Ship collision accidents not only endanger the safety of ships and personnel, but also may cause serious marine environmental pollution. To solve this problem, advanced technologies have been developed and applied in the field of intelligent ships in recent years. In this paper, a novel path planning algorithm is proposed based on particle swarm optimization (PSO) to construct a decision-making system for ship's autonomous collision avoidance using the process analysis which combines with the ship encounter situation and the decision-making method based on ship collision avoidance responsibility. This algorithm is designed to avoid both static and dynamic obstacles by judging the collision risk considering bad weather conditions by using BP neural network. When the two ships enter a certain distance, the optimal collision avoidance course and speed of the ship are obtained through the improved collision avoidance decision-making method. Finally, through MATLAB and Visual C++ platform simulations, the results show that the ship collision avoidance decision-making scheme can obtain reasonable optimal collision avoidance speed and course, which can ensure the safety of ship path planning and reduce energy consumption.

Abstract
1. Introduction
2. Ship Collision Avoidance Theoretical BasisPreparation
    2.1 Division of ship encountering situation andavoidance responsibility
    2.2 Ship motion parameters
    2.3 The improved PSO algorithm
3.Ship Collision Avoidance Method Based onImproved PSO Algorithm
    3.1 Collision avoidance model of dynamic obstacles
    3.2 Fitness function of dynamic collision avoidance
    3.3 Modeling of obstacles and ships for collisionavoidance in bad weather
4.Simulations
    4.1 Numerical simulation verification
    4.2 Simulation of ship collision avoidance in open waters
    4.3 Simulation of ship collision avoidance path in narrowwater
5. Conclusion
Acknowledgments
References

• Hu Yancai(Navigation and Shipping College, Shandong Jiaotong University)
• Gai Xudong(Navigation and Shipping College, Shandong Jiaotong University)
• Zhang Qiang(Navigation and Shipping College, Shandong Jiaotong University) Corresponding Author
• Liu Yang(Division of Maritime Transportation, Mokpo National Maritime University)