
  • China Ship Development and Design Center, Zhangzhidong Road, Wuhan, China
autor
  • School of Transportation, Wuhan Unioversity of Technology, Heping Avenue, 430063 Wuhan, China
autor
  • Key Laboratory of High Performance Ship Technology, Wuhan University of Technology, Ministry of Education, Heping Avenue, 430063 Wuhan, China
  • School of Transportation, Wuhan Unioversity of Technology, Heping Avenue, 430063 Wuhan, China
autor
  • University of Southern California, University Park Los Angeles, 740-2311 Los Angeles, USA
autor
  • School of Transportation, Wuhan Unioversity of Technology, Heping Avenue, 430063 Wuhan, China
autor
  • China Ship Development and Design Center, Zhangzhidong Road, Wuhan, China

Bibliografia

  • 1. Campbell S., Naeem W., Irwin G.W.: A review on improving the autonomy of unmanned surface vehicles through intelligent collision avoidance manoeuvre. Annu. Rev. Control 2012, 36(23): pp. 267–283.
  • 2. Simetti E., Turetta A., Casalino G.: Towards the Use of a Team of USVs for Civilian Harbour Protection: the Problem of Intercepting Detected Menaces, OCEANS 2010 IEEE – Sydney.
  • 3. U.S.Dept. Homeland Security/U.S. Coast Guard; Navigation rules. Paradise Cay Publications, 2010.
  • 4. Larson J., Bruch M., Halterman R., Rogers J., Webster R.: Advances in Autonomous Obstacle Avoidance for Unmanned Surface Vehicles. Space and Naval Warfare Systems Center, San Diego, CA., 2007.
  • 5. Colito J.: Autonomous mission planning and execution for unmanned surface vehicles in compliance with the marine rules of the road, M.S. thesis, Dept. Aeronaut. Astronaut., Univ. Washington, Seattle, WA, US., 2007.
  • 6. Choi, S., Yu, W.: Any-angle path planning on non-uniform costmaps. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2011: pp. 5615–5621.
  • 7. Kim H., Park B., Myung H.: Curvature path planning with high resolution graph for unmanned surface vehicle. In: Proceedings of the Robot Intelligence Technology and Applications (RiTA), 2012: pp. 147–154
  • 8. Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. Int. J. Robot. Res. 1986, 5: pp. 90–98.
  • 9. Wang J., Wu X., Xu Z.: Potential-based obstacle avoidance in formation control. J. Control Theory Appl. 2008, 6: pp. 311–316.
  • 10. Ge S., Cui Y.: Dynamic motion planning for mobile robots using potential field method. Auton. Robots 2007, 13: pp. 207–222.
  • 11. Fiorini P., Shiller Z.: Motion planning in dynamic environments using velocity obstalces, Int. J. Robot, Res. 1998, 17(7): pp. 760–772.
  • 12. Berg J., Lin M., and Manocha D.: Reciprocal velocity obstacles for real-time multi-agent navigation, Proc. IEEE Int. Conf. Robot. Autom., 2008: pp. 1928–1935.
  • 13. Kluge B., 2004 Parssler: Reflective navigation: Individual behaviors and group behaviors, Proc. IEEE Int. Conf. Robot. Autom., 2004, 4: pp. 4172–4177.
  • 14. Berg J., Patil S., Sewall J., Manocha D., and Lin M. C.: Interactive navigation of multiple agents in crowded environments, Proc. Symp. Interactive 3D Graphics Games, 2004, pp. 139–147.
  • 15. Zhao Y.X., Li W., Shi P.: A real-time collision avoidance learning system for Unmanned Surface Vessels. Neurocomputing, 2016, 182: pp. 255–266.
  • 16. Li W. F., Ma W. Y.: Simulation on Vessel Intelligent Collision Avoidance Based on Artificial Fish Swarm Algorithm. Polish Maritime Research, 2016, 23: pp. 138–143.
  • 17. Lazarowska A.: Swarm Intelligence Approach to Safe Ship Control. Polish Maritime Research, 2015, 22(4): pp. 34–40
  • 18. Soulignac M.: Feasible and optimal path planning in strong current fields. IEEE Trans. Robot. 2011, 27: pp. 89–98.
  • 19. Isern G., Hernández, S. D., Fernández P. E., Cabrera G., Dominguez B., Prieto M. V.: Path planning for underwater gliders using iterative optimisation. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2011: pp. 1538–1543.
  • 20. Yang Y., Wang S., Wu Z., Wang Y.: Motion planning for multi-HUG formation in an environment with obstacles. Ocean Engineering. 2011, 38: pp. 2262–2269.
  • 21. Song L.F., Su Y.R., Dong Z.P.: A two-level dynamic obstacle avoidance algorithm for unmanned surface vehicles, Ocean Engineering, 2018, 170: pp. 351–360.
  • 22. Song L.F., Chen Z., Xiang Z.Q.: Error Mitigation Algorithm based on Bidirectional Fitting Method for Collision Avoidance of Unmanned Surface Vehicle, Polish Maritime Research, 2018.

Typ dokumentu

Bibliografia

Identyfikatory

Identyfikator YADDA

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