Distributed Finite Memory Estimation From Relative Measurements for Multiple-Robot Localization in Wireless Sensor Networks
- Authors
- Kim, Yeong Jun; Kang, Hyun Ho; Lee, Sang Su; Pak, Jung Min; Ahn, Choon Ki
- Issue Date
- 2022
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Robot sensing systems; Location awareness; Estimation; Mobile robots; Wireless sensor networks; Robustness; Noise measurement; Distributed localization; finite memory estimation; mobile robot; relative measurements; wireless sensor networks
- Citation
- IEEE ACCESS, v.10, pp.5980 - 5989
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE ACCESS
- Volume
- 10
- Start Page
- 5980
- End Page
- 5989
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/136626
- DOI
- 10.1109/ACCESS.2022.3141492
- ISSN
- 2169-3536
- Abstract
- 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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Collections - College of Engineering > School of Electrical Engineering > 1. Journal Articles
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