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；
This paper presents a global path-planning algorithm of mobile robot under uncertain environment.
本文提出了在不确定的环境中，移动机器人的一种全局路径规划算法。
Thirdly, basing on characteristics of mobile robot path planning, we designs a kind of Election-survey Algorithm to solve global optimal result of mobile robot path planning.
再次，根据移动机器人路径规划问题的特性，设计出一种新的基于竞选算法的移动机器人全局最优路径规划方法。
It is difficult to get the global geography information without expensive device. All our experiments are based local path planning algorithm.
由于硬件条件的限制，使用家庭安防机器人采集全局地理信息难以实现。
It is difficult to get the global geography information without expensive device. All our experiments are based local path planning algorithm.
由于硬件条件的限制，使用家庭安防机器人采集全局地理信息难以实现。
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