PnuTac: A vision-based pneumatic tactile sensor for slip detection and object classification

Rayamane, Prasad ORCID: https://orcid.org/0000-0001-6336-7393, Herbert, Peter, Munguia-Galeano, Francisco ORCID: https://orcid.org/0000-0001-8397-3083 and Ji, Ze ORCID: https://orcid.org/0000-0002-8968-9902 2024. PnuTac: A vision-based pneumatic tactile sensor for slip detection and object classification. Presented at: 29th IEEE International Conference On Mechatronics And Machine Vision In Practice, 21 - 24 November 2023. Proceedings 29th International Conference on Mechatronics and Machine Vision in Practice (M2VIP). IEEE, 10.1109/M2VIP58386.2023.10413420

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Abstract

Soft optical tactile sensors allow robots to capture important information, such as contact geometry, estimations of object compliance, and slip detection. However, most optical tactile sensors utilize gel-filled elastic membranes with non-variable stiffness. To overcome this limitation, this paper presents the development of a pneumatic tactile sensor with tunable pressure (PnuTac). The sensor comprises a pneumatic system, an elastic membrane, and a sealed chamber with a camera inside. The inner side of the membrane layer has dot markers on its surface that are used for slip detection. Slippage is prevented by controlling a Robotiq 2-finger gripper that closes according to the slip detection signal. Additionally, objects held by the gripper appear as contours in sensor images. A dataset of 10,000 such images from 10 tools was utilized for training a VGG-19 convolutional neural network for tool classification. Our results show that increasing the pressure of the PnuTac sensor reduces the time it takes for the gripper to stabilize a slipping object. The overall success rate of slip detection was determined to be 87%. The trained neural network, fed from the PnuTac's sensor live data, successfully classified 8 out of the 10 tools.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Publisher:	IEEE
ISBN:	979-8-3503-2562-1
Date of First Compliant Deposit:	28 September 2023
Date of Acceptance:	21 September 2023
Last Modified:	20 Mar 2024 16:08
URI:	https://orca.cardiff.ac.uk/id/eprint/162820

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