Improving Autonomous Exploration Using Reduced Approximated Generalized Voronoi Graphs
Improving Autonomous Exploration Using Reduced Approximated Generalized Voronoi Graphs
复制标题
使用简化的近似广义 Voronoi 图改进自主探索
DOI：
10.1007/s10846-019-01119-6
复制
发表时间：
2020-02-20
影响因子：
3.3
通讯作者：
Zhou, Gang
中科院分区：
文献类型：
--
作者：
Li, Lin;Zuo, Xinkai;Zhou, Gang
自主机器人探测在移动的测绘、室内搜索等领域有着广泛的应用。最具挑战性的问题之一是找到下一个最佳视图，并引导机器人通过以前未知的环境。现有的基于广义Voronoi图（GVG）的方法已经提出了可行的解决方案，但是从度量图构造GVG需要大量的计算，并且GVG通常是冗余的。本文提出了一种基于约简近似GVG（RAGVG）的改进方法，该方法用更小的图来表示被探索空间的拓扑结构。此外，提出了一种快速、鲁棒的图像细化算法，用于从度量地图构建RAGVG，并设计了一个使用RAGVG的自主机器人探索框架。所提出的方法进行了验证与三个已知的公共数据集和两个自主探索任务的模拟。实验结果表明，该算法能够有效地构造RAGVG，仿真结果表明，基于RAGVG的探索方法控制的移动的机器人完成给定任务的总时间减少了约20%.
Autonomous robotic exploration has been extensively applied in many tasks, such as mobile mapping and indoor searching. One of the most challenging issues is to locate the Next-Best-View and to guide robots through a previously unknown environment. Existing methods based on generalized Voronoi graphs (GVGs) have presented feasible solutions but require excessive computation to construct GVGs from metric maps, and the GVGs are usually redundant. This paper proposes an improving method based on reduced approximated GVG (RAGVG), which provides a topological representation of the explored space with a smaller graph. Additionally, a fast and robust image thinning algorithm for constructing RAGVGs from metric maps is presented, and an autonomous robotic exploration framework using RAGVGs is designed. The proposed method is validated with three known common data sets and two simulations of autonomous exploration tasks. The experimental results show that the proposed algorithm is efficient in constructing RAGVGs, and the simulations indicate that the mobile robot controlled by the RAGVG-based exploration method reduced the total time by approximately 20% for the given tasks.