Research On Ship Route Planning Based On Improved Particle Swarm Optimization

Accidents such as collision and grounding may occur during ship navigation,which can lead to serious disaster consequences.Carrying out research on intelligent navigation of ships can reduce the influence of human uncertainties and improve the safety of ship navigation,which has important scientific research significance and engineering application value.The key technical problems in the field of ship safety,path planning and collision avoidance assisted decision-making are the key technical problems in the field of ship intelligent navigation.The existing research has not fully considered the environmental factors and the ship’s own performance constraints,and there is still room for technical improvement,which is a current hot spot in scientific research.In view of the key technical problems of intelligent navigation of ships,the modeling method of ship safety domain is constructed based on Wang&Chin model by analyzing the influence of factors such as shape and contour,scale,speed and heading of structures;the particle swarm algorithm is optimized and improved considering the complex marine environment such as wind,wave and current and the distribution of dense obstacles,and the path planning algorithm based on the environment model is proposed;the numerical simulation of the main technical requirements of the International Regulations for Collision Avoidance at Sea is studied.The numerical simulation method of the main technical requirements of the International Rules for Collision Avoidance was studied,and the collision avoidance assisted decision-making algorithm based on the collision avoidance rules was proposed.The following three main areas of research have been carried out:(1)Research on ship collision avoidance perimeter theory based on navigation state.Based on the Wang&Chin model,the research aims at identifying the risk of ship navigation,analysing the influence of multiple factors such as the main scale,speed and heading of both sides of the collision avoidance on the safety of navigation,proposing an analytical expression of the hazard perimeter and safety perimeter,and constructing a multifaceted collision avoidance perimeter for safe navigation at sea,and comparing the existing Fujii model with the Wang&Chin model under different navigational conditions.The sensitivity of the multivariate collision avoidance perimeter to changes in speed and heading is highlighted by comparing it with the existing Fujii and Wang&Chin models under different navigational conditions.(2)Research on the global path planning algorithm for ships based on environmental models.A multi-objective planning model for path selection is established by improving the particle swarm algorithm considering the influence of waves,navigation energy consumption and path length.The planned paths are smoothed and optimised by a detached ship model.Taking Zhoushan waters as an example,the effectiveness of the improved particle swarm algorithm is verified.The results show that compared with the traditional algorithm,the navigation comfort of the improved particle swarm algorithm is improved by18.49%～24.75%,and the navigation energy consumption is reduced by 0.52%～0.56%.(3)Research on the local path planning algorithm for ships based on environmental model.By considering navigation factors such as channel shape and relative ship motion,and referring to the relevant requirements of the International Regulations for Collision Avoidance at Sea,a ship collision avoidance decision algorithm including emergency collision avoidance and warning collision avoidance was proposed,and the real scenarios of its Qin Line and Dongneng Line in Huangpu River waters were used as examples to verify the effectiveness of the ship collision avoidance decision algorithm.The results show that the ship collision avoidance decision algorithm reduces fuel consumption by 1.88% and time consumption by16.50% compared to the unoptimised route.In order to consider the effects of dynamic and static obstacles in an integrated manner,the traditional particle swarm algorithm is compared with the improved particle swarm algorithm,taking Hong Kong waters as an example.The results show that the improved algorithm improves navigation comfort by 11.53%～15.90%,reduces navigation energy consumption by 14.62%～14.86%,and has dynamic collision avoidance function.