scholarly journals Development of Fully Flexible Tactile Pressure Sensor with Bilayer Interlaced Bumps for Robotic Grasping Applications

Micromachines ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 770
Author(s):  
Lingfeng Zhu ◽  
Yancheng Wang ◽  
Deqing Mei ◽  
Chengpeng Jiang
Keyword(s):  
Pressure Sensor ◽  
Tactile Sensor ◽  
External Pressure ◽  
Robotic Hand ◽  
Sensing Element ◽  
Tactile Sensors ◽  
Human Machine Interaction ◽  
Tactile Information ◽  
Sensing Range ◽  
Intelligent Robotics

Flexible tactile sensors have been utilized in intelligent robotics for human-machine interaction and healthcare monitoring. The relatively low flexibility, unbalanced sensitivity and sensing range of the tactile sensors are hindering the accurate tactile information perception during robotic hand grasping of different objects. This paper developed a fully flexible tactile pressure sensor, using the flexible graphene and silver composites as the sensing element and stretchable electrodes, respectively. As for the structural design of the tactile sensor, the proposed bilayer interlaced bumps can be used to convert external pressure into the stretching of graphene composites. The fabricated tactile sensor exhibits a high sensing performance, including relatively high sensitivity (up to 3.40% kPa−1), wide sensing range (200 kPa), good dynamic response, and considerable repeatability. Then, the tactile sensor has been integrated with the robotic hand finger, and the grasping results have indicated the capability of using the tactile sensor to detect the distributed pressure during grasping applications. The grasping motions, properties of the objects can be further analyzed through the acquired tactile information in time and spatial domains, demonstrating the potential applications of the tactile sensor in intelligent robotics and human-machine interfaces.

scholarly journals Fully 3D Printed Flexible, Conformal and Multi-directional Tactile Sensor with Integrated Biomimetic and Auxetic Structure

2021 ◽  
Author(s):  
Yuyang Wei ◽  
Bingqian Li ◽  
Marco Domingos ◽  
Yiming Zhu ◽  
Lingyun Yan ◽  
...  
Keyword(s):  
Lumbar Vertebra ◽  
Tactile Sensor ◽  
Tactile Feedback ◽  
Robotic Hand ◽  
Tactile Sensors ◽  
Current Sensing ◽  
Sensing Range ◽  
Sensing Technology ◽  
3D Printed ◽  
Auxetic Structure

Abstract Tactile sensors are instrumental for developing the next generation of biologically inspired robotic prostheses with tactile feedback. Despite significant advancements made in current sensing technology, several limitations still exist including the reduced sensing sensitivity under high pressure, lack of compliance of the planar sensor with working surfaces and the demand for sophisticated manufacturing processes. In this study, we investigate the feasibility of using the 3D printing technology for the rapid and simple fabrication of a new conformal tactile sensor with an improved linear sensing range. The auxetic structure is integrated with a biomimetic inter-locked papilla feature which allows to detect multi-directional stimuli. Using the proposed design, the linear sensing range is extended to 0.5MPa and responsive to normal and shear forces with the sensitivities of 2.42KPa^(-1)and 2.20N^(-1) respectively. The proposed tactile sensor was printed on the fingertip of a prosthetic robotic hand to perform the sensorimotor control, or on the proximal femur head and lumbar vertebra for monitoring the bone-on-bone load. The results have shown promising application prospects of the proposed tactile sensor.

scholarly journals Evaluation of Circle Diameter by Distributed Tactile Information in Active Tracing

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Hiroyuki Nakamoto ◽  
Futoshi Kobayashi ◽  
Fumio Kojima
Keyword(s):  
Voluntary Movement ◽  
Synthesis Method ◽  
Tactile Sensor ◽  
Active Touch ◽  
Robotic Hand ◽  
Tactile Sensors ◽  
Robotic Hands ◽  
Tactile Information ◽  
Sensor Model ◽  
Circle Diameter

