Dynamic Tactile Sensor Research Articles

Multimodal Fibrous Static and Dynamic Tactile Sensor.

A highly versatile, low-cost, and robust tactile sensor capable of acquiring load measurements under static and dynamic modes employing a poly(vinylidene fluoride-co-trifluoroethylene) [P(VDF-TrFE)] micronanofiber element is presented. The sensor is comprised of three essential layers, a fibrous core P(VDF-TrFE) layer and two Ni/Cu conductive fabric electrode layers, with a total thickness of less than 300 μm. Using an in situ electrospinning process, the core fibers are deposited directly to a soft poly(dimethylsiloxane) (PDMS) fingertip. The core layer conforms to the surface and requires no additional processing, exhibiting the capability of the in situ electrospinning fabrication method to alleviate poor surface contacts and resolve issues associated with adhesion. The fabricated tactile sensor displayed a reliable and consistent measurement performance of static and instantaneous dynamic loads over a total of 30 000 test cycles. The capabilities and implications of the presented tactile sensor design for multimodal sensing in robot tactile sensing applications is further discussed and elucidated.

Dynamic Piezoelectric Tactile Sensor for Tissue Hardness Measurement Using Symmetrical Flexure Hinges and Anisotropic Vibration Modes

This paper presents a novel dynamic tactile sensor with a symmetrical longitudinal piezoelectric cantilever structure, which is different from the conventional transverse cantilever structure. It has a radical size of 1.1 centimeters. A flexible hinge chain and mass probe are introduced into the structure to reduce the sensor’s natural frequency to improve the estimation accuracy of tissue hardness. Double piezoelectric patches, symmetrically distributed in the sensor, are used as the actuator to generate vibration and as the sensor for resonance detection. The sensor’s basic working principle is derived mathematically, followed by simulation verification of statics analysis for safety consideration, anisotropic vibration mode analysis, harmonic response analysis, and a calibration experiment to establish fitting curves. The threshold point is set as 192 Hz/0.6 MPa in the calibration curve, and the hardness range is divided into a soft range (0–0.6 MPa) with the sensitivity of 9.75 Hz/MPa, which could realize hardness measurement of soft tissue and normal tissue, and a hard range (which exceeds 0.6 MPa) with higher frequency to realize the identification of the lesion tissue. Finally, the sensor prototype is tested on biological tissue (fresh pig liver) to verify that the calibration curve with the selected threshold is effective for identifying lesion areas and that the sensor prototype has a function of hardness measurement with a relatively stable sensitivity and lesion identification in its operating frequency range.

Fingerprint‐Enhanced Capacitive‐Piezoelectric Flexible Sensing Skin to Discriminate Static and Dynamic Tactile Stimuli

Inspired by the structure and functions of the human skin, a highly sensitive capacitive‐piezoelectric flexible sensing skin with fingerprint‐like patterns to detect and discriminate between spatiotemporal tactile stimuli including static and dynamic pressures and textures is presented. The capacitive‐piezoelectric tandem sensing structure is embedded in the phalange of a 3D‐printed robotic hand, and a tempotron classifier system is used for tactile exploration. The dynamic tactile sensor, interfaced with an extended gate configuration to a common source metal oxide semiconductor field effect transistor (MOSFET), exhibits a sensitivity of 2.28 kPa−1. The capacitive sensing structure has nonlinear characteristics with sensitivity varying from 0.25 kPa−1 in the low‐pressure range (<100 Pa) to 0.002 kPa−1 in high pressure (≈2.5 kPa). The output from the presented sensor under a closed‐loop tactile scan, carried out with an industrial robotic arm, is used as latency‐coded spike trains in a spiking neural network (SNN) tempotron classifier system. With the capability of performing a real‐time binary naturalistic texture classification with a maximum accuracy of 99.45%, the presented bioinspired skin finds applications in robotics, prosthesis, wearable sensors, and medical devices.

A Variable-Impedance Tactile Sensor With Online Performance Tuning for Tissue Hardness Palpation in Robot-Assisted Minimally Invasive Surgery.

