Soft magnetic skin for super-resolution tactile sensing with force self-decoupling

2021 ◽  
Vol 6 (51) ◽  
pp. eabc8801
Author(s):  
Youcan Yan ◽  
Zhe Hu ◽  
Zhengbao Yang ◽  
Wenzhen Yuan ◽  
Chaoyang Song ◽  
...  
Keyword(s):  
External Disturbance ◽  
Super Resolution ◽  
Tactile Sensor ◽  
Human Robot Interaction ◽  
Tactile Sensors ◽  
Dexterous Manipulation ◽  
Robot Interaction ◽  
Flexible Film ◽  
Challenging Tasks ◽  
External Forces

Human skin can sense subtle changes of both normal and shear forces (i.e., self-decoupled) and perceive stimuli with finer resolution than the average spacing between mechanoreceptors (i.e., super-resolved). By contrast, existing tactile sensors for robotic applications are inferior, lacking accurate force decoupling and proper spatial resolution at the same time. Here, we present a soft tactile sensor with self-decoupling and super-resolution abilities by designing a sinusoidally magnetized flexible film (with the thickness ~0.5 millimeters), whose deformation can be detected by a Hall sensor according to the change of magnetic flux densities under external forces. The sensor can accurately measure the normal force and the shear force (demonstrated in one dimension) with a single unit and achieve a 60-fold super-resolved accuracy enhanced by deep learning. By mounting our sensor at the fingertip of a robotic gripper, we show that robots can accomplish challenging tasks such as stably grasping fragile objects under external disturbance and threading a needle via teleoperation. This research provides new insight into tactile sensor design and could be beneficial to various applications in robotics field, such as adaptive grasping, dexterous manipulation, and human-robot interaction.

scholarly journals Design of a Sensitive Balloon Sensor for Safe Human–Robot Interaction

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2163
Author(s):  
Dongjin Kim ◽  
Seungyong Han ◽  
Taewi Kim ◽  
Changhwan Kim ◽  
Doohoe Lee ◽  
...  
Keyword(s):  
High Performance ◽  
Strain Sensor ◽  
High Sensitivity ◽  
Human Robot Interaction ◽  
Large Area ◽  
Tactile Sensors ◽  
Robot Interaction ◽  
Mechanical Crack ◽  
Flexible Strain Sensor ◽  
Contact Sensing

As the safety of a human body is the main priority while interacting with robots, the field of tactile sensors has expanded for acquiring tactile information and ensuring safe human–robot interaction (HRI). Existing lightweight and thin tactile sensors exhibit high performance in detecting their surroundings. However, unexpected collisions caused by malfunctions or sudden external collisions can still cause injuries to rigid robots with thin tactile sensors. In this study, we present a sensitive balloon sensor for contact sensing and alleviating physical collisions over a large area of rigid robots. The balloon sensor is a pressure sensor composed of an inflatable body of low-density polyethylene (LDPE), and a highly sensitive and flexible strain sensor laminated onto it. The mechanical crack-based strain sensor with high sensitivity enables the detection of extremely small changes in the strain of the balloon. Adjusting the geometric parameters of the balloon allows for a large and easily customizable sensing area. The weight of the balloon sensor was approximately 2 g. The sensor is employed with a servo motor and detects a finger or a sheet of rolled paper gently touching it, without being damaged.

scholarly journals Towards natural handshakes for social robots: human-aware hand grasps using tactile sensors

2018 ◽  
Vol 9 (1) ◽  
pp. 221-234 ◽  
Author(s):  
João Avelino ◽  
Tiago Paulino ◽  
Carlos Cardoso ◽  
Ricardo Nunes ◽  
Plinio Moreno ◽  
...  
Keyword(s):  
Human Subjects ◽  
Tactile Feedback ◽  
Human Robot Interaction ◽  
Physical Interaction ◽  
Tactile Sensors ◽  
Cultural Backgrounds ◽  
Robot Interaction ◽  
Human Interactions ◽  
Hand Grip ◽  
Fundamental Part

Abstract Handshaking is a fundamental part of human physical interaction that is transversal to various cultural backgrounds. It is also a very challenging task in the field of Physical Human-Robot Interaction (pHRI), requiring compliant force control in order to plan the arm’s motion and for a confident, but at the same time pleasant grasp of the human user’s hand. In this paper,we focus on the study of the hand grip strength for comfortable handshakes and perform three sets of physical interaction experiments between twenty human subjects in the first experiment, thirty-five human subjects in the second one, and thirty-eight human subjects in the third one. Tests are made with a social robot whose hands are instrumented with tactile sensors that provide skin-like sensation. From these experiments, we: (i) learn the preferred grip closure according to each user group; (ii) analyze the tactile feedback provided by the sensors for each closure; (iii) develop and evaluate the hand grip controller based on previous data. In addition to the robot-human interactions, we also learn about the robot executed handshake interactions with inanimate objects, in order to detect if it is shaking hands with a human or an inanimate object. This work adds physical human-robot interaction to the repertory of social skills of our robot, fulfilling a demand previously identified by many users of the robot.

