With the rapid development of the water transportation, ships tend to be more large-scale and intelligent, and ships navigable waters becomes more and more complicated. It leads to ship's navigation is difficult in complex water area. The path planning of complex waters becomes increasingly outstanding. As a kind of complex water, group of bridge-building has been formed in many waterways, such as the Yangtze River and Pearl River, which resulted in collision between ship and bridge abutment increased, made the navigable waters complicated. This paper selected multi-bridges water area as the model environment, and established path planning mathematical models. The model was solved via the Hybrid based on Particle Swarm Optimization algorithm to get the planning path in the given environment. Considering the application of path in nautical practice, this paper put forward an optimization approach by controlling the difference of course between 2 adjacent segments of the path. Through calculating the inclination angle difference of relevant lines obtain the difference of course, combined with the passage planning experience find the appropriate inclination angle difference threshold to eliminate redundant path nodes. The simulation result shows that the optimization can effectively reduce the way-points of the path, avoid frequent course changing for ships, get the way-points and courses, and the result of route planning can be applied to nautical practice directly, which makes the route planning more practical significance.
The prosperity of the world trade economic has stimulated the development of water transportation with a high speed, ships tend to be more large-scale and intelligent, and ships navigable waters becomes more and more complicated. It leads to ship's navigation is difficult in complex water area. Therefore, study on ship path planning in complex water area has become the focus of research. In recent years, Bionic intelligent computing has made many breakthroughs in both theories and application, which has greatly promoted the development of related fields. The bionic intelligent algorithm has the characteristics of self-adaptation, self-organization and self-learning, and it can solve the non-linear complex problems that are difficult to solve in traditional calculation. Particle swarm optimization (PSO) and Ant colony algorithm (ACO) are widely used in unmanned aerial vehicle, robot and missile route planning (Chen, 2015; Jun, 2015; Lee, 2012; Ma, 2013; MOSCATO.P, 1989; Wu, 2014). There are many research methodologies for route planning, but the results of planning need to be further processed before they can be applied to navigation practice. So, it is necessary to optimize the existing algorithms from the perspective of navigation practice. In this paper, the PSO algorithm based on natural selection is used to solve the problem of the route planning of the group bridge waters, and the planning route is optimized from the perspective of navigation practice.