Design and development of a robust vision-based tactile sensor

Rayamane, Prasad ORCID: https://orcid.org/0000-0001-6336-7393, Ji, Ze ORCID: https://orcid.org/0000-0002-8968-9902 and Packianather, Michael ORCID: https://orcid.org/0000-0002-9436-8206 2022. Design and development of a robust vision-based tactile sensor. Presented at: 2022 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Sapporo, Japan, 11-15 July 2022. 2022 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). IEEE, 10.1109/AIM52237.2022.9863285

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Abstract

For robots to perform advanced manipulation of objects, touch is a critical source of information, and a high-quality tactile sensor is essential. Image-based optical tactile sensors, and its inheritances, which have soft touch interfaces, can provide high-resolution tactile images of the contact geometry, contact pressure, and slip conditions. However, due to the lack of robustness provided by the current tactile sensors, the ability to grasp hard or sharp objects is minimal. In this work, we propose an image-based optical tactile sensor and overcome the above limitation of poor robustness by introducing a latex layer on the touch interface. We use a combination of silicone elastomer covered with a latex material and an acrylic sheet to support the silicone elastomer. A camera placed at the bottom of the sensor housing captures the deformation of the elastomer surface illuminated by an inner light. To evaluate the performance, we carried out a series of experiments. First, we evaluated the mechanical characteristics of the silicone elastomer with three types of coating, namely latex membrane, metallic coating, and no coating. The proposed latex membrane clearly outperformed the other two in terms of robustness. Second, we carried out the force-displacement experiments quantitatively to further study the sensitivity and robustness. Last, we validated the sensor performance in terms of its spatial resolution by applying the VGG-19 neural network for classifying touch patterns captured by the sensor. Overall, the proposed sensor achieved the desired robustness, sensitivity, and spatial resolution performance.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Publisher:	IEEE
ISBN:	9781665413091
ISSN:	2159-6255
Date of First Compliant Deposit:	17 May 2022
Date of Acceptance:	3 May 2022
Last Modified:	05 Dec 2023 03:14
URI:	https://orca.cardiff.ac.uk/id/eprint/149803

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