scholarly journals Robust Region Tracking Control for a Robot Manipulator

2021 ◽  
Author(s):  
xiangyun Li ◽  
QI LU ◽  
Zhaoyang Chen ◽  
Qinlin Yang ◽  
Kang Li
Keyword(s):  
Trajectory Tracking ◽  
Model Uncertainty ◽  
Tracking Control ◽  
Robot Manipulator ◽  
External Disturbance ◽  
Human Robot Interaction ◽  
Moving Target ◽  
Robot Interaction ◽  
Target Region ◽  
Region Tracking

n this work, the uncertainty and disturbance estimator (UDE)-based robust region reaching controller for a robot manipulator is developed to achieve the moving target region trajectory tracking and complaint human robot interaction inside the target region. The regional feedback error is derived from the potential function to drive the robot manipulator end effector converging into the target region. Under the back-stepping control framework, the UDE is fused into the region tracking control law to estimate and compensate the model uncertainty and external disturbance. Both simulation and experimental studies are carried out with a universal robots (UR) 10 manipulator to demonstrate the effectiveness of the proposed method for moving target trajectory tracking, model uncertainty and external disturbance rejection, and compliant human robot interaction within the target region.

scholarly journals Robust Region Tracking Control for a Robot Manipulator

2021 ◽  
Author(s):  
xiangyun Li ◽  
QI LU ◽  
Zhaoyang Chen ◽  
Qinlin Yang ◽  
Kang Li
Keyword(s):  
Trajectory Tracking ◽  
Model Uncertainty ◽  
Tracking Control ◽  
Robot Manipulator ◽  
External Disturbance ◽  
Human Robot Interaction ◽  
Moving Target ◽  
Robot Interaction ◽  
Target Region ◽  
Region Tracking

n this work, the uncertainty and disturbance estimator (UDE)-based robust region reaching controller for a robot manipulator is developed to achieve the moving target region trajectory tracking and complaint human robot interaction inside the target region. The regional feedback error is derived from the potential function to drive the robot manipulator end effector converging into the target region. Under the back-stepping control framework, the UDE is fused into the region tracking control law to estimate and compensate the model uncertainty and external disturbance. Both simulation and experimental studies are carried out with a universal robots (UR) 10 manipulator to demonstrate the effectiveness of the proposed method for moving target trajectory tracking, model uncertainty and external disturbance rejection, and compliant human robot interaction within the target region.

scholarly journals NAO-Teach: helping kids to learn societal and theoretical knowledge with friendly human-robot interaction

2020 ◽  
Vol 17 (3) ◽  
pp. 1657
Author(s):  
Ahmad Hoirul Basori
Keyword(s):  
Tactile Sensor ◽  
Human Robot Interaction ◽  
The Body ◽  
Theoretical Knowledge ◽  
Body Parts ◽  
Learning Method ◽  
Robotic Technology ◽  
Robot Interaction ◽  
Left Hand ◽  
Vision Processing

<span lang="EN-GB">Robotic technology has affected the education field, and even early education involves robot to attract Kids. Technical education is the notion of giving students knowledge of robots and technology. The main contribution of our research is to provide an interactive way of learning for kids through play and fun method. Two approaches proposed here: first, we provide an interactive game by touching robots body parts to teach kids how they were practising their motoric nerve and the listening to the instruction. In this game, kids asked to find some robot parts such as right hand, or left hand, where it equipped with a tactile sensor. The game difficulty can be increased by setting up the time limit for the answer and make kids touch the body parts of the robot very fast. The second learning method is practising number counting and pronunciation with NAO Robots. The robots will do computer vision processing to analyse and pronounce the kids handwriting with an artificial neural network. The result of implementation has obtained more than 75% success rate on recognition part with loss es than 0.6. The system received strong appreciation from kids and their parent, while  This research believed able to attract kids to study in interactive and fun ways.</span>

scholarly journals Uncertainty-Aware Knowledge Distillation for Collision Identification of Collaborative Robots

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6674
Author(s):  
Wookyong Kwon ◽  
Yongsik Jin ◽  
Sang Jun Lee
Keyword(s):  
Neural Network ◽  
Collision Detection ◽  
Deep Neural Network ◽  
Human Robot Interaction ◽  
Learning Method ◽  
Robot Interaction ◽  
Collaborative Robots ◽  
Knowledge Distillation ◽  
Articulated Robots ◽  
External Forces

Human-robot interaction has received a lot of attention as collaborative robots became widely utilized in many industrial fields. Among techniques for human-robot interaction, collision identification is an indispensable element in collaborative robots to prevent fatal accidents. This paper proposes a deep learning method for identifying external collisions in 6-DoF articulated robots. The proposed method expands the idea of CollisionNet, which was previously proposed for collision detection, to identify the locations of external forces. The key contribution of this paper is uncertainty-aware knowledge distillation for improving the accuracy of a deep neural network. Sample-level uncertainties are estimated from a teacher network, and larger penalties are imposed for uncertain samples during the training of a student network. Experiments demonstrate that the proposed method is effective for improving the performance of collision identification.

scholarly journals Cost-Efficient Flexible Supercapacitive Tactile Sensor With Superior Sensitivity and High Spatial Resolution for Human-Robot Interaction

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 64836-64845
Author(s):  
Jixiao Liu ◽  
Manfei Wang ◽  
Peng Wang ◽  
Funing Hou ◽  
Chuizhou Meng ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Tactile Sensor ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Cost Efficient
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