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Special Issue "Tactile Sensors for Robotic Applications"

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

Deadline for manuscript submissions: 30 April 2020.

Special Issue Editor

Prof. Dr. Salvatore Pirozzi
E-Mail Website
Guest Editor
Dipartimento di Ingegneria, Università degli Studi della Campania “Luigi Vanvitelli”, Naples, Italy
Interests: design and modelling of innovative sensors, in particular of tactile solutions, as well as interpretation and fusion of data acquired from the developed sensors; development of models for tactile sensors and algorithms for the tactile data interpretation and for slipping detection and avoidance; modeling and control of smart actuators for active noise and vibration control.

Special Issue Information

Dear Colleagues,

In recent years, tactile sensing has become a key enabling technology to implement complex tasks by using robotic systems. For example, the successful execution of robotic grasping and manipulation tasks is strongly dependent on the knowledge of objects’ geometrical and physical characteristics, especially when objects are deformable and can change their shapes depending on their interaction with the environment. To this aim, the robotic systems are more and more frequently equipped with sensorized grippers, which estimate the object’s features by using tactile sensors. Also, a safe and efficient pHRI (physical Human Robot Interaction) requires the knowledge of interaction forces and contact locations in order to perform cooperation and co-manipulation tasks and to limit damage from accidental impacts. This crucial information can be obtained through direct measurements by using an artificial sense of touch.

It is evident that the number of different contexts in which the sense of touch can be fundamental for the robotic systems of the future is high and growing. The aim of this Special Issue is to present robotic applications for which tactile sensing represents a solution that allows clear improvements for task automation.

This Special Issue invites contributions in the following topics (but is not limited to them):

  • Tactile sensor technologies
  • Tactile sensor modelling
  • Tactile data interpretation
  • Robot tactile systems
  • Force and tactile sensing
  • Grasping and manipulation
  • Deformable object manipulation
  • Contact modelling
  • Dexterous manipulation
  • Artificial skin
  • Object features recognition
  • Slipping detection and avoidance
  • Physical human robot interaction
  • Human machine interfaces

Prof. Dr. Pirozzi Salvatore
Guest Editor

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. 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 1800 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.

Published Papers (9 papers)

