| A sampling-based multi-tree fusion algorithm for frontier detection | |
Qiao, Wenchuan; Fang Z(方正); Si BL(斯白露)
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| Department | 机器人学研究室 |
| Source Publication | INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS
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| ISSN | 1729-8814 |
| 2019 | |
| Volume | 16Issue:4Pages:1-14 |
| Indexed By | SCI ; EI |
| EI Accession number | 20193607406703 |
| WOS ID | WOS:000481806100001 |
| Contribution Rank | 1 |
| Funding Organization | National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities ; Natural Science Foundation of Liaoning Province ; State Key Laboratory of Robotics, China |
| Keyword | Exploration frontier-based rapidly-exploring random tree |
| Abstract | Autonomous exploration is a key step toward real robotic autonomy. Among various approaches for autonomous exploration, frontier-based methods are most commonly used. One efficient method of frontier detection exploits the idea of the rapidly-exploring random tree and uses tree edges to search for frontiers. However, this method usually needs to consume a lot of memory resources and searches for frontiers slowly in the environments where random trees are not easy to grow (unfavorable environments). In this article, a sampling-based multi-tree fusion algorithm for frontier detection is proposed. Firstly, the random tree's growing and storage rules are changed so that the disadvantage of its slow growing under unfavorable environments is overcome. Secondly, a block structure is proposed to judge whether tree nodes in a block play a decisive role in frontier detection, so that a large number of redundant tree nodes can be deleted. Finally, two random trees with different growing rules are fused to speed up frontier detection. Experimental results in both simulated and real environments demonstrate that our algorithm for frontier detection consumes fewer memory resources and shows better performances in unfavorable environments. |
| Language | 英语 |
| WOS Subject | Robotics |
| WOS Keyword | STRATEGIES |
| WOS Research Area | Robotics |
| Funding Project | National Natural Science Foundation of China[61573091] ; Fundamental Research Funds for the Central Universities[N182608003] ; Fundamental Research Funds for the Central Universities[N172608005] ; Natural Science Foundation of Liaoning Province[20180520006] ; State Key Laboratory of Robotics, China[2018-O08] ; National Natural Science Foundation of China[61573091] ; Fundamental Research Funds for the Central Universities[N182608003] ; Fundamental Research Funds for the Central Universities[N172608005] ; Natural Science Foundation of Liaoning Province[20180520006] ; State Key Laboratory of Robotics, China[2018-O08] ; National Natural Science Foundation of China[61573091] ; Fundamental Research Funds for the Central Universities[N182608003] ; Fundamental Research Funds for the Central Universities[N172608005] ; Natural Science Foundation of Liaoning Province[20180520006] ; State Key Laboratory of Robotics, China[2018-O08] |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.sia.cn/handle/173321/25481 |
| Collection | 机器人学研究室 |
| Corresponding Author | Fang Z(方正) |
| Affiliation | 1.Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, China 2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China |
| Recommended Citation GB/T 7714 | Qiao, Wenchuan,Fang Z,Si BL. A sampling-based multi-tree fusion algorithm for frontier detection[J]. INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS,2019,16(4):1-14. |
| APA | Qiao, Wenchuan,Fang Z,&Si BL.(2019).A sampling-based multi-tree fusion algorithm for frontier detection.INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS,16(4),1-14. |
| MLA | Qiao, Wenchuan,et al."A sampling-based multi-tree fusion algorithm for frontier detection".INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 16.4(2019):1-14. |
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| A sampling-based mul（1780KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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