scholarly journals Experimental Evaluation of Tactile Sensors for Compliant Robotic Hands

2021 ◽  
Vol 8 ◽  
Author(s):  
Werner A. Friedl ◽  
Máximo A. Roa
Keyword(s):  
Sensor Array ◽  
Fruits And Vegetables ◽  
Variable Stiffness ◽  
Gravity Compensation ◽  
Control Loop ◽  
Tactile Sensors ◽  
Robotic Hands ◽  
Pick And Place ◽  
Human Capability ◽  
Sense Of Touch

The sense of touch is a key aspect in the human capability to robustly grasp and manipulate a wide variety of objects. Despite many years of development, there is still no preferred solution for tactile sensing in robotic hands: multiple technologies are available, each one with different benefits depending on the application. This study compares the performance of different tactile sensors mounted on the variable stiffness gripper CLASH 2F, including three commercial sensors: a single taxel sensor from the companies Tacterion and Kinfinity, the Robotic Finger Sensor v2 from Sparkfun, plus a self-built resistive 3 × 3 sensor array, and two self-built magnetic 3-DoF touch sensors, one with four taxels and one with one taxel. We verify the minimal force detectable by the sensors, test if slip detection is possible with the available taxels on each sensor, and use the sensors for edge detection to obtain the orientation of the grasped object. To evaluate the benefits obtained with each technology and to assess which sensor fits better the control loop in a variable stiffness hand, we use the CLASH gripper to grasp fruits and vegetables following a published benchmark for pick and place operations. To facilitate the repetition of tests, the CLASH hand is endowed with tactile buttons that ease human–robot interactions, including execution of a predefined program, resetting errors, or commanding the full robot to move in gravity compensation mode.

scholarly journals A Vibrissa-Inspired Highly Flexible Tactile Sensor: Scanning 3D Object Surfaces Providing Tactile Images

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1572
Author(s):  
Lukas Merker ◽  
Joachim Steigenberger ◽  
Rafael Marangoni ◽  
Carsten Behn
Keyword(s):  
Steel Wire ◽  
Sense Organ ◽  
Tactile Sensor ◽  
Tactile Sensors ◽  
Measuring Device ◽  
3D Object ◽  
Torque Transducer ◽  
Sense Of Touch ◽  
Important Basis ◽  
Support Reactions

Just as the sense of touch complements vision in various species, several robots could benefit from advanced tactile sensors, in particular when operating under poor visibility. A prominent tactile sense organ, frequently serving as a natural paragon for developing tactile sensors, is the vibrissae of, e.g., rats. Within this study, we present a vibrissa-inspired sensor concept for 3D object scanning and reconstruction to be exemplarily used in mobile robots. The setup consists of a highly flexible rod attached to a 3D force-torque transducer (measuring device). The scanning process is realized by translationally shifting the base of the rod relative to the object. Consequently, the rod sweeps over the object’s surface, undergoing large bending deflections. Then, the support reactions at the base of the rod are evaluated for contact localization. Presenting a method of theoretically generating these support reactions, we provide an important basis for future parameter studies. During scanning, lateral slip of the rod is not actively prevented, in contrast to literature. In this way, we demonstrate the suitability of the sensor for passively dragging it on a mobile robot. Experimental scanning sweeps using an artificial vibrissa (steel wire) of length 50 mm and a glass sphere as a test object with a diameter of 60 mm verify the theoretical results and serve as a proof of concept.

Toward the total coverage of joints of robotic hands with tactile sensors

2018 ◽  
Vol 2018 (0) ◽  
pp. 1P2-G14
Author(s):  
Kosuke Kusayanagi ◽  
Kyo Shinsei ◽  
Satoshi Funabashi ◽  
Alexander Schmitz ◽  
Shigeki Sugano
Keyword(s):  
Tactile Sensors ◽  
Robotic Hands ◽  
Total Coverage

Biomimetics Microfluidic Tactile Sensor Array

2008 ◽  
Author(s):  
Wei-Yu Tseng ◽  
Jefferey S. Fisher ◽  
Javier L. Prieto ◽  
Kentaro Rinaldi ◽  
Abraham P. Lee
Keyword(s):  
Minimally Invasive Surgery ◽  
Minimally Invasive ◽  
Physical Properties ◽  
Invasive Surgery ◽  
Visual Information ◽  
Sensor Array ◽  
Tactile Sensor ◽  
Prosthetic Hand ◽  
Tactile Sensors ◽  
Robotic Sensing

Tactile sensors are the interfaces to detect the physical properties of objects and have extensive applications in robotic sensing, biomechanics, minimally invasive surgery and human prosthetics [1]. For human prosthetics applications, the current prosthetic hand can offer only the manipulation function. With the sensing being part of the prosthetic hand, the user can get feedback from the prosthetic. This feeling can help users decrease their dependency on visual information and have better body control on weight balancing and signal limb stance.

scholarly journals Soft Biomimetic Optical Tactile Sensing with the TacTip: A Review