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Research

Open AccessArticle
Using 3D Convolutional Neural Networks for Tactile Object Recognition with Robotic Palpation
Sensors 2019, 19(24), 5356; https://doi.org/10.3390/s19245356 - 05 Dec 2019
Abstract
In this paper, a novel method of active tactile perception based on 3D neural networks and a high-resolution tactile sensor installed on a robot gripper is presented. A haptic exploratory procedure based on robotic palpation is performed to get pressure images at different [...] Read more.
In this paper, a novel method of active tactile perception based on 3D neural networks and a high-resolution tactile sensor installed on a robot gripper is presented. A haptic exploratory procedure based on robotic palpation is performed to get pressure images at different grasping forces that provide information not only about the external shape of the object, but also about its internal features. The gripper consists of two underactuated fingers with a tactile sensor array in the thumb. A new representation of tactile information as 3D tactile tensors is described. During a squeeze-and-release process, the pressure images read from the tactile sensor are concatenated forming a tensor that contains information about the variation of pressure matrices along with the grasping forces. These tensors are used to feed a 3D Convolutional Neural Network (3D CNN) called 3D TactNet, which is able to classify the grasped object through active interaction. Results show that 3D CNN performs better, and provide better recognition rates with a lower number of training data. Full article
(This article belongs to the Special Issue Tactile Sensors for Robotic Applications)
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Open AccessArticle
Towards Tangible Vision for the Visually Impaired through 2D Multiarray Braille Display
Sensors 2019, 19(23), 5319; https://doi.org/10.3390/s19235319 - 03 Dec 2019
Abstract
This paper presents two methodologies for delivering multimedia content to visually impaired people with the use of a haptic device and braille display. Based on our previous research, the research using Kinect v2 and haptic device with 2D+ (RGB frame with depth) data [...] Read more.
This paper presents two methodologies for delivering multimedia content to visually impaired people with the use of a haptic device and braille display. Based on our previous research, the research using Kinect v2 and haptic device with 2D+ (RGB frame with depth) data has the limitations of slower operational speed while reconstructing object details. Thus, this study focuses on the development of 2D multiarray braille display using an electronic book translator application because of its accuracy and high speed. This approach provides mobility and uses 2D multiarray braille display to represent media content contour more efficiently. In conclusion, this study achieves the representation of considerably massive text content compared to previous 1D braille displays. Besides, it also represents illustrations and figures to braille displays through quantization and binarization. Full article
(This article belongs to the Special Issue Tactile Sensors for Robotic Applications)
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Open AccessArticle
Magnetic-based Soft Tactile Sensors with Deformable Continuous Force Transfer Medium for Resolving Contact Locations in Robotic Grasping and Manipulation
Sensors 2019, 19(22), 4925; https://doi.org/10.3390/s19224925 - 12 Nov 2019
Abstract
The resolution of contact location is important in many applications in robotics and automation. This is generally done by using an array of contact or tactile receptors, which increases cost and complexity as the required resolution or area is increased. Tactile sensors have [...] Read more.
The resolution of contact location is important in many applications in robotics and automation. This is generally done by using an array of contact or tactile receptors, which increases cost and complexity as the required resolution or area is increased. Tactile sensors have also been developed using a continuous deformable medium between the contact and the receptors, which allows few receptors to interpolate the information among them, avoiding the weakness highlighted in the former approach. The latter is generally used to measure contact force intensity or magnitude but rarely used to identify the contact locations. This paper presents a systematic design and characterisation procedure for magnetic-based soft tactile sensors (utilizing the latter approach with the deformable contact medium) with the goal of locating the contact force location. This systematic procedure provides conditions under which design parameters can be selected, supported by a selected machine learning algorithm, to achieve the desired performance of the tactile sensor in identifying the contact location. An illustrative example, which combines a particular sensor configuration (magnetic hall effect sensor as the receptor, a selected continuous medium and a selected sensing resolution) and a specific data-driven algorithm, is used to illustrate the proposed design procedure. The results of the illustrative example design demonstrates the efficacy of the proposed design procedure and the proposed sensing strategy in identifying a contact location. The resulting sensor is also tested on a robotic hand (Allegro Hand, SimLab Co) to demonstrate its application in real-world scenarios. Full article
(This article belongs to the Special Issue Tactile Sensors for Robotic Applications)
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Open AccessArticle
