Industrial Grippers Research Articles

Modeling Abduction Mechanism of Soft Actuator with Elastica for Robotic Hands

Abstract Soft actuators, composed of pliable materials, are increasingly adopted in industrial grippers owing to their inherent flexibility, elasticity, and safety attributes, rendering them conducive for anthropomorphic robotic applications. Notably, a discernible void in extant literature pertains to the nuanced exploration of hand abduction movements. Addressing this lacuna, the present investigation delineates three salient contributions. Primarily, it introduces the Abduction Soft-Actuator (ASA) – an innovative design specifically tailored for robotic hand abduction. Secondly, it posits an analytical framework augmented by Large Deformation Virtual Beam (LDVB) theory for soft Elastica, facilitating a rigorous dissection of the intrinsic physical properties of the actuator's internal membrane. Thirdly, the study underscores the versatility of the ASA, drawing attention to its innate capability to seamlessly integrate membranes and springs, extending its applicability across multifarious design paradigms. Empirical findings accentuate the ASA's proficiency in prognosticating operational angles under an assortment of spring conditions, thereby refining spring selection protocols for a gamut of applications. Diverging from traditional soft actuators which predominantly deploy a singular material, the ASA epitomizes modularity, allowing for facile spring alternations to cater to diversified requirements. In juxtaposition with prevailing case-by-case analytical approaches, the ASA conspicuously amplifies its domain of applicability. Validation experiments employing inflated silicone membranes substantiate the LDVB theoretical framework, intimating those estimations, reliant on empirical metrics, are amenable to analytical prognostication. Collectively, this methodological intervention not only bridges the prevailing technological chasm but also enriches the comprehension matrix associated with soft actuator mechanism across myriad applications.

Quasi-Static Modeling Framework for Soft Bellow-Based Biomimetic Actuators.

Soft robots that incorporate elastomeric matrices and flexible materials have gained attention for their unique capabilities, surpassing those of rigid robots, with increased degrees of freedom and movement. Research has highlighted the adaptability, agility, and sensitivity of soft robotic actuators in various applications, including industrial grippers, locomotive robots, wearable assistive devices, and more. It has been demonstrated that bellow-shaped actuators exhibit greater efficiency compared to uniformly shaped fiber-reinforced actuators as they require less input pressure to achieve a comparable range of motion (ROM). Nevertheless, the mathematical quantification of the performance of bellow-based soft fluidic actuators is not well established due to their inherent non-uniform and complex structure, particularly when compared to fiber-reinforced actuators. Furthermore, the design of bellow dimensions is mostly based on intuition without standardized guidance and criteria. This article presents a comprehensive description of the quasi-static analytical modeling process used to analyze bellow-based soft actuators with linear extension. The results of the models are validated through finite element method (FEM) simulations and experimental testing, considering elongation in free space under fluidic pressurization. This study facilitates the determination of optimal geometrical parameters for bellow-based actuators, allowing for effective biomimetic robot design optimization and performance prediction.

Bistable Stopper Design and Force Prediction for Precision and Power Grasps of Soft Robotic Fingers for Industrial Manipulation

Abstract This article aims at presenting a detailed and practical comparison between three designs of robotic soft fingers for industrial grippers. While the soft finger based on the fin ray effect (FRE) shown here has been proposed long ago, few works in the literature have studied its reliance on the presence of the crossbeams or its precision grasp performance compared to its power grasps. Aiming at addressing these gaps, two novel designs are proposed and compared to FRE fingers in this article. First, the three designs are presented. One of these fingers, named PacomeFlex, embeds changeable grasping modes by relying on two sets of kinematic structures with a bistable stopper design. Then, finite element analyses (FEA) are conducted to simulate power and precision grasps of the three fingers followed by the estimation of the grasp forces produced. These FEA are then used to train neural networks capable of predicting these grasp forces. Finally, the grasp strength and pullout resistance of the fingers are experimentally measured, and experimental results are shown to be in good accordance with the FEA and neural network models. As will also be shown, the PacomeFlex finger introduced in this work provides a noticeably higher performance level than all other fingers with respect to typical metrics in soft grasping.

