Vision-Based Proximity and Tactile Sensing for Robot Arms: Design, Perception, and Control

Soft-bodied robots with multimodal sensing capabilities hold promise for versatile and user-friendly robotics. However, seamlessly integrating multiple sensing functionalities into soft artificial skins remains a challenge due to compatibility issues between soft materials and conventional electronics. While vision-based tactile sensing has enabled simple and effective sensor designs for robotic touch, there has been limited exploration of this technique for intrinsic multimodal sensing in large-sized soft robot bodies. To address this gap, this paper introduces a novel vision-based soft sensing technique, named ProTac, capable of operating either in tactile or proximity sensing modes. This vision-based sensing technology relies on a soft functional skin that can actively switch its optical properties between opaque and transparent states. Furthermore, the paper develops efficient learning pipelines for proximity and tactile perceptions, as well as sensing strategies enabled through the timing activation of the two sensing modes. The effectiveness of the soft sensing technology is demonstrated through a soft ProTac link, which can be integrated into newly constructed or existing commercial robot arms. Results suggest that robots integrated with the ProTac link, along with rigorous control formulation can perform safe and purposeful control actions, which enhances human-robot interaction scenarios and facilitates motion control tasks that are challenging to achieve with conventional rigid links. Supplementary video: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://youtu.be/dFgZLUpeWw4</uri> Project website: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://quan-luu.github.io/protac-website/</uri>

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Publication Details

Journal
IEEE Transactions on Robotics
Published
2025-01-01
DOI
https://doi.org/10.1109/tro.2025.3593087
Citations
3
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
Field-Weighted Citation Impact
3.73
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article

Vision-Based Proximity and Tactile Sensing for Robot Arms: Design, Perception, and Control

Van Anh Ho, Nhan Huu Nguyen, Quan Khanh Luu, Dinh Quang Nguyen et al.
3 citations
IEEE Transactions on Robotics
Industrial Vision Systems and Defect Detection
3.73
article

Vision-Based Proximity and Tactile Sensing for Robot Arms: Design, Perception, and Control

Van Anh Ho, Nhan Huu Nguyen, Quan Khanh Luu, Dinh Quang Nguyen, Nam Phuong Dam
article en
3 citations

Abstract

Soft-bodied robots with multimodal sensing capabilities hold promise for versatile and user-friendly robotics. However, seamlessly integrating multiple sensing functionalities into soft artificial skins remains a challenge due to compatibility issues between soft materials and conventional electronics. While vision-based tactile sensing has enabled simple and effective sensor designs for robotic touch, there has been limited exploration of this technique for intrinsic multimodal sensing in large-sized soft robot bodies. To address this gap, this paper introduces a novel vision-based soft sensing technique, named ProTac, capable of operating either in tactile or proximity sensing modes. This vision-based sensing technology relies on a soft functional skin that can actively switch its optical properties between opaque and transparent states. Furthermore, the paper develops efficient learning pipelines for proximity and tactile perceptions, as well as sensing strategies enabled through the timing activation of the two sensing modes. The effectiveness of the soft sensing technology is demonstrated through a soft ProTac link, which can be integrated into newly constructed or existing commercial robot arms. Results suggest that robots integrated with the ProTac link, along with rigorous control formulation can perform safe and purposeful control actions, which enhances human-robot interaction scenarios and facilitates motion control tasks that are challenging to achieve with conventional rigid links. Supplementary video: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://youtu.be/dFgZLUpeWw4</uri> Project website: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://quan-luu.github.io/protac-website/</uri>

IEEE Transactions on RoboticsVol. 41
Japan Advanced Institute of Science and Technology (JP), VNU University of Science (VN)
Openalex Percentile: Top 7%
Industrial Vision Systems and Defect Detection
3.73
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