Active touch with voluntary movement on the surface of an object is important for human to obtain the local and detailed features on it. In addition, the active touch is considered to enhance the human spatial resolution. In order to improve dexterity performance of multifinger robotic hands, it is necessary to study an active touch method for robotic hands. In this paper, first, we define four requirements of a tactile sensor for active touch and design a distributed tactile sensor model, which can measure a distribution of compressive deformation. Second, we suggest a measurement process with the sensor model, a synthesis method of distributed deformations. In the experiments, a five-finger robotic hand with tactile sensors traces on the surface of cylindrical objects and evaluates the diameters. We confirm that the hand can obtain more information of the diameters by tracing the finger.

scholarly journals Magnetostrictive tactile sensor of detecting friction and normal force for object recognition

2020 ◽  
Vol 17 (4) ◽  
pp. 172988142093232
Author(s):  
Bing Zhang ◽  
Bowen Wang ◽  
Yunkai Li ◽  
Shaowei Jin
Keyword(s):  
Recognition Rate ◽  
Tactile Sensor ◽  
Tactile Sensing ◽  
Dynamic Force ◽  
Force Output ◽  
Tactile Sensors ◽  
Tactile Information ◽  
Normal Contact ◽  
New Type ◽  
Learning Machine

Tactile information is valuable in determining properties of objects that are inaccessible from visual perception. A new type of tangential friction and normal contact force magnetostrictive tactile sensor was developed based on the inverse magnetostrictive effect, and the force output model has been established. It can measure the exerted force in the range of 0–4 N, and it has a good response to the dynamic force in cycles of 0.25–0.5 s. We present a tactile perception strategy that a manipulator with tactile sensors in its grippers manipulates an object to measure a set of tactile features. It shows that tactile sensing system can use these features and the extreme learning machine algorithm to recognize household objects—purely from tactile sensing—from a small training set. The complex matrixes show the recognition rate is up to 83%.

scholarly journals Tactile Slippage Analysis in Optical Three-Axis Tactile Sensor for Robotic Hand

2013 ◽  
Vol 465-466 ◽  
pp. 1375-1379
Author(s):  
Hanafiah Yussof ◽  
Zahari Nur Ismarrubie ◽  
Ahmad Khushairy Makhtar ◽  
Masahiro Ohka ◽  
Siti Nora Basir
Keyword(s):  
Control Algorithm ◽  
Tactile Sensor ◽  
Robotic Hand ◽  
Robot Hand ◽  
Robot Arm ◽  
Tactile Sensors ◽  
Shear Forces ◽  
Improve Performance ◽  
Robot Arm Control ◽  
Two Phases

This paper presents experimental results of object handling motions to evaluate tactile slippage sensation in a multi fingered robot arm with optical three-axis tactile sensors installed on its two hands. The optical three-axis tactile sensor is a type of tactile sensor capable of defining normal and shear forces simultaneously. Shear force distribution is used to define slippage sensation in the robot hand system. Based on tactile slippage analysis, a new control algorithm was proposed. To improve performance during object handling motions, analysis of slippage direction is conducted. The control algorithm is classified into two phases: grasp-move-release and grasp-twist motions. Detailed explanations of the control algorithm based on the existing robot arm control system are presented. The experiment is conducted using a bottle cap, and the results reveal good performance of the proposed control algorithm to accomplish the proposed object handling motions.

scholarly journals Target Classification Method of Tactile Perception Data with Deep Learning

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1537
Author(s):  
Xingxing Zhang ◽  
Shaobo Li ◽  
Jing Yang ◽  
Qiang Bai ◽  
Yang Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Tactile Sensor ◽  
Tactile Perception ◽  
Machine Learning Algorithms ◽  
Target Classification ◽  
Clustering Method ◽  
Tactile Sensors ◽  
Residual Network ◽  
Tactile Information

In order to improve the accuracy of manipulator operation, it is necessary to install a tactile sensor on the manipulator to obtain tactile information and accurately classify a target. However, with the increase in the uncertainty and complexity of tactile sensing data characteristics, and the continuous development of tactile sensors, typical machine-learning algorithms often cannot solve the problem of target classification of pure tactile data. Here, we propose a new model by combining a convolutional neural network and a residual network, named ResNet10-v1. We optimized the convolutional kernel, hyperparameters, and loss function of the model, and further improved the accuracy of target classification through the K-means clustering method. We verified the feasibility and effectiveness of the proposed method through a large number of experiments. We expect to further improve the generalization ability of this method and provide an important reference for the research in the field of tactile perception classification.