Improving the Grasping Force Behavior of a Robotic Gripper: Model, Simulations, and Experiments

Robotic grippers allow industrial robots to interact with the surrounding environment. However, control architectures of the grasping force are still rare in common industrial grippers. In this context, one or more sensors (e.g., force or torque sensors) are necessary. However, the incorporation of such sensors might heavily affect the cost of the gripper, regardless of its type (e.g., pneumatic or electric). An alternative approach could be open-loop force control strategies. Hence, this work proposes an approach for optimizing the open-loop grasping force behavior of a robotic gripper. For this purpose, a specialized robotic gripper was built, as well as its mathematical model. The model was employed to predict the gripper performance during both static and dynamic force characterization, simulating grasping tasks under different experimental conditions. Both simulated and experimental results showed that by managing the mechanical properties of the finger–object contact interface (e.g., stiffness), the steady-state force variability could be greatly reduced, as well as undesired effects such as finger bouncing. Further, the object’s size is not required unlike most of the grasping approaches for industrial rigid grippers, which often involve high finger velocities. These results may pave the way toward conceiving cheaper and more reliable open-loop force control techniques for use in robotic grippers.

Mass-Manufacturable 3D Magnetic Force Sensor for Robotic Grasping and Slip Detection.

The manipulation of delicate objects remains a key challenge in the development of industrial robotic grippers. Magnetic force sensing solutions, which provide the required sense of touch, have been demonstrated in previous work. The sensors feature a magnet embedded within a deformable elastomer, which is mounted on top of a magnetometer chip. A key drawback of these sensors lies in the manufacturing process, which relies on the manual assembly of the magnet-elastomer transducer, impacting both the repeatability of measurements across sensors and the potential for a cost-effective solution through mass-manufacturing. In this paper, a magnetic force sensor solution is presented with an optimized manufacturing process that will facilitate mass production. The elastomer-magnet transducer was fabricated using injection molding, and the assembly of the transducer unit, on top of the magnetometer chip, was achieved using semiconductor manufacturing techniques. The sensor enables robust differential 3D force sensing within a compact footprint (5 mm × 4.4 mm × 4.6 mm). The measurement repeatability of these sensors was characterized over multiple samples and 300,000 loading cycles. This paper also showcases how the 3D high-speed sensing capabilities of these sensors can enable slip detection in industrial grippers.

Curvature Tracking of a Two-Segmented Soft Finger Using an Adaptive Sliding-Mode Controller

The emergence of soft robotics in the last decade has brought about the exponential usage of elastomer bending robots in numerous fields of expertise from prosthetic hands to industrial grippers. While considerable attention has been drawn to static grasp, a substantial gap can be recognized in the development of soft grippers and anthropomorphic hands with the ability to manipulate objects dynamically, an ability that is referred to as in-hand object manipulation. In order to fill this gap, efficient and practical approaches should be investigated for the fabrication, modeling, and control of soft robots that are computationally efficient and capable of yielding repeatable results. Accordingly, in this article, qualified approaches for the dynamic modeling and control of a soft bending finger are introduced that can be efficiently implemented for planar in-hand manipulation applications in soft robotics. In this regard, a novel 3-D-printed mold is designed to cast a uniformly fabricated soft finger consisting of two independently actuated bending segments. After deriving the dynamic model of the soft finger and identifying its dynamic parameters, an adaptive sliding-mode controller is proposed to guarantee the convergence of the bending angles to desired trajectories under dynamic uncertainties and external disturbances. In the experimental validations, the root-mean-square errors of the proposed controller are 0.0323 and 0.0303 rad/m in free motion for each segment, while these numbers are 0.0328 and 0.0226 rad/m for constrained motion. For a comparative evaluation of the proposed controller, the results of the proportional–integral–differential controller are demonstrated under similar circumstances.

A Quadratic-Programming Approach for the Real-Time Control of the Fingers Position in Industrial Pneumatic Grippers

Industrial grippers are generally either electric or pneumatic, with the latter being preferred as their air-based functioning guarantees many advantages such as cost effectiveness and reduced encumbrance. Despite the very large employment, pneumatic grippers do not yet offer performance beyond the open/closed behavior in the majority of cases. Pneumatic grippers mounted on industrial robotic arms are commonly rigid as high forces might be required during the grasping operations. The poorness of the control strategies for the grasping force, as well as for the fingers position, does not match the great advancement that robotics obtained in the last years. Therefore, this letter intends to deliver a new control algorithm for the control of pneumatic grippers. The proposed algorithm permits to finely control the position of the gripper fingers, exploiting a quadratic-programming function to minimize the controller output. Different experiments are shown to demonstrate the algorithm effectiveness.

