A Machine Learning Approach to Contact Localization in Variable Density Three-Dimensional Tactile Artificial Skin
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866912139262820352 |
|---|---|
| author | Kohlbrenner, Carson Murray, Mitchell Zhang, Yutong Escobedo, Caleb Dunnington, Thomas Stevenson, Nolan Correll, Nikolaus Roncone, Alessandro |
| author_facet | Kohlbrenner, Carson Murray, Mitchell Zhang, Yutong Escobedo, Caleb Dunnington, Thomas Stevenson, Nolan Correll, Nikolaus Roncone, Alessandro |
| contents | Estimating the location of contact is a primary function of artificial tactile sensing apparatuses that perceive the environment through touch. Existing contact localization methods use flat geometry and uniform sensor distributions as a simplifying assumption, limiting their ability to be used on 3D surfaces with variable density sensing arrays. This paper studies contact localization on an artificial skin embedded with mutual capacitance tactile sensors, arranged non-uniformly in an unknown distribution along a semi-conical 3D geometry. A fully connected neural network is trained to localize the touching points on the embedded tactile sensors. The studied online model achieves a localization error of $5.7 \pm 3.0$ mm. This research contributes a versatile tool and robust solution for contact localization that is ambiguous in shape and internal sensor distribution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_00689 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Machine Learning Approach to Contact Localization in Variable Density Three-Dimensional Tactile Artificial Skin Kohlbrenner, Carson Murray, Mitchell Zhang, Yutong Escobedo, Caleb Dunnington, Thomas Stevenson, Nolan Correll, Nikolaus Roncone, Alessandro Robotics Estimating the location of contact is a primary function of artificial tactile sensing apparatuses that perceive the environment through touch. Existing contact localization methods use flat geometry and uniform sensor distributions as a simplifying assumption, limiting their ability to be used on 3D surfaces with variable density sensing arrays. This paper studies contact localization on an artificial skin embedded with mutual capacitance tactile sensors, arranged non-uniformly in an unknown distribution along a semi-conical 3D geometry. A fully connected neural network is trained to localize the touching points on the embedded tactile sensors. The studied online model achieves a localization error of $5.7 \pm 3.0$ mm. This research contributes a versatile tool and robust solution for contact localization that is ambiguous in shape and internal sensor distribution. |
| title | A Machine Learning Approach to Contact Localization in Variable Density Three-Dimensional Tactile Artificial Skin |
| topic | Robotics |
| url | https://arxiv.org/abs/2412.00689 |