scholarly journals Facile Fabrication of 3D Porous Sponges Coated with Synergistic Carbon Black/Multiwalled Carbon Nanotubes for Tactile Sensing Applications

Nanomaterials ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 1941 ◽  
Author(s):  
Yousef Al-Handarish ◽  
Olatunji Mumini Omisore ◽  
Wenke Duan ◽  
Jing Chen ◽  
Luo Zebang ◽  
...  
Keyword(s):  
Carbon Black ◽  
High Sensitivity ◽  
Tactile Sensor ◽  
Fast Response ◽  
Wearable Electronics ◽  
Tactile Sensors ◽  
Multiwalled Carbon ◽  
Human Machine Interaction ◽  
External Compression ◽  
Sensing Applications

Recently, flexible tactile sensors based on three-dimensional (3D) porous conductive composites, endowed with high sensitivity, a wide sensing range, fast response, and the capability to detect low pressures, have aroused considerable attention. These sensors have been employed in different practical domain areas such as artificial skin, healthcare systems, and human–machine interaction. In this study, a facile, cost-efficient method is proposed for fabricating a highly sensitive piezoresistive tactile sensor based on a 3D porous dielectric layer. The proposed sensor is designed with a simple dip-coating homogeneous synergetic conductive network of carbon black (CB) and multi-walled carbon nanotube (MWCNTs) composite on polydimethysiloxane (PDMS) sponge skeletons. The unique combination of a 3D porous structure, with hybrid conductive networks of CB/MWCNTs displayed a superior elasticity, with outstanding electrical characterization under external compression. The piezoresistive tactile sensor exhibited a high sensitivity of (15 kPa−1), with a rapid response time (100 ms), the capability of detecting both large and small compressive strains, as well as excellent mechanical deformability and stability over 1000 cycles. Benefiting from a long-term stability, fast response, and low-detection limit, the piezoresistive sensor was successfully utilized in monitoring human physiological signals, including finger heart rate, pulses, knee bending, respiration, and finger grabbing motions during the process of picking up an object. Furthermore, a comprehensive performance of the sensor was carried out, and the sensor’s design fulfilled vital evaluation metrics, such as low-cost and simplicity in the fabrication process. Thus, 3D porous-based piezoresistive tactile sensors could rapidly promote the development of high-performance flexible sensors, and make them very attractive for an enormous range of potential applications in healthcare devices, wearable electronics, and intelligent robotic systems.

scholarly journals A Soft Tactile Sensor Based on Magnetics and Hybrid Flexible-Rigid Electronics

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5098
Author(s):  
Miguel Neto ◽  
Pedro Ribeiro ◽  
Ricardo Nunes ◽  
Lorenzo Jamone ◽  
Alexandre Bernardino ◽  
...  
Keyword(s):  
Humanoid Robot ◽  
Spin Valve ◽  
Tactile Sensor ◽  
Contact Forces ◽  
Robotic Hand ◽  
Electronic Components ◽  
Tactile Sensors ◽  
Robotic Hands ◽  
Custom Made ◽  
Comsol Simulation

Tactile sensing is crucial for robots to manipulate objects successfully. However, integrating tactile sensors into robotic hands is still challenging, mainly due to the need to cover small multi-curved surfaces with several components that must be miniaturized. In this paper, we report the design of a novel magnetic-based tactile sensor to be integrated into the robotic hand of the humanoid robot Vizzy. We designed and fabricated a flexible 4 × 2 matrix of Si chips of magnetoresistive spin valve sensors that, coupled with a single small magnet, can measure contact forces from 0.1 to 5 N on multiple locations over the surface of a robotic fingertip; this design is innovative with respect to previous works in the literature, and it is made possible by careful engineering and miniaturization of the custom-made electronic components that we employ. In addition, we characterize the behavior of the sensor through a COMSOL simulation, which can be used to generate optimized designs for sensors with different geometries.

