Device diversity in crowdsourced WiFi fingerprinting database for autonomous mobile robot
Positioning and navigation of mobile robot is the main feature for the trajectory or motion of the mobile robot. Conventional mobile robot positioning and navigation system relies heavily on fusion of multiple costly sensors, which does not promote mass production. This paper aim is to use readily a...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2024-11-01
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| Series: | International Journal of Advanced Robotic Systems |
| Online Access: | https://doi.org/10.1177/17298806241297109 |
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| _version_ | 1849220703193137152 |
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| author | Ahmad Hakimi Ahmad Sa’ahiry Abdul Halim Ismail Masahiro Toyoura Latifah Munirah Kamaruddin Mohd Sani Mohamad Hashim Muhamad Safwan Muhamad Azmi Hiromitsu Nishizaki Xiaoyang Mao |
| author_facet | Ahmad Hakimi Ahmad Sa’ahiry Abdul Halim Ismail Masahiro Toyoura Latifah Munirah Kamaruddin Mohd Sani Mohamad Hashim Muhamad Safwan Muhamad Azmi Hiromitsu Nishizaki Xiaoyang Mao |
| author_sort | Ahmad Hakimi Ahmad Sa’ahiry |
| collection | DOAJ |
| description | Positioning and navigation of mobile robot is the main feature for the trajectory or motion of the mobile robot. Conventional mobile robot positioning and navigation system relies heavily on fusion of multiple costly sensors, which does not promote mass production. This paper aim is to use readily and available technologies which is WiFi due to its reliability as it is pre-deployed, and it exist in most of the building. The system used are based on indoor positioning system (IPS) by using a crowdsourced fingerprinting method. This seeks to improve crowdsourced fingerprinting database performance by solving the issue of the device diversity or heterogeneity of difference devices. To cope with the crowdsourced fingerprinting database as the location estimation method for the robot application, deep neural network (DNN) is employed. The proposed method namely ratio and ranged-based (RRB) shows an improvement of 60% increments by implementing the pre-processing technique of the raw data before feeding it to the DNN. The comparison between other method shows that RRB is better in term of accuracy in three validation techniques, which are root mean square error (RMSE), distance error and accuracy between true and estimate position. This improvement effectively could facilitate the actual positioning system utilizing the WiFi infrastructure for the mobile robot in very near future. |
| format | Article |
| id | doaj-art-4b023d0a6f734baaa4ae9ae71c6f96bb |
| institution | Kabale University |
| issn | 1729-8814 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | International Journal of Advanced Robotic Systems |
| spelling | doaj-art-4b023d0a6f734baaa4ae9ae71c6f96bb2024-12-05T10:03:54ZengSAGE PublishingInternational Journal of Advanced Robotic Systems1729-88142024-11-012110.1177/17298806241297109Device diversity in crowdsourced WiFi fingerprinting database for autonomous mobile robotAhmad Hakimi Ahmad Sa’ahiry0Abdul Halim Ismail1Masahiro Toyoura2Latifah Munirah Kamaruddin3Mohd Sani Mohamad Hashim4Muhamad Safwan Muhamad Azmi5Hiromitsu Nishizaki6Xiaoyang Mao7 Faculty of Information and Communication Technology, , Jalan Universiti, Perak, Malaysia Faculty of Electrical Engineering Technology, , Arau Perlis, Malaysia Department of Computer Science and Engineering, , Kofu Yamanashi, Japan Faculty of Electronic Engineering Technology, , Arau Perlis, Malaysia Faculty of Mechanical Engineering Technology, , Arau Perlis, Malaysia Department of Mechatronics, , Kofu Yamanashi, Japan Department of Mechatronics, , Kofu Yamanashi, Japan Department of Computer Science and Engineering, University of Yamanashi, Kofu Yamanashi, JapanPositioning and navigation of mobile robot is the main feature for the trajectory or motion of the mobile robot. Conventional mobile robot positioning and navigation system relies heavily on fusion of multiple costly sensors, which does not promote mass production. This paper aim is to use readily and available technologies which is WiFi due to its reliability as it is pre-deployed, and it exist in most of the building. The system used are based on indoor positioning system (IPS) by using a crowdsourced fingerprinting method. This seeks to improve crowdsourced fingerprinting database performance by solving the issue of the device diversity or heterogeneity of difference devices. To cope with the crowdsourced fingerprinting database as the location estimation method for the robot application, deep neural network (DNN) is employed. The proposed method namely ratio and ranged-based (RRB) shows an improvement of 60% increments by implementing the pre-processing technique of the raw data before feeding it to the DNN. The comparison between other method shows that RRB is better in term of accuracy in three validation techniques, which are root mean square error (RMSE), distance error and accuracy between true and estimate position. This improvement effectively could facilitate the actual positioning system utilizing the WiFi infrastructure for the mobile robot in very near future.https://doi.org/10.1177/17298806241297109 |
| spellingShingle | Ahmad Hakimi Ahmad Sa’ahiry Abdul Halim Ismail Masahiro Toyoura Latifah Munirah Kamaruddin Mohd Sani Mohamad Hashim Muhamad Safwan Muhamad Azmi Hiromitsu Nishizaki Xiaoyang Mao Device diversity in crowdsourced WiFi fingerprinting database for autonomous mobile robot International Journal of Advanced Robotic Systems |
| title | Device diversity in crowdsourced WiFi fingerprinting database for autonomous mobile robot |
| title_full | Device diversity in crowdsourced WiFi fingerprinting database for autonomous mobile robot |
| title_fullStr | Device diversity in crowdsourced WiFi fingerprinting database for autonomous mobile robot |
| title_full_unstemmed | Device diversity in crowdsourced WiFi fingerprinting database for autonomous mobile robot |
| title_short | Device diversity in crowdsourced WiFi fingerprinting database for autonomous mobile robot |
| title_sort | device diversity in crowdsourced wifi fingerprinting database for autonomous mobile robot |
| url | https://doi.org/10.1177/17298806241297109 |
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