Hysteresis Compensation in Force/Torque Sensors Using Time Series Information
Sensors 2019, 19(19), 4259; https://doi.org/10.3390/s19194259 - 30 Sep 2019
Abstract
The purpose of this study is to compensate for the hysteresis in a six-axis force sensor using signal processing, thereby achieving high-precision force sensing. Although mathematical models of hysteresis exist, many of these are one-axis models and the modeling is difficult if they [...] Read more.
The purpose of this study is to compensate for the hysteresis in a six-axis force sensor using signal processing, thereby achieving high-precision force sensing. Although mathematical models of hysteresis exist, many of these are one-axis models and the modeling is difficult if they are expanded to multiple axes. Therefore, this study attempts to resolve this problem through machine learning. Since hysteresis is dependent on the previous history, this study investigates the effect of using time series information in machine learning. Experimental results indicate that the performance is improved by including time series information in the linear regression process generally utilized to calibrate six-axis force sensors. Full article
(This article belongs to the Special Issue Tactile Sensors for Robotic Applications)
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Open AccessArticle
Soft Magnetic Powdery Sensor for Tactile Sensing
Sensors 2019, 19(12), 2677; https://doi.org/10.3390/s19122677 - 13 Jun 2019
Abstract
Soft resistive tactile sensors are versatile devices with applications in next-generation flexible electronics. We developed a novel type of soft resistive tactile sensor called a soft magnetic powdery sensor (soft-MPS) and evaluated its response characteristics. The soft-MPS comprises ferromagnetic powder that is immobilized [...] Read more.
Soft resistive tactile sensors are versatile devices with applications in next-generation flexible electronics. We developed a novel type of soft resistive tactile sensor called a soft magnetic powdery sensor (soft-MPS) and evaluated its response characteristics. The soft-MPS comprises ferromagnetic powder that is immobilized in a liquid resin such as polydimethylsiloxane (PDMS) after orienting in a magnetic field. On applying an external force to the sensor, the relative distance between particles changes, thereby affecting its resistance. Since the ferromagnetic powders are in contact from the initial state, they have the ability to detect small contact forces compared to conventional resistive sensors in which the conductive powder is dispersed in a flexible material. The sensor unit can be made in any shape by controlling the layout of the magnetic field. Soft-MPSs with different hardnesses that could detect small forces were fabricated. The soft-MPS could be applied to detect collisions in robot hands/arms or in ultra-sensitive touchscreen devices. Full article
(This article belongs to the Special Issue Tactile Sensors for Robotic Applications)
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Open AccessArticle
Estimating the Orientation of Objects from Tactile Sensing Data Using Machine Learning Methods and Visual Frames of Reference
Sensors 2019, 19(10), 2285; https://doi.org/10.3390/s19102285 - 17 May 2019
Cited by 1
Abstract
Underactuated hands are useful tools for robotic in-hand manipulation tasks due to their capability to seamlessly adapt to unknown objects. To enable robots using such hands to achieve and maintain stable grasping conditions even under external disturbances while keeping track of an in-hand [...] Read more.
Underactuated hands are useful tools for robotic in-hand manipulation tasks due to their capability to seamlessly adapt to unknown objects. To enable robots using such hands to achieve and maintain stable grasping conditions even under external disturbances while keeping track of an in-hand object’s state requires learning object-tactile sensing data relationships. The human somatosensory system combines visual and tactile sensing information in their “What and Where” subsystem to achieve high levels of manipulation skills. The present paper proposes an approach for estimating the pose of in-hand objects combining tactile sensing data and visual frames of reference like the human “What and Where” subsystem. The system proposed here uses machine learning methods to estimate the orientation of in-hand objects from the data gathered by tactile sensors mounted on the phalanges of underactuated fingers. While tactile sensing provides local information about objects during in-hand manipulation, a vision system generates egocentric and allocentric frames of reference. A dual fuzzy logic controller was developed to achieve and sustain stable grasping conditions autonomously while forces were applied to in-hand objects to expose the system to different object configurations. Two sets of experiments were used to explore the system capabilities. On the first set, external forces changed the orientation of objects while the fuzzy controller kept objects in-hand for tactile and visual data collection for five machine learning estimators. Among these estimators, the ridge regressor achieved an average mean squared error of 0.077 . On the second set of experiments, one of the underactuated fingers performed open-loop object rotations and data recorded were supplied to the same set of estimators. In this scenario, the Multilayer perceptron (MLP) neural network achieved the lowest mean squared error of 0.067 . Full article
(This article belongs to the Special Issue Tactile Sensors for Robotic Applications)
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Open AccessArticle
