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http://bura.brunel.ac.uk/handle/2438/7367
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DC Field | Value | Language |
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dc.contributor.author | Zhuang, Y | - |
dc.contributor.author | Wang, Z | - |
dc.contributor.author | Yu, H | - |
dc.contributor.author | Wang, W | - |
dc.contributor.author | Lauria, S | - |
dc.date.accessioned | 2013-04-22T13:44:08Z | - |
dc.date.available | 2013-04-22T13:44:08Z | - |
dc.date.issued | 2013-04-17 | - |
dc.identifier.citation | Control Engineering Practice, 21(7): 953 - 961, Jul 2013 | en_US |
dc.identifier.issn | 0967-0661 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/7367 | - |
dc.description.abstract | Multi-robot cooperative localization serves as an essential task for a team of mobile robots to work within an unknown environment. Based on the real-time laser scanning data interaction, a robust approach is proposed to obtain optimal multi-robot relative observations using the Metric-based Iterative Closest Point (MbICP) algorithm, which makes it possible to utilize the surrounding environment information directly instead of placing a localization-mark on the robots. To meet the demand of dealing with the inherent non-linearities existing in the multi-robot kinematic models and the relative observations, a robust extended H∞ filtering (REHF) approach is developed for the multi-robot cooperative localization system, which could handle non-Gaussian process and measurement noises with respect to robot navigation in unknown dynamic scenes. Compared with the conventional multi-robot localization system using extended Kalman filtering (EKF) approach, the proposed filtering algorithm is capable of providing superior performance in a dynamic indoor environment with outlier disturbances. Both numerical experiments and experiments conducted for the Pioneer3-DX robots show that the proposed localization scheme is effective in improving both the accuracy and reliability of the performance within a complex environment. | en_US |
dc.description.sponsorship | This work was supported inpart by the National Natural Science Foundation of China under grants 61075094, 61035005 and 61134009. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2013 Elsevier. | - |
dc.subject | multi-robot cooperative localization | en_US |
dc.subject | robust extended H∞ filtering (REHF) | en_US |
dc.subject | metric-based Iterative Closest Point (MbICP) | en_US |
dc.subject | laser data interaction | en_US |
dc.title | A robust extended H∞ filtering approach to multi-robot cooperative localization in dynamic indoor environments | en_US |
dc.title.alternative | A robust extended H-infinity filtering approach to multi-robot cooperative localization in dynamic indoor environments | - |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1016/j.conengprac.2013.02.006 | - |
pubs.organisational-data | /Brunel | - |
pubs.organisational-data | /Brunel/Brunel Active Staff | - |
pubs.organisational-data | /Brunel/Brunel Active Staff/School of Info. Systems, Comp & Maths | - |
pubs.organisational-data | /Brunel/Brunel Active Staff/School of Info. Systems, Comp & Maths/IS and Computing | - |
pubs.organisational-data | /Brunel/University Research Centres and Groups | - |
pubs.organisational-data | /Brunel/University Research Centres and Groups/School of Health Sciences and Social Care - URCs and Groups | - |
pubs.organisational-data | /Brunel/University Research Centres and Groups/School of Health Sciences and Social Care - URCs and Groups/Centre for Systems and Synthetic Biology | - |
pubs.organisational-data | /Brunel/University Research Centres and Groups/School of Information Systems, Computing and Mathematics - URCs and Groups | - |
pubs.organisational-data | /Brunel/University Research Centres and Groups/School of Information Systems, Computing and Mathematics - URCs and Groups/Centre for Information and Knowledge Management | - |
Appears in Collections: | Publications Computer Science Dept of Computer Science Research Papers |
Files in This Item:
File | Description | Size | Format | |
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Fulltext.pdf | This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2013 Elsevier. | 590.64 kB | Adobe PDF | View/Open |
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