Sample-Based Frontier Detection for Autonomous Robot Exploration
Sample-Based Frontier Detection for Autonomous Robot Exploration
复制标题
用于自主机器人探索的基于样本的前沿检测
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Bailu Si
中科院分区：
文献类型：
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作者：
Wenchuan Qiao;Z. Fang;Bailu Si
基于边界的方法是机器人探测中最常用的方法。一种流行的前沿搜索方法是利用快速探索随机树的思想，并使用树的生长边来搜索前沿。与传统的基于图像处理的方法相比，它可以更有效地应用于高维空间的探测。但是这种方法通常需要占用大量的存储资源，并且在随机树不易生长的环境（不利环境）中搜索边界速度慢。提出了一种基于样本的边界检测算法。首先，通过改变随机树的生长规律和存储方式，克服了树在不利环境下生长缓慢的缺点。其次，我们将地图分成块，用于删除冗余的树节点在探索，以减少所需的计算资源。为了评估所提出的前沿检测算法，已经建立了两种不同的仿真环境。实验结果表明，该算法大大节省了内存资源，在恶劣环境下也有较好的性能。
Frontier-based method is most commonly used in robotic exploration. One popular frontier searching method is to exploit the idea of rapidly-exploring random tree and to use the grown edges of the tree to search for frontiers. Compared to traditional methods based on image processing, it can be applied to high-dimensional exploration more efficiently. However, this method usually needs to occupy a large number of storage resources and searches for frontiers slowly in the environment where random trees are not easy to grow (unfavorable environment). In this paper, a sample-based frontier detection algorithm (SFD) is proposed. Firstly, by changing the growth rule and the storage mode of the random tree, the disadvantage of slow growth of the tree under unfavorable environments is overcome. Secondly, we divide the map into blocks which are used to delete redundant tree nodes during the exploration to reduce required computation resources. In order to evaluate the proposed frontier detection algorithm, two different kind of simulation environments have been set up. The experimental results show that our algorithm saves the memory resource greatly and shows better performances in unfavorable environments.