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Advanced Tactile Sensors: Design and Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 2078

Special Issue Editors

Bristol Robotics Laboratory, University of the West of England Bristol, Bristol, BS16 1QY, UK
Interests: robot learning; tactile-based manipulation; teleoperation
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Guest Editor
School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: embodied intelligence; human–robot interaction; robot dexterity; robot large model

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Guest Editor
Tsinghua-Berkeley Shenzhen Institute, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
Interests: self-powered sensors; energy harvesting; wearable devices for health and soft robotics with the help of signal processing; machine learning and mobile computing
Department of Engineering Mechanics, Shanghai Jiaotong University, Shanghai 200240, China
Interests: Robotic manipulation; tactile sensing and perception

Special Issue Information

Dear Colleagues,

This Special Issue, “Advanced Tactile Sensors: Design and Applications”, delves into the cutting-edge developments and innovative applications of tactile sensing technologies. This Special Issue primarily focuses on tactile sensors within the scope of Sensors and the significant impact on perception, robot manipulation, embodied intelligence, human–robot interaction, and robot dexterity. By exploring the latest advancements in sensor design and integration, this collection aims to highlight how tactile sensors enhance the capabilities of robots, enabling more sophisticated and nuanced interactions with their environments.

Potential topics include, but are not limited to, the following:

  1. Advanced tactile sensors and actuators;
  2. Embodied intelligence with touch;
  3. System integration of tactile sensors in robots;
  4. Tactile sensing in robot manipulation and human–robot interactions;
  5. Advanced methodologies in tactile perception;
  6. Datasets and benchmarks for robot tactile perception;
  7. Tactile simulations;
  8. The tactile internet;
  9. Other tactile applications.

Dr. Zhenyu Lu
Prof. Dr. Bin Fang
Dr. Wenbo Ding
Dr. Daolin Ma
Prof. Dr. Charlie Yang
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • tactile sensor
  • tactile internet
  • robotics
  • embodied intelligence
  • dataset

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Published Papers (1 paper)

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Research

16 pages, 5032 KiB  
Article
Detecting Transitions from Stability to Instability in Robotic Grasping Based on Tactile Perception
by Zhou Zhao, Dongyuan Zheng and Lu Chen
Sensors 2024, 24(15), 5080; https://doi.org/10.3390/s24155080 - 5 Aug 2024
Viewed by 1593
Abstract
Robots execute diverse load operations, including carrying, lifting, tilting, and moving objects, involving load changes or transfers. This dynamic process can result in the shift of interactive operations from stability to instability. In this paper, we respond to these dynamic changes by utilizing [...] Read more.
Robots execute diverse load operations, including carrying, lifting, tilting, and moving objects, involving load changes or transfers. This dynamic process can result in the shift of interactive operations from stability to instability. In this paper, we respond to these dynamic changes by utilizing tactile images captured from tactile sensors during interactions, conducting a study on the dynamic stability and instability in operations, and propose a real-time dynamic state sensing network by integrating convolutional neural networks (CNNs) for spatial feature extraction and long short-term memory (LSTM) networks to capture temporal information. We collect a dataset capturing the entire transition from stable to unstable states during interaction. Employing a sliding window, we sample consecutive frames from the collected dataset and feed them into the network for the state change predictions of robots. The network achieves both real-time temporal sequence prediction at 31.84 ms per inference step and an average classification accuracy of 98.90%. Our experiments demonstrate the network’s robustness, maintaining high accuracy even with previously unseen objects. Full article
(This article belongs to the Special Issue Advanced Tactile Sensors: Design and Applications)
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