Special Issue "Tactile Sensing for Soft Robotics and Wearables"

A special issue of Micromachines (ISSN 2072-666X).

Deadline for manuscript submissions: closed (30 April 2018)

Special Issue Editors

Guest Editor
Dr. Lucia Beccai

Center for Micro-Biorobotics, Istituto Italiano di Tecnologia (IIT), Pontedera (Pisa), Italy
Website 1 | Website 2 | E-Mail
Phone: +39-050-883-079(400)
Interests: artificial touch; mechanotransduction; biorobotics; soft robotics; 3D lithography; mechanical sensing; soft MEMS/NEMS
Guest Editor
Dr. Massimo Totaro

Center for Micro-Biorobotics, Istituto Italiano di Tecnologia (IIT), Pontedera (Pisa), Italy
Website | E-Mail
Phone: +39-050-883-015
Interests: tactile sensing; nanotechnology; soft robotics; MEMS/NEMS; FEM electromechanical modeling

Special Issue Information

Dear Colleagues,

In recent years, rapid developments in tactile sensing have mainly been due to the advent of novel deformable materials, mimicking skin flexibility and elasticity. In addition to single sensors, electronic skins built from both inorganic and organic electronic materials have boosted up, especially ultra-thin and ultra-conformable systems. However, many aspects require new concepts at component, as well as at system, levels. For example, effective solutions for achieving multimodality are needed. Indeed, information of various types (e.g., pressure, temperature, humidity, etc.) should be detected in a robust and reliable manner, and in large area issues of real-time signal processing are posed, as well of wiring and connections. Additionally, today, new challenges emerge from soft robotic approaches and wearable systems, where the use of deformable sensors becomes crucial for encoding a variety of information that are not only provided by the external world, but also by the deformation of the hosting robot/human body. This Special Issue seeks to showcase research papers, short communications, and review articles on novel developments of soft tactile sensing, and mechanical sensing more at large. The focus is on new designs and models, new materials and fabrication processes, advanced signal processing and innovative machine learning algorithms that could be useful to target real applications in both in robotics and wearable systems, e.g., biomimetic skins, smart surgical tools, wearables for monitoring vital parameters (e.g., body temperature, blood pulse) and body movements.

Dr. Lucia Beccai
Dr. Massimo Totaro
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Micromachines is an international peer-reviewed open access monthly 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 1200 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

  • Artificial touch
  • Soft robotics
  • Mechanical sensors
  • Wearable systems
  • Soft materials
  • Machine learning

Published Papers (5 papers)

