Gap Navigation Trees for Discovering Unknown Environments

Reem Nasir, Ashraf Elnagar
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引用次数: 6

Abstract

We propose a motion planning gap-based algorithms for mobile robots in an unknown environment for exploration purposes. The results are locally optimal and sufficient to navigate and explore the environment. In contrast with the traditional roadmap-based algorithms, our proposed algorithm is designed to use minimal sensory data instead of costly ones. Therefore, we adopt a dynamic data structure called Gap Navigation Trees (GNT), which keeps track of the depth discontinuities (gaps) of the local environment. It is incrementally constructed as the robot which navigates the environment. Upon exploring the whole environment, the resulting final data structure exemplifies the roadmap required for further processing. To avoid infinite cycles, we propose to use landmarks. Similar to traditional roadmap techniques, the resulting algorithm can serve key applications such as exploration and target finding. The simulation results endorse this conclusion. However, our solution is cost effective, when compared to traditional roadmap systems, which makes it more attractive to use in some applications such as search and rescue in hazardous environments.
用于发现未知环境的间隙导航树
提出了一种基于间隙的移动机器人在未知环境下的运动规划算法。结果是局部最优的,足以导航和探索环境。与传统的基于路线图的算法相比,我们提出的算法旨在使用最小的感官数据而不是昂贵的感官数据。因此,我们采用了一种称为间隙导航树(GNT)的动态数据结构,它可以跟踪局部环境的深度不连续(间隙)。它被逐渐构建为一个在环境中导航的机器人。在探索整个环境之后,得到的最终数据结构举例说明了进一步处理所需的路线图。为了避免无限循环,我们建议使用地标。与传统的路线图技术类似,生成的算法可以服务于勘探和目标查找等关键应用。仿真结果证实了这一结论。然而,与传统的路线图系统相比,我们的解决方案具有成本效益,这使得它在某些应用中更具吸引力,例如在危险环境中的搜索和救援。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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