Research On Path Planning Of Warship Navigation Based On Information Entropy-Genetic Algorithm

As technology progresses, the ship navigation are gradually moving towards the direction of the development of intelligent and path planning is one of the key technologies of it. In the background of path planning of ship and on the platform of electronic chart, the paper makes the thorough research in path planning for ship sailing in the real marine environment. Information entropy-genetic algorithm which applied to the field of path planning is proposed based on the integration of information entropy and genetic arithmetic and the characteristics of path planning. The defects of traditional genetic algorithm in path planning, time-consuming and falling into local solutions are overcome.For the characteristics of the display of electronic chart, this paper adopts polygonal obstacles and path-point coding presentation, and genetic algorithm as the basic algorithm. To describe the diversity of population, a concept of path population entropy is proposed based on grid concept and focusing research on the information entropy principle and realize the diversity of population measurement. On this basis, the paper further proposed the dual -wheeled-gambling selection strategy based on path population entropy and given the adaptive formula of variation rate and cross rate based on path population entropy, reaches purpose of maintaining diversity of population in the early part of algorithm and speeding up the convergence of the algorithm in the latter part of algorithm. A method of createing initial population which optimizeing path after creating path is proposed and the quality of the initial population is improved. To reduce the detection times, a region-based collision detection method is proposed and the efficiency of the algorithm is improved.Finally, the use of algorithms designed in this paper a path planning simulation, has been the ideal outcome, validate the effectiveness of the algorithm. At the same time with the original self-adaptive genetic algorithm simulation comparison, through the difference between the actual results validate the superiority of the algorithm.