基于蚁群优化的AUV全局路径规划研究(英文)

Research on global path planning based on ant colony optimization for AUV

路径规划是自主式水下潜器(AUV)导航研究的重要课题,AUV可用于未知环境如海洋空间探测.在大范围海洋环境中,应用蚁群优化原理对自主式水下潜器的全局路径规划问题进行了研究.引入栅格建模方法建立了蚁群可视图模型,设计了蚁群信息素更新规则;给出了蚁群全局路径规划的操作步骤;针对蚁群规划路径不平滑问题,设计了切割算子和插点算子.仿真实验结果表明,蚁群全局规划算法非常适合于求解复杂环境中的规划问题,规划时间短、路径平滑,其原型系统可应用于非结构化无人环境监测。

Path planning is an important issue for autonomous underwater vehicles （AUVs） traversing an unknown environment such as a sea floor, a jungle, or the outer celestial planets. For this paper, global path planning using large-scale chart data was studied, and the principles of ant colony optimization （ACO） were applied. This paper introduced the idea of a visibility graph based on the grid workspace model. It also brought a series of pheromone updating rules for the ACO planning algorithm. The operational steps of the ACO algorithm are proposed as a model for a global path planning method for AUV. To mimic the process of smoothing a planned path, a cutting operator and an insertion-point operator were designed. Simulation results demonstrated that the ACO algorithm is suitable for global path planning. The system has many advantages, including that the operating path of the AUV can be quickly optimized, and it is shorter, safer, and smoother. The prototype system successfully demonstrated the feasibility of the concept, proving it can be applied to surveys of unstructured unmanned environments.