Utilization of Magnetically Induced Jamming in a Novel Soft Robotic Gripper

Today’s high-tech society creates a pressing need for advanced medical prosthetics and industrial grippers. However, as technological advancements continue to be incorporated, a gap is created by the quality–cost ratio associated with using technologically advanced solutions. For example, many open-source prosthetics provide relatively inexpensive devices, but they often sacrifice the ability to grip irregularly shaped or smooth objects. For instance, doorknobs are difficult to grip using the typical hard robotics approach but can be gripped using soft robotic techniques. Prior work with soft robotics successfully used compressed air and a small granular material (coffee grounds) to build a gripper that can hold various objects. However, the use of compressed air makes these devices relatively slow, and the need for an air compressor limits the applicability of this design. Our primary goal was to explore the feasibility of a soft robotic approach that utilizes ferromagnetic granular materials to grip an object via the granular jamming transition that is induced with an external magnetic field. The granular material is placed inside flexible membranes made of polyisoprene, wherein the granular materials can go from a more relaxed state to a more rigid state depending on the strength of the magnetic field. The flexible membrane also allows the object to be held and then return to its original state when released. A solenoid provides a magnetic field that is easily turned on and off to jam and unjam the magnetic granular material and thus allows us to hold an object in place without the use of compressed air. In searching for a magnetic granular material, we found that iron filings work well, as they easily conform to the shape of the object. We have observed success in holding and releasing several smooth knobs as well as more angular shapes. This work will further the development of a low-cost but high- functioning universal gripper with applications in prosthetics and pick-and-place devices, as well as a multitude of industrial applications.

Instrumenting a Robotic Finger to Augment the Capabilities of Robotic Grippers

Robots are helping humans perform tasks, especially in industry. The collaboration between humans and machines is considered to be among the principal factors underlying the so called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Industry 4.0</i> . To boost such a form of collaboration, robots could be provided with a higher degree of instrumentation, which undoubtedly resorts to a reliable sensorization. In this regard, the robot end-effector is central, being often the interaction organ of the robot. The present article aims at delivering a device, namely instrumented finger (IF), that can be mounted on robotic end-effectors such as grippers. The device is able to sense the grasping force and the associated bending moment thanks to an integrated load cell. It has also a tactile matrix through which the center of pressure can be determined, as well as the slip. Further, the device electronics includes an inertial sensor. Cables are protected by covers which hide the inner metallic body and the electronics. The device is compact, robust and precise, and constitute a promising solution for the instrumentation of robotic grippers. In the present article, the IF functionalities, such as measurement of force, torque, acceleration and center of pressure, will be demonstrated. Further, it will be shown the IF integration on a real industrial gripper mounted on a robotic arm, executing slip compensation experiments and mass estimation of grasped items.

A novel multi objective constraints based industrial gripper design with optimized stiffness for object grasping

Soft gripper design is a rising area of research due of its great possibilities in automation. One difficult problem in robot design is the ability to grasp a broader variety of items with variable stiffness, forms, and sizes in a single gripper. An ideal soft robotic gripper design with variable stiffness was designed in this research as a grasping model. Its distinctiveness is found in the methods utilized for modelling actuators and in the shifting stiffness characteristics of silicon soft gripper. When modelling the actuator in this case, multi-objective functions like gripping displacement and force transmission ratio are taken into account, and the actuator functions are controlled by the MDF (multiple degrees of freedom). The precise stiffness needed to grasp the item is then chosen using an adaptive optimization method. This enhanced weight-based horse herd (IHH) optimization method carries out the stiffness adjustment based on actuation pressure. Additionally, the suggested soft robotic gripper with variable stiffness employs the adaptive level set (ALS) technique to build the gripping force model. Additionally, several validations are offered in relation to the outcomes for item gripping by the suggested soft gripper. This shown that the results of the created soft gripper excelled those of other methods. The developed ABBIRB 1410 robot gripper type may enhance the work cycle in industrial applications and performs object grabbing with dependability and speed. The experimental validations show that the developed gripper model provides an enhanced object grasp with a range of curvatures, delivering a maximum pulling force of 121.07 kPa at 50 kpa, 119.15 kPa for patterned pulling, and 45.05 kPa for non-patterned pulling. The designed gripper type has a curve with a minimum size of 1.1 mm and a maximum size of 218 mm. Additionally, the soft gripper for industrial applications is examined with variously sized and weighted items. The suggested gripper model achieved an RMSE performance of 2.9 and a Pearson correlation of 0.993.

