Zhou Zhao
Multi-purpose tactile perception based on deep learning in a new tendon-driven optical tactile sensor
Zhao, Zhou; Lu, Zhenyu
Authors
Zhenyu Lu
Abstract
In this paper, we create a new tendon-connected multi-functional optical tactile sensor, MechTac, for object perception in the field of view (TacTip) and location of touching points in the blind area of vision (TacSide). In a multi-point touch task, the information of the TacSide and the TacTip are overlapped to commonly affect the distribution of papillae pins on the TacTip. Since the effects of TacSide are much less obvious to those affected on the TacTip, a perceiving out-of-view neural network (O2VNet) is created to separate the mixed information with unequal affection. To reduce the dependence of the O2VNet on the grayscale information of the image, we create one new binarized convolutional (BConv) layer in front of the backbone of the O2VNet. The O2VNet can not only achieve real-time temporal sequence prediction (34 ms per image), but also attain the average classification accuracy of 99.06%. The experimental results show that the O2VNet can hold a high classification accuracy even facing the image contrast changes.
Citation
Zhao, Z., & Lu, Z. (2022). Multi-purpose tactile perception based on deep learning in a new tendon-driven optical tactile sensor. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2099-2104). https://doi.org/10.1109/iros47612.2022.9981477
Conference Name | 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
---|---|
Conference Location | Kyoto, Japan |
Start Date | Oct 23, 2022 |
End Date | Oct 27, 2022 |
Acceptance Date | Jun 30, 2022 |
Online Publication Date | Dec 26, 2022 |
Publication Date | Dec 26, 2022 |
Deposit Date | Jan 14, 2023 |
Publicly Available Date | Dec 27, 2024 |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Volume | 2022-October |
Pages | 2099-2104 |
Series ISSN | 2153-0866 |
Book Title | 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
ISBN | 9781665479288 |
DOI | https://doi.org/10.1109/iros47612.2022.9981477 |
Keywords | Optical design, Neural networks, Tactile sensors, Optical computing, Optical imaging, Real-time systems, Pins |
Public URL | https://uwe-repository.worktribe.com/output/10337420 |
Publisher URL | https://ieeexplore.ieee.org/document/9981477 |
Related Public URLs | https://ieeexplore.ieee.org/xpl/conhome/9981026/proceeding |
Files
This file is under embargo until Dec 27, 2024 due to copyright reasons.
Contact Zhenyu.Lu@uwe.ac.uk to request a copy for personal use.
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