自主式水下机器人最优路径规划问题的研究

路径规划是水下机器人实现自主航行的重要环节。根据自主式水下机器人的动力学性质，路径规划的特点以及实现智能行为的要求，采用基于案例的遗传算法，实现了自主式水下机器人最优路径规划。给出该方案的基本框架和算法，在基于案例类比的学习方法中引入模糊多属性综合决策的方法建立决策算子进行案例的匹配，在遗传算法中实际知识的指导，适当地改进遗传算子，加快搜索速度。仿真结果证明该路径规划方法能够取得较好的规划结果，使自主式水下机器人具有了一定的自主导航，自主避障和自主作业的能力。

Research on AUV (Autonomous Underwater Vehicle) optimum path planning

Path planning is an important link of underwater vehicle carrying out autonomous voyage, which marks the working level and ensures the safety of AUV. So far, the main purpose of path planning has been only to avoid obstruction successfully, not to select the shortest path. The genetic algorithm with case-based learning method is applied to realize the AUV path planning. In this paper the mainframe and corresponding arithmetic are given, which includes extracting attribution from case features, case index selection, decision operator, genetic algorithm coding, crossover operator and mutation operator.