Design and Development of a Multi-Functional Bioinspired Soft Robotic Actuator via Additive Manufacturing.

The industrial revolution 4.0 has led to a burst in the development of robotic automation and platforms to increase productivity in the industrial and health domains. Hence, there is a necessity for the design and production of smart and multi-functional tools, which combine several cutting-edge technologies, including additive manufacturing and smart control systems. In the current article, a novel multi-functional biomimetic soft actuator with a pneumatic motion system was designed and fabricated by combining different additive manufacturing techniques. The developed actuator was bioinspired by the natural kinematics, namely the motion mechanism of worms, and was designed to imitate the movement of a human finger. Furthermore, due to its modular design and the ability to adapt the actuator’s external covers depending on the requested task, this actuator is suitable for a wide range of applications, from soft (i.e., fruit grasping) or industrial grippers to medical exoskeletons for patients with mobility difficulties and neurological disorders. In detail, the motion system operates with two pneumatic chambers bonded to each other and fabricated from silicone rubber compounds molded with additively manufactured dies made of polymers. Moreover, the pneumatic system offers multiple-degrees-of-freedom motion and it is capable of bending in the range of −180° to 180°. The overall pneumatic system is protected by external covers made of 3D printed components whose material could be changed from rigid polymer for industrial applications to thermoplastic elastomer for complete soft robotic applications. In addition, these 3D printed parts control the angular range of the actuator in order to avoid the reaching of extreme configurations. Finally, the bio-robotic actuator is electronically controlled by PID controllers and its real-time position is monitored by a one-axis soft flex sensor which is embedded in the actuator’s configuration.

Simulative Evaluation of a Joint-Cartesian Hybrid Motion Mapping for Robot Hands Based on Spatial In-Hand Information.

Two sub-problems are typically identified for the replication of human finger motions on artificial hands: the measurement of the motions on the human side and the mapping method of human hand movements (primary hand) on the robotic hand (target hand). In this study, we focus on the second sub-problem. During human to robot hand mapping, ensuring natural motions and predictability for the operator is a difficult task, since it requires the preservation of the Cartesian position of the fingertips and the finger shapes given by the joint values. Several approaches have been presented to deal with this problem, which is still unresolved in general. In this work, we exploit the spatial information available in-hand, in particular, related to the thumb-finger relative position, for combining joint and Cartesian mappings. In this way, it is possible to perform a large range of both volar grasps (where the preservation of finger shapes is more important) and precision grips (where the preservation of fingertip positions is more important) during primary-to-target hand mappings, even if kinematic dissimilarities are present. We therefore report on two specific realizations of this approach: a distance-based hybrid mapping, in which the transition between joint and Cartesian mapping is driven by the approaching of the fingers to the current thumb fingertip position, and a workspace-based hybrid mapping, in which the joint–Cartesian transition is defined on the areas of the workspace in which thumb and fingertips can get in contact. The general mapping approach is presented, and the two realizations are tested. In order to report the results of an evaluation of the proposed mappings for multiple robotic hand kinematic structures (both industrial grippers and anthropomorphic hands, with a variable number of fingers), a simulative evaluation was performed.

Fabrication of Flexible Multi-Cavity Bio-Inspired Adhesive Unit Using Laminated Mold Pouring

