Posture Estimation and Obstacle Detection by Embedding Distance-Measuring Sensors in a Spherical Mobile Robot
In this study, we developed a method for designing spherical mobile robots that can detect obstacles and can estimate posture using embedded laser-ranging sensors in a spherical shell. A mobile robot used in commercial facilities must be safe for humans, and must also be able to detect and avoid obs...
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| Vydáno v: | Journal of advanced computational intelligence and intelligent informatics Ročník 29; číslo 6; s. 1402 - 1409 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Tokyo
Fuji Technology Press Co. Ltd
20.11.2025
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| Témata: | |
| ISSN: | 1343-0130, 1883-8014 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | In this study, we developed a method for designing spherical mobile robots that can detect obstacles and can estimate posture using embedded laser-ranging sensors in a spherical shell. A mobile robot used in commercial facilities must be safe for humans, and must also be able to detect and avoid obstacles. Spherical mobile robots are considered suitable for such purposes as operating near humans. However, the installation of external measurement sensors in spherical mobile robots can reduce their mobility. In this study, we developed a novel installation method for embedding external laser-ranging measurement sensors in a spherical shell. This method can successfully install sensors without compromising on the capability such as mobile characteristics of the robot. In addition, we proposed a posture estimation method using embedded laser-ranging sensors only. Moreover, we proposed a method for classifying point-cloud data into floors or obstacles. The validity of these methods was verified by simulations, which demonstrated that the methods could detect obstacles and estimate the robot’s posture, even in the presence of sensor noise. |
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| Bibliografie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 1343-0130 1883-8014 |
| DOI: | 10.20965/jaciii.2025.p1402 |