Estimation of Care Receiver’s Position Based on Tactile Information for Transfer Assist Using Dual Arm Robot

2014 ◽  
Vol 26 (6) ◽  
pp. 743-749 ◽  
Author(s):  
Yuki Mori ◽  
◽  
Ryojun Ikeura ◽  
Ming Ding ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Nursing Care ◽  
Tactile Sensor ◽  
Position Estimation ◽  
Sensor Data ◽  
Tactile Sensors ◽  
Tactile Information ◽  
Dual Arm Robot ◽  
Dual Arm ◽  
Experience Pain

<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00260006/07.jpg"" width=""300"" />Position estimation by forearms</div> For a robot that uses two arms to lift and transfer a care receiver from a bed to a wheelchair, we report a method of estimating the positioning of the care receiver. The maneuver for such a task involves a high DOF, and the robot is capable of executing the maneuver much like a human being. The care receiver may experience pain or become unstable when being carried, however, depending on the positioning of contact between the robot’s arms and the care receiver. For this reason, nursing care robots must be able to recognize the positioning of contact with the care receiver and either modify it or alert the operator if it is unsuitable. We use the information obtained by tactile sensors on the robot’s arms when making contact with the care receiver to estimate the latter’s positioning. By dividing a care receiver’s position on a bed into nine zones and applying machine learning to tactile sensor data and positioning, it is possible to estimate positioning highly accurately. </span>

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 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 3D Contact Position Estimation of Image-Based Areal Soft Tactile Sensor with Printed Array Markers and Image Sensors

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3796
Author(s):  
Jong-il Lee ◽  
Suwoong Lee ◽  
Hyun-Min Oh ◽  
Bo Ram Cho ◽  
Kap-Ho Seo ◽  
...  
Keyword(s):  
Tactile Sensor ◽  
Estimation Algorithm ◽  
Industrial Applications ◽  
Position Estimation ◽  
Image Sensors ◽  
Multiple Cameras ◽  
Tactile Sensors ◽  
Input Devices ◽  
The Stability ◽  
Contact Position

Tactile sensors have been widely used and researched in various fields of medical and industrial applications. Gradually, they will be used as new input devices and contact sensors for interactive robots. If a tactile sensor is to be applied to various forms of human–machine interactions, it needs to be soft to ensure comfort and safety, and it should be easily customizable and inexpensive. The purpose of this study is to estimate 3D contact position of a novel image-based areal soft tactile sensor (IASTS) using printed array markers and multiple cameras. First, we introduce the hardware structure of the prototype IASTS, which consists of a soft material with printed array markers and multiple cameras with LEDs. Second, an estimation algorithm for the contact position is proposed based on the image processing of the array markers and their Gaussian fittings. A series of basic experiments was conducted and their results were analyzed to verify the effectiveness of the proposed IASTS hardware and its estimation software. To ensure the stability of the estimated contact positions a Kalman filter was developed. Finally, it was shown that the contact positions on the IASTS were estimated with a reasonable error value for soft haptic applications.

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.

Realization and Safety Measures of Patient Transfer by Nursing-Care Assistant Robot RIBA with Tactile Sensors

2011 ◽  
Vol 23 (3) ◽  
pp. 360-369 ◽  
Author(s):  
Toshiharu Mukai ◽  
◽  
Shinya Hirano ◽  
Hiromichi Nakashima ◽  
Yuki Sakaida ◽  
...  
Keyword(s):  
Information Processing ◽  
Nursing Care ◽  
Tactile Feedback ◽  
Patient Transfer ◽  
Tactile Sensors ◽  
Safety Measures ◽  
Tactile Information ◽  
Challenging Tasks ◽  
Aging Societies ◽  
Bedridden Patient

In aging societies, there is a strong demand for robotics to tackle with problems caused by the aging population. Patient transfer, such as lifting and moving a bedridden patient from a bed to a wheelchair and back, is one of the most physically challenging tasks in nursing care, the burden of which should be reduced by the introduction of robot technologies. To this end, we have developed a new prototype robot named RIBA having human-type arms with tactile sensors. RIBA succeeded in transferring a human from a bed to a wheelchair and back. The tactile sensors play important roles in sensor feedback and detection of instructions from the operator. In this paper, after outlining the concept and specifications of RIBA, we will explain the tactile information processing, its application to tactile feedback and instruction detection, and safety measures to realize patient transfer. The results of patient transfer experiments are also reported.

