应用粒子群优化算法改进无人船航行路径规划研究  

On Improvement of Path Planning of Unmanned Ship Using Particle Swarm Optimization Algorithm

作　　者：高炳[1,2] 王林 张少明 陈锦濠[1] GAO Bing;WANG Lin;ZHANG Shaoming;CHEN Jinhao(Maritime College,Guangdong Communication Polytechnic,Guangzhou 510800,China;Guangdong Ship Automation Engineering Technology Research Center,Guangzhou 510800,China)

机构地区：[1]广东交通职业技术学院海事学院,广州510800 [2]广东省船舶自动化工程技术研究中心,广州510800

出　　处：《船舶》2022年第6期47-54,共8页Ship & Boat

基　　金：广东大学生科技创新培育专项资金资助项目(pdjh2021b0776);广东省普通高校重点科研项目重点领域专项(2022ZDZX3060);广东省普通高校特色创新类科研项目(2019GKTSCX034)。

摘　　要：在已开发的FR无人船的设计与实现背景基础上,针对其航线规划使用传统算法时易于困处局部最优、抗干扰适应性不够和控制精度不高等问题,应用粒子群优化算法来改进求解更优航迹。通过在粒子初始化时添加适应度函数加以修正改进,使路径种群在初始化有较高的有效性,从而增进算法的整体收敛速度和航迹跟踪精度。研究完成模型构建、算法优化、模拟仿真计算和试验验证。计算及试验结果显示,应用改进粒子群优化算法在复杂环境中,FR无人船能规避水面障碍物,逃离局部最小值点,准确到达设定点,收敛速度更快,满足及时响应的需要。On the basis of the design and implementation background of the developed FR unmanned ship,the particle swarm optimization(PSO)algorithm is used to improve the solution of optimal path to address the problems of local optimization,insufficient anti-interference adaptability and low control accuracy when the traditional algorithm was used in the path planning.The path population becomes more effective in initialization by adding fitness function during the particle initialization,improving the overall convergence speed and path tracking accuracy of the algorithm.The model establishment,algorithm optimization,simulation calculation and experimental verification are carried out.The results show that the FR unmanned ship in the complex environment can avoid obstacles on the water surface,escape from the local minimum point,reach the set point accurately,convergence faster and meet the needs of timely response by adopting the improved PSO algorithm.

关 键 词：无人船 路径规划 模型构建 粒子群算法 

分 类 号：U665.261[交通运输工程—船舶及航道工程]