Method for Automatic Slippage Detection With Tactile Sensors Embedded in Prosthetic Hands (original) (raw)
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Slippage Detection with Piezoresistive Tactile Sensors
Sensors
One of the crucial actions to be performed during a grasping task is to avoid slippage. The human hand can rapidly correct applied forces and prevent a grasped object from falling, thanks to its advanced tactile sensing. The same capability is hard to reproduce in artificial systems, such as robotic or prosthetic hands, where sensory motor coordination for force and slippage control is very limited. In this paper, a novel algorithm for slippage detection is presented. Based on fast, easy-to-perform processing, the proposed algorithm generates an ON/OFF signal relating to the presence/absence of slippage. The method can be applied either on the raw output of a force sensor or to its calibrated force signal, and yields comparable results if applied to both normal or tangential components. A biomimetic fingertip that integrates piezoresistive MEMS sensors was employed for evaluating the method performance. Each sensor had four units, thus providing 16 mono-axial signals for the analysis. A mechatronic platform was used to produce relative movement between the finger and the test surfaces (tactile stimuli). Three surfaces with submillimetric periods were adopted for the method evaluation, and 10 experimental trials were performed per each surface. Results are illustrated in terms of slippage events detection and of latency between the slippage itself and its onset.
Slippage Sensory Feedback and Nonlinear Force Control System for a Low-Cost Prosthetic Hand
Am. J. Biomed. Sci, 2009
The low acceptability of current prosthetic devices can be attributed to the extensive psychological effort and the high cost associated with them. To address these concerns, an on-line slippage detector was developed using only inexpensive force sensors placed at the tips of a prototype hand. The prototype consists of a five fingered prosthetic hand consisting of active digits driven independently by DC motors. Force sensor resistors (FSR) are placed at the tip of each active finger and potentiometers are attached at the proximal and middle joints. Using the information from the FSR, not only can we detect the level of normal force exerted but also slippage between the fingers and the object by calculating the fluctuations of the exerted force. An on-line algorithm is developed to calculate the derivative of the force and determine when slippage is produced. Nonlinear model predictive control (NMPC) is used to provide feedback control to the prosthetic device. It utilizes a neural network to model the dynamics of each finger. Using this model, it is possible to predict future plant performance (the amount of force exerted by the prosthetic hand). Consequently, the controller uses this prediction to calculate the best input (current needed to drive the actuators) for the system to obtain the desired output over a specific time horizon. In order to calculate the future control inputs, the optimization system minimizes the cost function associated with the difference between the measured force and the reference / target output. Experimental protocols involve grasping various objects and inducing slippage. Data was collected using the NI DAQ cards and LabVIEW software. Experiments showed promising results using this strategy in which the force exerted on an object can be modulated without additional efforts from the users.
Generating tactile afferent stimulation patterns for slip and touch feedback in neural prosthetics
Current prosthetic limbs are limited by a lack of tactile feedback. Slip feedback is particularly important to inform grip. Object slip is marked by both a change in the normal grip force applied and a change in force tangential to the fingertips. In this study, we demonstrate that a new multiaxial tactile sensor composed of gold nanoparticle strain gauges is able to record slip and reconstruct the X, Y, and Z forces incident on the sensor's surface due to a slipping object. We entered the X, Y, and Z force components generated by the slip event into a noisy leaky integrate and fire model to simulate the firing responses of SA1 and FA1 afferents. We also recorded a uniaxial normal force input representative of tactile contact. A single set of SA1 model and FA1 model parameters generated realistic firing patterns for both the slip and normal force recordings. These results suggest that canonical SA1 and FA1 afferent models could be used to generate biomimetic electrical stimulation patterns for both slip and touch stimuli. When used to activate the tactile afferents of an amputee, these electrical stimulation patterns could create natural and distinguishable slip and touch percepts for closed loop control of an upper limb neural prosthesis.
