Sample-Based Frontier Detection for Autonomous Robot Exploration
文献类型：会议论文
| 作者 | Qiao, Wenchuan2; Fang Z(方正)2; Si BL(斯白露)1
|
| 出版日期 | 2018 |
| 会议日期 | December 12-15, 2018 |
| 会议地点 | Kuala Lumpur, Malaysia |
| 页码 | 1165-1170 |
| 英文摘要 | 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. |
| 产权排序 | 2 |
| 会议录 | Proceedings of the 2018 IEEE International Conference on Robotics and Biomimetics
 |
| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISBN号 | 978-1-7281-0376-1 |
| WOS记录号 | WOS:000468772200186 |
| 源URL | [http://ir.sia.cn/handle/173321/24651]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 通讯作者 | Fang Z(方正) |
| 作者单位 | 1.Chinese Academy of Sciences, State Key Laboratory of Robotics, Shenyang Institute of Automation, China 2.Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China |
| 推荐引用方式 GB/T 7714 | Qiao, Wenchuan,Fang Z,Si BL. Sample-Based Frontier Detection for Autonomous Robot Exploration[C]. 见:. Kuala Lumpur, Malaysia. December 12-15, 2018. |
入库方式： OAI收割
来源：沈阳自动化研究所
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