scholarly journals Pouring from Deformable Containers Based on Tactile Information Using a Dual-Arm Robot

2019 ◽  
Vol 1 (2) ◽  
pp. 123-143
Author(s):  
Yuto Tsuchiya
Keyword(s):  
Success Rate ◽  
Motion Capture ◽  
The Other ◽  
Motion Capture System ◽  
Breakfast Cereal ◽  
Tactile Information ◽  
Coffee Beans ◽  
Arm Motion ◽  
Dual Arm Robot ◽  
Dual Arm

In this paper, we consider household robots that pour various contents from deformable containers. Such pouring is often seen in cooking and refilling. To achieve this kind of pouring, we reduce the deformation of the container during pouring and thus carefully design the grasping strategy: the palm of one hand supports the deformable container from the bottom and the other hand pulls up the container from the top. We apply the proposed system to pouring four different kinds of contents: breakfast cereal, coffee beans, flour, and rice. The experiment verifies that the proposed system successfully pours the four contents. To evaluate the system quantitatively, we measure 1) the deformation of the container using a motion capture system and 2) the success rate of pouring. We verify that the dual-arm pouring reduced the deformation by 66% compared to a single-arm motion and that the success rate is greater than 90%.

scholarly journals Tactile Sensor Data Interpretation for Estimation of Wire Features

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1458
Author(s):  
Andrea Cirillo ◽  
Gianluca Laudante ◽  
Salvatore Pirozzi
Keyword(s):  
Recognition Rate ◽  
Experimental Tests ◽  
Tactile Sensor ◽  
Data Interpretation ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Tactile Sensors ◽  
Straightforward Method ◽  
Place Task ◽  
Linear Object

At present, the tactile perception is essential for robotic applications when performing complex manipulation tasks, e.g., grasping objects of different shapes and sizes, distinguishing between different textures, and avoiding slips by grasping an object with a minimal force. Considering Deformable Linear Object manipulation applications, this paper presents an efficient and straightforward method to allow robots to autonomously work with thin objects, e.g., wires, and to recognize their features, i.e., diameter, by relying on tactile sensors developed by the authors. The method, based on machine learning algorithms, is described in-depth in the paper to make it easily reproducible by the readers. Experimental tests show the effectiveness of the approach that is able to properly recognize the considered object’s features with a recognition rate up to 99.9%. Moreover, a pick and place task, which uses the method to classify and organize a set of wires by diameter, is presented.

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 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.

Binary Spectrum Feature for Improved Classifier Performance

2020 ◽  
Author(s):  
Nalika Ulapane ◽  
Karthick Thiyagarajan ◽  
sarath kodagoda
Keyword(s):  
Machine Learning ◽  
Classification Performance ◽  
Feature Reduction ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Monitoring Task ◽  
Classifier Performance ◽  
Spectrum Feature

<div>Classification has become a vital task in modern machine learning and Artificial Intelligence applications, including smart sensing. Numerous machine learning techniques are available to perform classification. Similarly, numerous practices, such as feature selection (i.e., selection of a subset of descriptor variables that optimally describe the output), are available to improve classifier performance. In this paper, we consider the case of a given supervised learning classification task that has to be performed making use of continuous-valued features. It is assumed that an optimal subset of features has already been selected. Therefore, no further feature reduction, or feature addition, is to be carried out. Then, we attempt to improve the classification performance by passing the given feature set through a transformation that produces a new feature set which we have named the “Binary Spectrum”. Via a case study example done on some Pulsed Eddy Current sensor data captured from an infrastructure monitoring task, we demonstrate how the classification accuracy of a Support Vector Machine (SVM) classifier increases through the use of this Binary Spectrum feature, indicating the feature transformation’s potential for broader usage.</div><div><br></div>

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