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COLREGS-Constrained Real-time Path Planning for Autonomous Ships Using Modified Artificial Potential Fields

Published online by Cambridge University Press:  16 November 2018

Hongguang Lyu  and
Yong Yin
Hongguang Lyu*
Affiliation:
(Collaborative Innovation Research Institute of Autonomous Ship @ Dalian Maritime University, Dalian Maritime University, Dalian 116026, China) (Navigation College, Dalian Maritime University, Dalian 116026, China)
Yong Yin
Affiliation:
(Navigation College, Dalian Maritime University, Dalian 116026, China)
*
(E-mail: lhg@dlmu.edu.cn)
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Abstract

This paper presents a real-time and deterministic path planning method for autonomous ships or Unmanned Surface Vehicles (USV) in complex and dynamic navigation environments. A modified Artificial Potential Field (APF), which contains a new modified repulsion potential field function and the corresponding virtual forces, is developed to address the issue of Collision Avoidance (CA) with dynamic targets and static obstacles, including emergency situations. Appropriate functional and safety requirements are added in the corresponding virtual forces to ensure International Regulations for Preventing Collisions at Sea (COLREGS)-constrained behaviour for the own ship's CA actions. Simulations show that the method is fast, effective and deterministic for path planning in complex situations with multiple moving target ships and stationary obstacles and can account for the unpredictable strategies of other ships. The authors believe that automatic navigation systems operated without human interaction could benefit from the development of path planning algorithms.

Keywords

AutonomousCOLREGsRoute PlanningShip Collision
Type
Research Article
Information
The Journal of Navigation , Volume 72 , Issue 3 , May 2019 , pp. 588 - 608
Copyright
Copyright © The Royal Institute of Navigation 2018 

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References

REFERENCES

Brcko, T. and Svetak, J. (2013). Fuzzy reasoning as a base for collision avoidance decision support system. Promet–Traffic&Transportation, 25(6), 555–564.Google Scholar
Campbell, S., Naeem, W. and Irwin, G. (2012). A review on improving the autonomy of unmanned surface vehicles through intelligent collision avoidance manoeuvres. Annual Reviews in Control, 36(2), 267–283.Google Scholar
Fahimi, F. (2007). Non-linear model predictive formation control for groups of autonomous surface vessels. International Journal of Control, 80(8), 1248–1259.Google Scholar
Ge, S. S. and Cui, Y. J. (2002). Dynamic motion planning for mobile robots using potential field method. Autonomous Robots, 13(3), 207–222.Google Scholar
International Maritime Organization (IMO). (2017). Scoping exercise on autonomous vessels put on agenda.http://www.imo.org., Accessed 17 April 2018.Google Scholar
International Maritime Organization (IMO). (1972). [with amendments adopted from December 2009], Convention on the international regulations for preventing collisions at sea.Google Scholar
Johansen, T. A., Perez, T. and Cristofaro, A. (2016). Ship collision avoidance and COLREGS compliance using simulation-based control behavior selection with predictive hazard assessment. IEEE Transactions on Intelligent Transportation Systems, 17(12), 3407–3422.Google Scholar
Kuwata, Y., Wolf, M., Zarzhitsky, D. and Huntsberger, T. (2014). Safe maritime autonomous navigation with COLREGS, using velocity obstacles. IEEE Journal of Oceanic Engineering, 39(1), 110–119.Google Scholar
Lazarowska, A. (2015). Ship's trajectory planning for collision avoidance at sea based on ant colony optimisation. The Journal of Navigation, 68, 291–307.Google Scholar
Lazarowska, A. (2017). A new deterministic approach in a decision support system for ship's trajectory planning. Expert Systems with Applications, 71, 469–478.Google Scholar
Lee, S., Kwon, K. 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
Lyu, H. and Yin, Y. (2017). Ship's trajectory planning for collision avoidance at sea based on modified artificial potential field. In 2017 2nd International Conference on Robotics and Automation Engineering (ICRAE), 351–357.Google Scholar
Perera, L. P., Carvalho, J. P. and Soares, C. G. (2011). Fuzzy logic based decision making system for collision avoidance of ocean navigation under critical collision conditions. Journal of Marine Science and technology, 16(1), 84–99.Google Scholar
Perera, L. P., Carvalho, J. P. and Soares, C. G. (2012). Intelligent ocean navigation and fuzzy-Bayesian decision/action formulation. IEEE Journal of Oceanic Engineering, 37(2), 204–219.Google Scholar
Shah, B. C., Svec, P., Bertaska, I. R. and Klinger, W. (2014). Trajectory planning with adaptive control primitives for autonomous surface vehicles operating in congested civilian traffic. In IEEE/RSI International Conference on Intelligent Robots and Systems, 2312–2318.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(3), 227–241.Google Scholar
Szlapczynski, R. (2011). Evolutionary sets of safe ship trajectories a new approach to collision avoidance. The Journal of Navigation, 64, 169–181.Google Scholar
Szlapczynski, R. (2015). Evolutionary planning of safe ship tracks in restricted visibility. The Journal of Navigation, 68(1), 39–51.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
Tam, C. and Bucknall, R. (2013). Cooperative path planning algorithm for marine surface vessels. Ocean Engineering, 57, 25–33.Google Scholar
Wilson, P., Harris, C. and Hong, X. (2003). A line of sight counteraction navigation algorithm for ship encounter collision avoidance. The Journal of Navigation, 56(1), 111–121.Google Scholar
Woerner, K. (2016). Multi-Contact Protocol-Constrained Collision Avoidance for Autonomous Marine Vehicles. PhD thesis, Massachusetts Institute of Technology.Google Scholar
Woerner, K. and Novitzky, M. (2017). Legibility and predictability of protocol-constrained motion: Evaluating human-robot ship interactions under COLREGS collision avoidance requirements. Robotics Science and Systems.Google Scholar
Woerner, K. L., Benjamin, M. R., Novitzky, M. and Leonard, J. J. (2016). Collision avoidance road test for COLREGS-constrained autonomous vehicles. In OCEANS 2016 MTS/IEEE Monterey, 1–6.Google Scholar
Xue, Y., Clelland, D., Lee, B. and Han, D. (2011). Automatic simulation of ship navigation. Ocean Engineering, 38, 2290–2305.Google Scholar
Zeng, X. (2003). Evolution of the safe path for ship navigation. Applied Artificial Intelligence, 17(2), 87–104.Google Scholar
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