Distributed Finite Memory Estimation From Relative Measurements for Multiple-Robot Localization in Wireless Sensor Networks Article Swipe

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Yeong Jun Kim , Hyun Ho Kang , Sang Su Lee , Jung Min Pak , Choon Ki Ahn ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.1109/access.2022.3141492

Mobile robot localizations have been extensively studied, and various algorithms for multiple-robot localization have been developed. However, existing methods for multiple-robot localization often exhibit poor performance under harsh conditions, such as missing measurements and sudden appearance of obstacles. To overcome this problem, this paper proposes a novel method for multiple-robot localization in wireless sensor networks. The proposed method is theoretically based on the finite memory estimation and utilizes relative distance and angle measurements between robots. Thus, the proposed method is referred to as distributed finite memory estimation from relative measurements (DFMERM). Due to the finite memory structure, the DFMERM has inherent robustness against computational and modeling errors. Moreover, the novel distributed localization method using relative measurements shows the robustness against missing measurements. Robust DFMERM localization performance is experimentally demonstrated using multiple mobile robots under the harsh conditions.

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Simultaneous Localization And Mapping

Artificial Intelligence

Biochemistry

Chemistry

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Robustness (evolution) Mobile robot Robot Computer science Wireless sensor network Wireless Simultaneous localization and mapping Real-time computing Artificial intelligence Computer network Biochemistry Gene Telecommunications Chemistry

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Type: article
Language: en
Landing Page: https://doi.org/10.1109/access.2022.3141492
PDF: https://ieeexplore.ieee.org/ielx7/6287639/9668973/09674905.pdf
OA Status: gold
Cited By: 7
References: 37
Related Works: 10
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DOI: https://doi.org/10.1109/access.2022.3141492

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Title: Distributed Finite Memory Estimation From Relative Measurements for Multiple-Robot Localization in Wireless Sensor Networks

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Type: article

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Language: en

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Publication year: 2022

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Publication date: 2022-01-01

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Authors: Yeong Jun Kim, Hyun Ho Kang, Sang Su Lee, Jung Min Pak, Choon Ki Ahn

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Landing page: https://doi.org/10.1109/access.2022.3141492

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Concepts: Robustness (evolution), Mobile robot, Robot, Computer science, Wireless sensor network, Wireless, Simultaneous localization and mapping, Real-time computing, Artificial intelligence, Computer network, Biochemistry, Gene, Telecommunications, Chemistry

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Cited by: 7

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