Global path planning problem for autonomous underwater vehicle (AUV) based on large-scale chart data is investigated by using ant colony optimization (in shorts, ACO) algorithm.
在大范围海洋环境中，应用蚁群算法对自主式水下潜器(AUV)的全局路径规划问题进行了研究。
We construct a whole new algorithm for global path planning problem, and realized a lot of numerical experiments. It reveals the efficiency of this method.
我们构造了移动机器人全局路径规划的一种新算法，并进行了大量的数值实验，验证了方法的有效性。
On the basis of the global model, the algorithm of intelligent decision is developed, which is made up of two parts: path planning and velocity planning.
在此基础上开发了智能决策算法，算法包含路径选优和速度规划两部分；
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