To meet the requirements for the flexible end-effectors of industrial grippers and climbing robots, inspired by the animal attachment mechanism, a bio-inspired adhesive unit (Bio-AU) was designed. Due to its fluid-driven operating characteristics and multi-level adhesive structure, its fabrication and molding is challenging, including the assembly and molding of complex cavities with good pressure-bearing capability, mechanical properties of multi-level materials with variable stiffness, etc. In this study, based on the lamination mold casting process, the “simultaneous molding and assembly” method was established, which can be applied to form and assemble complex cavity parts simultaneously. Moreover, the dovetail tenon-and-mortise parting structures were analyzed and designed. Furthermore, the adhesion between the parting surfaces can be improved using plasma surface treatment technology. By applying the above methods, the assembly accuracy and pressure-bearing capability of the complex flexible cavities are improved, which reduces the individual differences between finished products. Additionally, the maximum pressure-bearing value of the sample was 83 kPa, which is 1.75 times that before optimization. the adhesive structure with different stiffness components was fabricated at low cost using silicon rubber substrates with different properties, which met the requirements of multi-level material with variable stiffness of the Bio-AU. The bending angle of the optimized molding product was about 50.9° at 80 kPa, which is significantly larger than the 24.6° of the lighting-cured product. This indicates that the optimized lamination mold casting process has a strong inclusion of materials, which improves the deformation capacity and self-adaptability of Bio-AUs and overcomes the defects of 3D printing technology in the formation of large, flexible, and controllable-stiffness structures. In this study, the effective fabrication of flexible multilayer adhesive structures was accomplished, and technical support for the development of Bio-AUs was provided, which met the requirements of bionic climbing robots and industrial adhesive grippers for end-effectors.

Marine Robotics for Deep-Sea Specimen Collection: A Systematic Review of Underwater Grippers.

The collection of delicate deep-sea specimens of biological interest with remotely operated vehicle (ROV) industrial grippers and tools is a long and expensive procedure. Industrial grippers were originally designed for heavy manipulation tasks, while sampling specimens requires dexterity and precision. We describe the grippers and tools commonly used in underwater sampling for scientific purposes, systematically review the state of the art of research in underwater gripping technologies, and identify design trends. We discuss the possibility of executing typical manipulations of sampling procedures with commonly used grippers and research prototypes. Our results indicate that commonly used grippers ensure that the basic actions either of gripping or caging are possible, and their functionality is extended by holding proper tools. Moreover, the approach of the research status seems to have changed its focus in recent years: from the demonstration of the validity of a specific technology (actuation, transmission, sensing) for marine applications, to the solution of specific needs of underwater manipulation. Finally, we summarize the environmental and operational requirements that should be considered in the design of an underwater gripper.

Immobilizing Caging Grasps of Convex Polyhedrons With a Four-Pin Gripper

This letter converts one high-dimensional caging configuration into several lower-dimensional cage sets in the translation space and rotation space, through which analysis of grasping can be carried out more efficiently. A caging set detection method is introduced based on the constraint region. It is found that a caging on a high-dimensional convex constraint region can be converted into a caging set in low-dimensional sub-region. An efficient method to compute the caging of a polyhedral object using a one-parameter four-pin industrial gripper is then presented. Several simulations and real experiments are conducted to show the efficacy of the proposed methods.

Comparative Study of the Parallel and Angular Electrical Gripper for Industrial Applications

Abstract The aim of this paper is to study the position and power performances of an electrical lead screw-driven industrial gripper mechanism (LSDIGM). This work consists of designing and developing an electrical LSDIGM that has the potential to meet various demands in the automation industry and factories. The performances of both angular electrical gripper (AEG) and parallel electrical gripper (PEG) mechanisms were compared based on their position and power efficiency. The position efficiency of these electrical LSDIGM is computed from the position root mean square error (PRMSE) obtained from errors between the two measured positions (input incremental encoder and output linear encoder). In the experimental setup, a current sensor and a spring were employed to measure the current in the input of the system and the stiffness in the output of the system, respectively. The electrical power in the input of the electrical LSDIGM and the mechanical power in the output of the LSDIGMs were calculated using the current and the spring force, respectively. Finally, the power efficiency of these electrical LSDIGMs was examined and compared at different velocity circumstances.

Force transmission analysis of surface coating materials for multi-fingered robotic grippers.

Robotic systems are generally used for grasping, carrying, holding, and many similar operations, typically in industrial applications. One of the most important components of robotic systems is robot grippers for the aforementioned operations, which are not only mission-critical but also represent a significant operational cost due to the time and expense associated with replacement. Grasping operations require sensitive and dexterous manipulation ability. As a consequence, tactile materials and sensors are an essential element in effective robot grippers; however, to date, little effort has been invested in the optimization of these systems. This study has set out to develop inexpensive, easily replaced pads, testing two different chemical compositions that are used to produce a tactile material for robot grippers, with the objective of generating cost, time, and environmental savings. Each tactile material produced has its specific individual dimension and weight. First, each of the materials under construction was tested under different constant pressures, and its characteristics were analyzed. Second, each tactile material was mounted on a two-fingered robot gripper and its characteristics. Material characteristics were tested and analyzed as regards their ability to grasp different sizes and types of objects using the two-fingered robot gripper. Based on the analysis of the results the most sensitive and cost-effective material for industrial type multi-fingered grippers was identified.

