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Title: | A robust extended H∞ filtering approach to multi-robot cooperative localization in dynamic indoor environments |
Other Titles: | A robust extended H-infinity filtering approach to multi-robot cooperative localization in dynamic indoor environments |
Authors: | Zhuang, Y Wang, Z Yu, H Wang, W Lauria, S |
Keywords: | multi-robot cooperative localization;robust extended H∞ filtering (REHF);metric-based Iterative Closest Point (MbICP);laser data interaction |
Issue Date: | 17-Apr-2013 |
Publisher: | Elsevier |
Citation: | Control Engineering Practice, 21(7): 953 - 961, Jul 2013 |
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. |
URI: | https://bura.brunel.ac.uk/handle/2438/7367 |
DOI: | https://doi.org/10.1016/j.conengprac.2013.02.006 |
ISSN: | 0967-0661 |
Appears in Collections: | Publications Computer Science Dept of Computer Science Research Papers |
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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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