.png){kind=icon}
返回
Distributed and resilient localization algorithm for Swarm Robotic Systems
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2016.07.049.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Many applications of Swarm Robotic Systems (SRSs) require each robot to be able to discover its own position. To provide such capability, some localization methods have been proposed, in which the positions of the robots are estimated based on a set of reference nodes in the swarm. In this paper, a distributed and resilient localization algorithm is proposed based on the BSAMMA algorithm, which uses the Backtracking Search Algorithm (BSA) and the Min-Max Area (MMA) confidence factor. It is designed in a novel four-stage approach, where a new method, called Multi-hop Collaborative Min-Max Localization (MCMM), is included to improve the resilience in case of failures during the recognition of the reference nodes. The results, obtained with real Kilobot robots, show 2836% of performance improvement obtained by the MCMM. Also, it is shown that the final result of the localization process is better when the MCMM is executed than if it is not executed. The experiments outcomes demonstrate that the novel four-stage approach and the use of the MCMM algorithm represents a progress in the design of distributed localization algorithms for SRS, especially with regard to its resilience. (C) 2016 Elsevier B.V. All rights reserved.
Keyword:
Localization
Swarm robotics
Swarm intelligence
BSA
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
机构
引用论文
Distributed efficient localization in swarm robotic systems using swarm intelligence algorithms.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
查看更多内容