scholarly journals Magnetic-based Soft Tactile Sensors with Deformable Continuous Force Transfer Medium for Resolving Contact Locations in Robotic Grasping and Manipulation

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4925 ◽  
Author(s):  
Alireza Mohammadi ◽  
Yangmengfei Xu ◽  
Ying Tan ◽  
Peter Choong ◽  
Denny Oetomo
Keyword(s):  
Contact Force ◽  
Learning Algorithm ◽  
Design Procedure ◽  
Tactile Sensor ◽  
Design Parameters ◽  
Robotic Hand ◽  
Tactile Sensors ◽  
Hall Effect Sensor ◽  
Grasping And Manipulation ◽  
Sensor Configuration

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.

scholarly journals Flexible mechanical metamaterials enabling soft tactile sensors with multiple sensitivities at multiple force sensing ranges

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alireza Mohammadi ◽  
Ying Tan ◽  
Peter Choong ◽  
Denny Oetomo
Keyword(s):  
Degrees Of Freedom ◽  
Force Measurement ◽  
Design Procedure ◽  
Tactile Sensor ◽  
Design Parameters ◽  
Force Sensing ◽  
Tactile Sensors ◽  
Human Machine Interaction ◽  
Mechanical Metamaterials ◽  
Sensor Structure

AbstractThe majority of existing tactile sensors are designed to measure a particular range of force with a fixed sensitivity. However, some applications require tactile sensors with multiple task-relevant sensitivities at multiple ranges of force sensing. Inspired by the human tactile sensing capability, this paper proposes a novel soft tactile sensor based on mechanical metamaterials which exhibits multiple sensitivity regimes due to the step-by-step locking behaviour of its heterogenous multi-layered structure. By tuning the geometrical design parameters of the collapsible layers, each layer experiences locking behaviour under different ranges of force which provides different sensitivity of the sensor at different force magnitude. The integration of a magnetic-based transduction method with the proposed structure results in high design degrees of freedom for realising the desired contact force sensitivities and corresponding force sensing ranges. A systematic design procedure is proposed to select appropriate design parameters to produce the desired characteristics. Two example designs of the sensor structure were fabricated using widely available benchtop 3D printers and tested for their performance. The results showed the capability of the sensor in providing the desired characteristics in terms of sensitivity and force range and being realised in different shapes, sizes and number of layers in a single structure. The proposed multi-sensitivity soft tactile sensor has a great potential to be used in a wide variety of applications where different sensitivities of force measurement is required at different ranges of force magnitudes, from robotic manipulation and human–machine interaction to biomedical engineering and health-monitoring.

scholarly journals Using Tactile Sensing to Improve the Sample Efficiency and Performance of Deep Deterministic Policy Gradients for Simulated In-Hand Manipulation Tasks

2021 ◽  
Vol 8 ◽  
Author(s):  
Andrew Melnik ◽  
Luca Lach ◽  
Matthias Plappert ◽  
Timo Korthals ◽  
Robert Haschke ◽  
...  
Keyword(s):  
Ground Truth ◽  
Tactile Sensor ◽  
Limiting Factors ◽  
Tactile Sensors ◽  
Tactile Information ◽  
Touch Sensor ◽  
Learning Techniques ◽  
Dexterous Hand ◽  
And Performance

Deep Reinforcement Learning techniques demonstrate advances in the domain of robotics. One of the limiting factors is a large number of interaction samples usually required for training in simulated and real-world environments. In this work, we demonstrate for a set of simulated dexterous in-hand object manipulation tasks that tactile information can substantially increase sample efficiency for training (by up to more than threefold). We also observe an improvement in performance (up to 46%) after adding tactile information. To examine the role of tactile-sensor parameters in these improvements, we included experiments with varied sensor-measurement accuracy (ground truth continuous values, noisy continuous values, Boolean values), and varied spatial resolution of the tactile sensors (927 sensors, 92 sensors, and 16 pooled sensor areas in the hand). To facilitate further studies and comparisons, we make these touch-sensor extensions available as a part of the OpenAI Gym Shadow-Dexterous-Hand robotics environments.

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