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Implementation of extended kalman filter for localization of ambulance robot

Chan-Yun Yang, Hooman Samani, ZIRONG TANG, Chunxu Li Orcid Logo

International Journal of Intelligent Robotics and Applications

Swansea University Authors: ZIRONG TANG, Chunxu Li Orcid Logo

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DOI (Published version): 10.1007/s41315-024-00352-z

Abstract

This paper focuses on the implementation of the Extended Kalman Filter for indoor localization of a semi-autonomous Ambulance Robot system named Ambubot. The system is designed to reduce the response time for lay rescuers to locate an Automated External Defibrillator (AED) during sudden cardiac arre...

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Published in: International Journal of Intelligent Robotics and Applications
ISSN: 2366-5971 2366-598X
Published: Springer Science and Business Media LLC 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa67360
first_indexed 2024-08-12T14:49:33Z
last_indexed 2024-11-25T14:20:01Z
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spelling 2024-09-19T12:24:09.9791167 v2 67360 2024-08-12 Implementation of extended kalman filter for localization of ambulance robot be49378076f647e6dacf0c46dfd3d091 ZIRONG TANG ZIRONG TANG true false e6ed70d02c25b05ab52340312559d684 0000-0001-7851-0260 Chunxu Li Chunxu Li true false 2024-08-12 This paper focuses on the implementation of the Extended Kalman Filter for indoor localization of a semi-autonomous Ambulance Robot system named Ambubot. The system is designed to reduce the response time for lay rescuers to locate an Automated External Defibrillator (AED) during sudden cardiac arrest events. To achieve this objective, the robot is equipped with an AED, and the Extended Kalman Filter is utilized for optimal indoor localization. The filter is implemented using data from the robot’s Inertial Measurement Unit, which comprises 9 Degrees of Freedom. The paper provides an explicit description of the performance of the Extended Kalman Filter in estimating the position of Ambubot, and demonstrates that the proposed approach is effective in accurately determining and estimating the robot’s position in unknown indoor environments. The results suggest that the proposed method is a promising solution for improving survival rates in cardiac arrest cases, and may have potential applications in other fields where accurate indoor localization is required. Journal Article International Journal of Intelligent Robotics and Applications 0 Springer Science and Business Media LLC 2366-5971 2366-598X Extended kalman filter; Localization; Intelligent system; Autonomous robot 25 6 2024 2024-06-25 10.1007/s41315-024-00352-z COLLEGE NANME COLLEGE CODE Swansea University SU Library paid the OA fee (TA Institutional Deal) Swansea University 2024-09-19T12:24:09.9791167 2024-08-12T15:46:30.5907008 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Chan-Yun Yang 1 Hooman Samani 2 ZIRONG TANG 3 Chunxu Li 0000-0001-7851-0260 4 67360__31086__7c362c3493204fef9c34946d2e37c75a.pdf 67360.VoR.pdf 2024-08-12T15:52:20.3458406 Output 2273964 application/pdf Version of Record true © The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License. true eng http://creativecommons.org/licenses/by/4.0/
title Implementation of extended kalman filter for localization of ambulance robot
spellingShingle Implementation of extended kalman filter for localization of ambulance robot
ZIRONG TANG
Chunxu Li
title_short Implementation of extended kalman filter for localization of ambulance robot
title_full Implementation of extended kalman filter for localization of ambulance robot
title_fullStr Implementation of extended kalman filter for localization of ambulance robot
title_full_unstemmed Implementation of extended kalman filter for localization of ambulance robot
title_sort Implementation of extended kalman filter for localization of ambulance robot
author_id_str_mv be49378076f647e6dacf0c46dfd3d091
e6ed70d02c25b05ab52340312559d684
author_id_fullname_str_mv be49378076f647e6dacf0c46dfd3d091_***_ZIRONG TANG
e6ed70d02c25b05ab52340312559d684_***_Chunxu Li
author ZIRONG TANG
Chunxu Li
author2 Chan-Yun Yang
Hooman Samani
ZIRONG TANG
Chunxu Li
format Journal article
container_title International Journal of Intelligent Robotics and Applications
container_volume 0
publishDate 2024
institution Swansea University
issn 2366-5971
2366-598X
doi_str_mv 10.1007/s41315-024-00352-z
publisher Springer Science and Business Media LLC
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
document_store_str 1
active_str 0
description This paper focuses on the implementation of the Extended Kalman Filter for indoor localization of a semi-autonomous Ambulance Robot system named Ambubot. The system is designed to reduce the response time for lay rescuers to locate an Automated External Defibrillator (AED) during sudden cardiac arrest events. To achieve this objective, the robot is equipped with an AED, and the Extended Kalman Filter is utilized for optimal indoor localization. The filter is implemented using data from the robot’s Inertial Measurement Unit, which comprises 9 Degrees of Freedom. The paper provides an explicit description of the performance of the Extended Kalman Filter in estimating the position of Ambubot, and demonstrates that the proposed approach is effective in accurately determining and estimating the robot’s position in unknown indoor environments. The results suggest that the proposed method is a promising solution for improving survival rates in cardiac arrest cases, and may have potential applications in other fields where accurate indoor localization is required.
published_date 2024-06-25T08:33:29Z
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score 11.3254

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