希尔伯特曲线

本文研究了复杂未知环境下多机器人地图构建的问题，首先，提出了一种基于粒子群优化算法进行全局优化，以及对目标区域进行希尔伯特曲线探测的多机器人协作构建地图的方法。

In this paper , firstly , multi-robot map building problem in a complex and unknown environment is investigated , presents a map building approach based on particle swarm optimization algorithm for global optimization , as well as Hilbert curve on the target region detection of multi-robot collaborative .

设计并实现了一个简单的基于自适应准希尔伯特曲线的实验检索系统。

Design and realize a simple retrieval system based on ASHC which has a friendly interface between computer and users .

将基于自适应准希尔伯特曲线的检索方法和基于全局颜色直方图的检索方法进行了比较，两者所花费的检索时间相差无几，但前者的检索效果比后者有很大的提高。

Experiments have been done to compare the retrieval method based on ASHC ( ASHC-RM ) and the retrieval method based on Global Color Histogram ( GCH-RM ) . ASHC-RM has a better retrieval results than GCH-RM .

在矢量数据查询方面，本文设计了基于希尔伯特空间填充曲线的矢量数据查询算法，仿真结果表明，该算法能够降低矢量数据查询的路由跳数及响应时延。

On the vector data query algorithms , a vector data query algorithm based on the Hilbert space filling curve is designed . The simulation result shows that the algorithm can reduce routing hops and shorten response delay on the vector data query .