Thick-film force and slip sensors for a prosthetic hand
Sensors and Actuators A: Physical, 2005
In an attempt to improve the functionality of a prosthetic hand device, a new fingertip has been developed that incorporates sensors to measure temperature and grip force and to detect the onset of object slip from the hand. The sensors have been implemented using thick-film printing technology and exploit the piezoresistive characteristics of commercially available screen printing resistor pastes and the piezoelectric properties of proprietary lead-zirconate-titanate (PZT) formulated pastes. The force sensor exhibits a highly linear response to forces up to 50 N with a maximum hysteresis of less than 1.4% of full scale. When configured as a pseudo half-bridge measurement circuit, the force sensor demonstrates superior insensitivity to the position of the force on the fingertip than when configured as a classic half-bridge circuit. The force sensor response is also extremely stable with temperature, typically showing variation in the output response of less than ±0.04% over the temperature range −10 • Cto+35 • C when loaded with forces up to 10.8 N. The ability of the piezoelectric PZT vibration sensor to detect small vibrations of the cantilever, indicative of object slip, has also been demonstrated. 9 10 11 12 13 14 15 16 17
Robotics and Autonomous Systems, 2015
h i g h l i g h t s • We have extracted human fingertip structure using MR images. • We have modeled the inhomogeneous human fingertip with proposed Beam Bundle Model as a platform that reduces remarkably calculation time. • Investigating sliding mechanics of the model, such as friction, and especially the localized displacement phenomenon during pre-slide phase. • Verifying the model with an artificial human-like fingertip and a fine experimental setup. • Pointing out the role of localized displacement phenomenon in assessing tactile sensing perception and its application in robotics.
Slip classification for dynamic tactile array sensors
The International Journal of Robotics Research, 2015
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...
Tactile Retina for Slip Detection
2006 IEEE Symposium on Virtual Environments, Human-Computer Interfaces and Measurement Systems, 2006
The interest in tactile sensors is increasing as their use in complex unstructured environments is demanded, like in telepresence, minimal invasive surgery, robotics etc. The array of pressure data provided by these devices can be treated with different image processing algorithms to extract the required information. However, as in the case of vision chips or artificial retinas, problems arise when the array size and the computation complexity increase. Having a look at the skin, the information collected by every mechanoreceptor is not sent to the brain for its processing, but some complex pre-processing is performed to fit the limited throughput of the nervous system. This is specially important for high bandwidth demanding tasks. Experimental works report that neural response of skin mechanoreceptors encodes the change in local shape from an offset level rather than the absolute force or pressure distributions. Something similar happens in the retina, which implements a spatiotemporal averaging. We propose the same strategy in tactile preprocessing, and we show preliminary results illustrated for the case of slip detection, which is certainly demanding in computing requirements.
Electro-tactile Feedback System for a Prosthetic Hand
2015
Without the sense of touch, amputees with prosthetic hands can have difficulty holding and manipulating objects, especially when a task requires some degree of skill and tactile feedback to perform. To equip prosthetic hand users with touch sensing and tactile feedback, researchers have been experimenting with various types of force and/or tactile sensors together with various methods for delivering the tactile information to the brain. Although some success has been achieved recently with force sensors and implanted electrodes, these systems are expensive, surgically invasive and can represent an infection risk where cables emerge through the skin. Also, non-invasive tactile feedback methods involving temperature, vibrations or electro-mechanical force feedbacks, can be somewhat awkward and ineffective due to being cumbersome or unable to deliver appropriate sensations. To address some of these issues we have been developing an electro-tactile feedback system for prosthetic hands. ...
Slip interface classification through tactile signal coherence
2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013
The manipulation of objects in a hand or gripper is typically accompanied by events such as slippage, between the fingers and a grasped object or between the object and external surfaces. Humans can identify such events using a combination of superficial and deep mechanoreceptors. In robotic hands, with more limited tactile sensing, such events can be hard to distinguish. This paper presents a signal processing method that can help to distinguish finger/object and object/world events based on multidimensional coherence, which measures whether a group of signals are sampling a single input or a group of incoherent inputs. A simple linear model of the fingertip/object interaction demonstrates how signal coherence can be used for slip classification. The method is evaluated through controlled experiments that produce similar results for two very different tactile sensing suites.