Localization of Sliding Movements Using Soft Tactile Sensing Systems with Three-axis Accelerometers
Sensors 2019, 19(9), 2036; https://doi.org/10.3390/s19092036 - 30 Apr 2019
Cited by 1
Abstract
This paper presents a soft tactile sensor system for the localization of sliding movements on a large contact surface using an accelerometer. The system consists of a silicone rubber base with a chamber covered by a thin silicone skin in which a three-axis [...] Read more.
This paper presents a soft tactile sensor system for the localization of sliding movements on a large contact surface using an accelerometer. The system consists of a silicone rubber base with a chamber covered by a thin silicone skin in which a three-axis accelerometer is embedded. By pressurizing the chamber, the skin inflates, changing its sensitivity to the sliding movement on the skin’s surface. Based on the output responses of the accelerometer, the sensor system localizes the sliding motion. First, we present the idea, design, fabrication process, and the operation principle of our proposed sensor. Next, we created a numerical simulation model to investigate the dynamic changes of the accelerometer’s posture under sliding actions. Finally, experiments were conducted with various sliding conditions. By confirming the numerical simulation, dynamic analysis, and experimental results, we determined that the sensor system can detect the sliding movements, including the sliding directions, velocity, and localization of an object. We also point out the role of pressurization in the sensing system’s sensitivity under sliding movements, implying the ideal pressurization for it. We also discuss its limitations and applicability. This paper reflects our developed research in intelligent integration and soft morphological computation for soft tactile sensing systems. Full article
(This article belongs to the Special Issue Tactile Sensors for Robotic Applications)
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Open AccessArticle
Robot Intelligent Grasp of Unknown Objects Based on Multi-Sensor Information
Sensors 2019, 19(7), 1595; https://doi.org/10.3390/s19071595 - 02 Apr 2019
Cited by 1
Abstract
Robots frequently need to work in human environments and handle many different types of objects. There are two problems that make this challenging for robots: human environments are typically cluttered, and the multi-finger robot hand needs to grasp and to lift objects without [...] Read more.
Robots frequently need to work in human environments and handle many different types of objects. There are two problems that make this challenging for robots: human environments are typically cluttered, and the multi-finger robot hand needs to grasp and to lift objects without knowing their mass and damping properties. Therefore, this study combined vision and robot hand real-time grasp control action to achieve reliable and accurate object grasping in a cluttered scene. An efficient online algorithm for collision-free grasping pose generation according to a bounding box is proposed, and the grasp pose will be further checked for grasp quality. Finally, by fusing all available sensor data appropriately, an intelligent real-time grasp system was achieved that is reliable enough to handle various objects with unknown weights, friction, and stiffness. The robots used in this paper are the NTU 21-DOF five-finger robot hand and the NTU 6-DOF robot arm, which are both constructed by our Lab. Full article
(This article belongs to the Special Issue Tactile Sensors for Robotic Applications)
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Open AccessArticle
Design and Calibration of a Force/Tactile Sensor for Dexterous Manipulation
Sensors 2019, 19(4), 966; https://doi.org/10.3390/s19040966 - 25 Feb 2019
Abstract
This paper presents the design and calibration of a new force/tactile sensor for robotic applications. The sensor is suitably designed to provide the robotic grasping device with a sensory system mimicking the human sense of touch, namely, a device sensitive to contact forces, [...] Read more.
This paper presents the design and calibration of a new force/tactile sensor for robotic applications. The sensor is suitably designed to provide the robotic grasping device with a sensory system mimicking the human sense of touch, namely, a device sensitive to contact forces, object slip and object geometry. This type of perception information is of paramount importance not only in dexterous manipulation but even in simple grasping tasks, especially when objects are fragile, such that only a minimum amount of grasping force can be applied to hold the object without damaging it. Moreover, sensing only forces and not moments can be very limiting to securely grasp an object when it is grasped far from its center of gravity. Therefore, the perception of torsional moments is a key requirement of the designed sensor. Furthermore, the sensor is also the mechanical interface between the gripper and the manipulated object, therefore its design should consider also the requirements for a correct holding of the object. The most relevant of such requirements is the necessity to hold a torsional moment, therefore a soft distributed contact is necessary. The presence of a soft contact poses a number of challenges in the calibration of the sensor, and that is another contribution of this work. Experimental validation is provided in real grasping tasks with two sensors mounted on an industrial gripper. Full article
(This article belongs to the Special Issue Tactile Sensors for Robotic Applications)
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