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Research

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Open AccessArticle Updated Tactile Feedback with a Pin Array Matrix Helps Blind People to Reduce Self-Location Errors
Micromachines 2018, 9(7), 351; https://doi.org/10.3390/mi9070351
Received: 4 June 2018 / Revised: 28 June 2018 / Accepted: 9 July 2018 / Published: 14 July 2018
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Abstract
Autonomous navigation in novel environments still represents a challenge for people with visual impairment (VI). Pin array matrices (PAM) are an effective way to display spatial information to VI people in educative/rehabilitative contexts, as they provide high flexibility and versatility. Here, we tested
[...] Read more.
Autonomous navigation in novel environments still represents a challenge for people with visual impairment (VI). Pin array matrices (PAM) are an effective way to display spatial information to VI people in educative/rehabilitative contexts, as they provide high flexibility and versatility. Here, we tested the effectiveness of a PAM in VI participants in an orientation and mobility task. They haptically explored a map showing a scaled representation of a real room on the PAM. The map further included a symbol indicating a virtual target position. Then, participants entered the room and attempted to reach the target three times. While a control group only reviewed the same, unchanged map on the PAM between trials, an experimental group also received an updated map representing, in addition, the position they previously reached in the room. The experimental group significantly improved across trials by having both reduced self-location errors and reduced completion time, unlike the control group. We found that learning spatial layouts through updated tactile feedback on programmable displays outperforms conventional procedures on static tactile maps. This could represent a powerful tool for navigation, both in rehabilitation and everyday life contexts, improving spatial abilities and promoting independent living for VI people. Full article
(This article belongs to the Special Issue Tactile Sensing for Soft Robotics and Wearables)
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Open AccessArticle Development of a Sensor Network System with High Sampling Rate Based on Highly Accurate Simultaneous Synchronization of Clock and Data Acquisition and Experimental Verification
Micromachines 2018, 9(7), 325; https://doi.org/10.3390/mi9070325
Received: 30 April 2018 / Revised: 21 June 2018 / Accepted: 25 June 2018 / Published: 27 June 2018
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Abstract
In this paper, we develop a new sensor network system with a high sampling rate (over 500 Hz) based on the simultaneous synchronization of clock and data acquisition for integrating the data obtained from various sensors. Hence, we also propose a method for
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In this paper, we develop a new sensor network system with a high sampling rate (over 500 Hz) based on the simultaneous synchronization of clock and data acquisition for integrating the data obtained from various sensors. Hence, we also propose a method for the synchronization of clock and data acquisition in the sensor network system. In the proposed scheme, multiple sensor nodes including PCs are connected via Ethernet for data communication and for clock synchronization. The timing of the data acquisition of each sensor is locally controlled based on the PC’s clock locally provided in the node, and the clocks are globally synchronized over the network. We construct three types of high-speed sensor network systems using the proposed method: the first one is composed of a high-speed tactile sensor node and a high-speed vision node; the second one is composed of a high-speed tactile sensor node and three acceleration sensor nodes; and the last one is composed of a high-speed tactile sensor node, two acceleration sensor nodes, and a gyro sensor node. Through experiments, we verify that the timing error between the sensor nodes for data acquisition is less than 15 μs, which is significantly smaller than the time interval of 2 ms or a shorter sampling time (less than 2 ms). We also confirm the effectiveness of the proposed method and it is expected that the system can be applied to various applications. Full article
(This article belongs to the Special Issue Tactile Sensing for Soft Robotics and Wearables)
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Open AccessArticle A Flexible Annular Sectorial Sensor for Detecting Contact Position Based on Constant Electric Field
Micromachines 2018, 9(6), 309; https://doi.org/10.3390/mi9060309
Received: 29 April 2018 / Revised: 7 June 2018 / Accepted: 15 June 2018 / Published: 19 June 2018
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Abstract
To achieve tactile detection on the irregular surface of a robot link, a flexible annular sectorial sensor with a five-layer structure was proposed that could be wrapped on the surface of a truncated cone-shaped link. The sensor was designed for the detection of
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To achieve tactile detection on the irregular surface of a robot link, a flexible annular sectorial sensor with a five-layer structure was proposed that could be wrapped on the surface of a truncated cone-shaped link. The sensor was designed for the detection of a contact position when robots collide with other objects during movement. The sensor obtains the coordinates of the contact position by exerting a constant electric field on the upper and lower conductive layers. The mathematical model linking the coordinates of the contact position and the corresponding electric potential on the conductive layer was established, based on the uniqueness of the electric field. The design of the sensor was simulated using COMSOL software, and the detection error of the contact position was discussed. A sensor sample was fabricated and wrapped on the mechanical arm. The results of the simulations and experiments indicated that the flexible sensor performed very well when wrapped on the robot link. Full article
(This article belongs to the Special Issue Tactile Sensing for Soft Robotics and Wearables)
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Open AccessArticle Decoupling Research of a Novel Three-Dimensional Force Flexible Tactile Sensor Based on an Improved BP Algorithm
Micromachines 2018, 9(5), 236; https://doi.org/10.3390/mi9050236
Received: 26 March 2018 / Revised: 10 May 2018 / Accepted: 11 May 2018 / Published: 14 May 2018
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Abstract
Decoupling research on flexible tactile sensors play a very important role in the intelligent robot skin and tactile-sensing fields. In this paper, an efficient machine learning method based on the improved back-propagation (BP) algorithm is proposed to decouple the mapping relationship between the
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Decoupling research on flexible tactile sensors play a very important role in the intelligent robot skin and tactile-sensing fields. In this paper, an efficient machine learning method based on the improved back-propagation (BP) algorithm is proposed to decouple the mapping relationship between the resistances of force-sensitive conductive pillars and three-dimensional forces for the 6 × 6 novel flexible tactile sensor array. Tactile-sensing principles and numerical experiments are analyzed. The tactile sensor array model accomplishes the decomposition of the force components by its delicate structure, and avoids direct interference among the electrodes of the sensor array. The force components loaded on the tactile sensor are decoupled with a very high precision from the resistance signal by the improved BP algorithm. The decoupling results show that the k-cross validation (k-CV) algorithm is a highly effective method to improve the decoupling precision of force components for the novel tactile sensor. The large dataset with the k-CV method obtains a better decoupling accuracy of the force components than the small dataset. All of the decoupling results are fairly good, and they indicate that the improved BP model with a strong non-linear approaching ability has an efficient and valid performance in decoupling force components for the tactile sensor. Full article
(This article belongs to the Special Issue Tactile Sensing for Soft Robotics and Wearables)
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Review

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Open AccessFeature PaperReview Recent Advances in Tactile Sensing Technology
Micromachines 2018, 9(7), 321; https://doi.org/10.3390/mi9070321
Received: 29 May 2018 / Revised: 21 June 2018 / Accepted: 21 June 2018 / Published: 25 June 2018
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Abstract
Research on tactile sensing technology has been actively conducted in recent years to pave the way for the next generation of highly intelligent devices. Sophisticated tactile sensing technology has a broad range of potential applications in various fields including: (1) robotic systems with
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Research on tactile sensing technology has been actively conducted in recent years to pave the way for the next generation of highly intelligent devices. Sophisticated tactile sensing technology has a broad range of potential applications in various fields including: (1) robotic systems with tactile sensors that are capable of situation recognition for high-risk tasks in hazardous environments; (2) tactile quality evaluation of consumer products in the cosmetic, automobile, and fabric industries that are used in everyday life; (3) robot-assisted surgery (RAS) to facilitate tactile interaction with the surgeon; and (4) artificial skin that features a sense of touch to help people with disabilities who suffer from loss of tactile sense. This review provides an overview of recent advances in tactile sensing technology, which is divided into three aspects: basic physiology associated with human tactile sensing, the requirements for the realization of viable tactile sensors, and new materials for tactile devices. In addition, the potential, hurdles, and major challenges of tactile sensing technology applications including artificial skin, medical devices, and analysis tools for human tactile perception are presented in detail. Finally, the review highlights possible routes, rapid trends, and new opportunities related to tactile devices in the foreseeable future. Full article
(This article belongs to the Special Issue Tactile Sensing for Soft Robotics and Wearables)
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