Fast development cycle for the design of industrial grippers

Recent trends, such as Supply Chain agility, just in time delivery, and mass customization of products, are pushing automated production processes in Industry 4.0 towards increasing flexibility. Although the entire set of devices is already on the market and can be selected according to the needs, the element that regularly has to be redesigned is the robotic end effector, mainly a gripper. Therefore, we established a Fast Development Cycle to accelerate the design and test process of new industrial grasping devices. The cycle consists of the three main steps: Build, Test, and Learn. The most fundamental aspect of the methodology is to decompose a gripper idea into its essential uncoupled constituents and convert it into a gripper pretotype that is solely oriented to validate its basic principles. This procedure ensures a quick initial building phase and allows to enter testing early. Test results are used to evaluate the initial idea, to enhance knowledge and to create the input for the next turn of the cycle. During each turn the design evolves further while reducing uncertainties. Contrary to traditional product development, this method enables to test the feasibility of a device in the earliest possible stage with the least possible amount of time and money. The single steps of the methodology are illustrated in this paper in detail based on real cases from industry. The successful development of industrial grippers from these real cases through the Fast Development Cycle demonstrates its applicability. Instead of optimizing complex systems, the methodology generates simple solutions with a high potential for cost savings in the design and production process of the devices themselves and during their operation in automated production processes in several industries. The methodology applies regardless of the underlying physical principles and encounters the use of TRLs as KPIs to measure technology maturity.

On the integration of FBG sensing technology into robotic grippers

Modern industrial processes aim for the continuous production of small volumes tailored to the customer’s needs. Machines and robotic platforms have to be more and more adaptable, flexible, and able to cope with complex scenarios where sensing and manipulation capabilities are the key technology to succeed. The literature has plenty of capacitive, resistive, piezoelectric, and piezoresistive sensors used as tactile or force sensors. All of them present some drawbacks like non-linear behavior, sensitivity to temperature or electromagnetic noise, and hysteresis, among others. Other sensing systems are bulky and hard to integrate, sometimes jeopardizing the dexterity and manipulability of the gripper. In this context, the manuscript proposes fiber Bragg grating (FBG) optical fiber as a tactile sensing element to capture the interaction forces during material handling and object manipulation since it has numerous advantages compared with the other sensing devices. The work also offers a methodology to easily integrate the fiber in industrial grippers and introduces a set of tests useful to characterize the sensors. Custom gripper fingers have been realized in rapid prototyping to present a pictorial example of such an integration. Finally, the essay presents some experiments that assess the capability of a tactile sensor based on FBG optical fiber showing as it can correctly perceive the contact forces (NRMSE = 0.75%) and can recognize the material of the object that is being manipulated. The authors believe that the application of optical fiber sensor as tactile feedback can be useful in industrial scenarios to enable complex manipulation activities.

Deep Vibro-Tactile Perception for Simultaneous Texture Identification, Slip Detection, and Speed Estimation.

Autonomous dexterous manipulation relies on the ability to recognize an object and detect its slippage. Dynamic tactile signals are important for object recognition and slip detection. An object can be identified based on the acquired signals generated at contact points during tactile interaction. The use of vibrotactile sensors can increase the accuracy of texture recognition and preempt the slippage of a grasped object. In this work, we present a Deep Learning (DL) based method for the simultaneous texture recognition and slip detection. The method detects non-slip and slip events, the velocity, and discriminate textures—all within 17 ms. We evaluate the method for three objects grasped using an industrial gripper with accelerometers installed on its fingertips. A comparative analysis of convolutional neural networks (CNNs), feed-forward neural networks, and long short-term memory networks confirmed that deep CNNs have a higher generalization accuracy. We also evaluated the performance of the highest accuracy method for different signal bandwidths, which showed that a bandwidth of 125 Hz is enough to classify textures with 80% accuracy.