2021 ◽  
Author(s):  
Nathan Lepora
Keyword(s):  
Tactile Sensor ◽  
Step Change ◽  
Tactile Sensors ◽  
The Past ◽  
Robot Hands ◽  
Human Touch ◽  
Skin Morphology ◽  
Sense Of Touch ◽  
And Control ◽  
Human Sense

<div>Reproducing the capabilities of the human sense of touch in machines is an important step in enabling robot manipulation to have the ease of human dexterity. A combination of robotic technologies will be needed, including soft robotics, biomimetics and the high-resolution sensing offered by optical tactile sensors. This combination is considered here as a SoftBOT (Soft Biomimetic Optical Tactile) sensor. This article reviews the BRL TacTip as a prototypical example of such a sensor. Topics include the relation between artificial skin morphology and the transduction principles of human touch, the nature and benefits of tactile shear sensing, 3D printing for fabrication and integration into robot hands, the application of AI to tactile perception and control, and the recent step-change in capabilities due to deep learning. This review consolidates those advances from the past decade to indicate a path for robots to reach human-like dexterity.</div><div><br></div>

Performing Energy-Efficient Pick-and-Place Motions for High-Speed Robots by using Variable Stiffness Springs

2021 ◽  
pp. 1-12
Author(s):  
Rafael Balderas Hill ◽  
Sebastien Briot ◽  
Abdelhamid Chriette ◽  
Philippe Martinet
Keyword(s):  
High Speed ◽  
Variable Stiffness ◽  
Robot Dynamics ◽  
Suggested Approach ◽  
High Speeds ◽  
Pick And Place ◽  
Operational Phase ◽  
Parallel Configuration ◽  
Braking Phase ◽  
Displacement Phase

Abstract Typically, for pick-and-place robots operating at high speeds, an enormous amount of energy is lost during the robot braking phase. This is due to the fact that, during such operational phase, most of the energy is dissipated as heat on the braking resistances of the motor drivers. In order to increase the energy-efficiency during the high-speed pick-and-place cycles, this paper investigates the use of variable stiffness springs (VSS) in parallel configuration with the motors. These springs store the energy during the braking phase, instead of dissipating it. The energy is then released to actuate the robot in a next displacement phase. This design approach is combined with a motion generator which seeks to optimize trajectories for input torques reduction (and thus of energy consumption), through solving a boundary value problem (BVP) based on the robot dynamics. Experimental results of the suggested approach on a five-bar mechanism show the drastic reduction of input torques, and therefore of energetic losses.

Application of adaptive neural network to localization of objects using pressure array transducer

Robotica ◽  
1996 ◽  
Vol 14 (4) ◽  
pp. 407-414 ◽  
Author(s):  
Anderson Leung ◽  
Shahram Payandeh
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Sensor Array ◽  
Pressure Sensors ◽  
Object Localization ◽  
Tactile Sensors ◽  
Neural Network Approach ◽  
Array Transducer ◽  
The Neural Network ◽  
Fixed Reference

SUMMARYPattern recognition and object localization, using various sensors such as vision and tactile sensors, are two important areas of research in the application of robotic systems. This paper demonstrates the feasibility of using some relatively inexpensive array of pressure sensors and a neural network approach to achieve object localization and pattern recognition. The sensors that are used are force sensing resistors (FSRs), more specifically, a 16 x 16 array of FSRs. Because of the nonlinearity associated with a FSR, three possible approaches for gathering output from the sensor array are used. The neural network that is used consists of two 2-layer counterpropagation networks (CPNs). One of the CPNs is trained to recognize contact signatures of different objects placed on a fixed reference position on the sensor array.

Dextrous Hands: Human, Prosthetic, and Robotic

1997 ◽  
Vol 6 (1) ◽  
pp. 29-56 ◽  
Author(s):  
Lynette Jones
Keyword(s):  
Sensory Processing ◽  
Human Hand ◽  
Robotic Hands ◽  
Prosthetic Hands ◽  
Robot Hands ◽  
Natural Sense ◽  
Manual Skills ◽  
Sense Of Touch ◽  
Manual Performance ◽  
Dextrous Hands

The sensory and motor capacities of the human hand are reviewed in the context of providing a set of performance characteristics against which prosthetic and dextrous robot hands can be evaluated. The sensors involved in processing tactile, thermal, and proprioceptive (force and movement) information are described, together with details on their spatial densities, sensitivity, and resolution. The wealth of data on the human hand's sensory capacities is not matched by an equivalent database on motor performance. Attempts at quantifying manual dexterity have met with formidable technological difficulties due to the conditions under which many highly trained manual skills are performed. Limitations in technology have affected not only the quantifying of human manual performance but also the development of prosthetic and robotic hands. Most prosthetic hands in use at present are simple grasping devices, and imparting a “natural” sense of touch to these hands remains a challenge. Several dextrous robot hands exist as research tools and even though some of these systems can outperform their human counterparts in the motor domain, they are still very limited as sensory processing systems. It is in this latter area that information from studies of human grasping and processing of object information may make the greatest contribution.

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