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Implementation of extended kalman filter for localization of ambulance robot
International Journal of Intelligent Robotics and Applications
Swansea University Authors:
ZIRONG TANG, Chunxu Li
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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...
Published in: | International Journal of Intelligent Robotics and Applications |
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ISSN: | 2366-5971 2366-598X |
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Springer Science and Business Media LLC
2024
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URI: | https://cronfa.swan.ac.uk/Record/cronfa67360 |
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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 2024-08-12T15:53:07.7321400 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 |
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|
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facultyofscienceandengineering |
hierarchy_top_title |
Faculty of Science and Engineering |
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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 |
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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 |
2024-06-25T15:55:48Z |
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1807194056999043072 |
score |
11.021648 |