This paper presents a novel tactile sensor whose sensing performance (sensitivity, range, etc.) can be tuned online by varying its mechanical impedance with a double-cantilever structure. Therefore, it can solve the problems of narrow sensing range and non-optimal sensitivity faced by existing dynamic tactile sensors. The sensor is driven by a piezoelectric patch which acts simultaneously as both the actuator to generate vibration and the sensor to detect resonance from its electrical impedance. Its basic working principle and the variable-impedance method for performance tuning are explained mathematically and verified by numerical simulations and experimental studies. The proposed variable-impedance methodology can inspire the designs of a new class of tactile sensors for applications in robotics, biomedical engineering, aerospace, etc.

Slip classification for dynamic tactile array sensors

The manipulation of objects held in a robotic hand or gripper is accompanied by events such as making and breaking contact and slippage, between the fingertips and the grasped object and between the grasped object and external surfaces. Humans can distinguish among such events, in part, because they excite the various mechanoreceptors in the hands differently. As part of an effort to provide robots with a similar capability, we propose two features that can be extracted from dynamic tactile array data and used to discriminate between hand/object and object/world slips. Both features rely on examining how slippage affects an array of dynamic tactile sensors compared with the way it affects individual elements of the array. In comparison with approaches that require extensive training with particular combinations of objects and skin, the features work for a wide range of frequencies and grasp conditions. The performance and generalizability of the features are verified with testing on three different kinds of sensors and for a range of object textures, grasp forces and slip conditions. Both features demonstrate greater than 85% accuracy in identifying the location of slip.

A dynamic tactile sensor on photoelastic effect

Certain photoelastic materials exhibit birefringent characteristics at a very low level of strain. This property of material may be suitable for dynamic or wave propagation studies, which can be exploited for designing tactile sensors. This paper presents the design, construction and testing of a novel dynamic sensor based on photoelastic effect, which is capable of detecting object slip as well as providing normal force information. The paper investigates the mechanics of object slip, and develops an approximate model of the sensor. This allows visualization of various parameters involved in the sensor design. The model also explains design improvements necessary to obtain continuous signal during object slip. The developed sensor has been compared with other existing sensors and experimental results from the sensor have been discussed. The sensor is calibrated for normal force which is in addition to the dynamic signal that it provides from the same contact location. The sensor has a simple design and is of a small size allowing it to be incorporated into robotic fingers, and it provides output signals which are largely unaffected by external disturbances.

물체의 안정한 조작을 위한 동작의 계획과 운동의 실현

This paper presents a motion planning and control method for the dexterous manipulation with a robotic hand. For a given trajectory of an object, a simulation system calculates the necessary joint displacements and contact forces at the fingertip surfaces. These joint displacements and contact forces are the reference inputs to the control loops of the robotic fingers. A task is decomposed into a set of primitive motions, and each primitive motion is executed using the planned output of the simulation system as the reference. Force sensors and dynamic tactile sensors are used to adapt to errors and uncertainties encountered during manipulation. Several experimental results are presented.

柔軟皮膚上に分離配置可能な動的触覚センサ素子の開発

柔軟皮膚上に分離配置可能な動的触覚センサ素子の開発

Dynamic tactile sensing: perception of fine surface features with stress rate sensing

Dynamic tactile sensing, which is defined as sensing during motion for perception of high spatial and temporal frequencies, is presented. Applications include sensing fine surface features and textures and monitoring contact conditions for dextrous manipulation. One type of dynamic tactile sensor, the stress rate sensor, is described in detail. It uses piezoelectric polymer transducers to measure the changes in stress induced in the sensor's rubber skin as it traverses small surface features and textures. The signals are interpreted with the aid of a solid mechanics model of the contact interaction and a linear deconvolution filter. Experimental verification of the sensor's performance, including the detection of surface features only 6.5 mu m high, are presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Tactile sensing and control of robotic manipulation

—This paper reviews the current state of the art and predicts the outlook in robotic tactile sensing for real-time control of dextrous manipulation. We begin with an overview of human touch sensing capabilities and draw lessons for robotic manipulation. Next, tactile sensor devices are described, including tactile array sensors, force-torque sensors, and dynamic tactile sensors. The information provided by these devices can be used in manipulation in many ways, such as finding contact locations and object shape, measuring contact forces, and determining contact conditions. Finally, recent progress in experimental use of tactile sensing in manipulation is discussed, and future directions for research in sensing and control are considered.