high performance haptic device for force rendering in textile exploration

Visual Comput (2007) 23: 247–256DOI 10.1007/s00371-007-0103-1 O R I G I N A L A R T I C L E

M. BergamascoF. SalsedoM. FontanaF. TarriC.A. AvizzanoA. FrisoliE. RuffaldiS. Marcheschi

High performance haptic device for forcerendering in textile exploration∗

Published online: 2 March 2007© Springer-Verlag 2007

M. Bergamasco (�) · F. Salsedo ·M. Fontana · F. Tarri · C.A. Avizzano ·A. Frisoli · E. Ruffaldi · S. MarcheschiPERCRO, Scuola Superiore Sant’Anna,Pontedera, [email protected]

Abstract The HAPTEX systemaims to develop a new multi-sensoryenvironment for the immersiveexploration of textiles.HAPTEX is based on a multi-layer/multi-thread architecture thatoptimizes the computational speedand integrates three different types ofsensory feedback: vision, tactile andhaptic.Such kind of environment is suitablefor demanding VR applications suchas the online marketing of novel tex-tiles or garments, however it requiresthe design of a high performancemulti-point haptic interface.The present work focuses on thehaptic device design and describeshow demanding requirements can

be met by integrating on a highperformance device a force sensor toachieve closed loop control.The methodology for the dimension-ing of a motion based explicit forcecontrol on the basis of the dynamicparameters will be discussed andthe specific implementation for theHAPTEX system presented.

Keywords Haptic interfaces · Textilemodeling · Visual/tactile integration

1 Introduction

The ability to assess, in virtual environments, cloth orgarments, as they are actually touched by the user, is ofcentral importance in the selection-for-purchase processof every consumer [12].

The realistic rendering of the physical interaction ofthe hand with fabrics implies the accurate generation ofvery small global and spatially distributed forces, of theorder of few grams.

Related major challenges require the design of newalgorithms for the real-time representation of visual andcontact stimuli and the design of new devices for the sim-

∗The present work has been supported by the EU within the context of thefollowing projects: HAPTEX, ENACTIVE

ultaneous representation of force and tactile informationduring exploration and manipulation [14].

A large amount of research has been carried out todevelop physically based models that are suitable for real-time simulation [4, 5] of fabrics. As far as the physicalmodels are concerned, the most used ones are particlemodels [11] and finite element models [8].

In recent years, haptic interfaces have received increas-ing attention from researchers for their potential applica-tions in industry and society, and also of their versatilitythat make them suitable to different applications.

Govindaraj envisaged [9] the possibility to enrich thevirtual interaction with digital clothes by integrating hap-tics and tactile displays. He made use of a tactile pin arrayattached to the end-effector of a planar haptic interface forthe rendering of the fine mechanical properties of virtualfabrics. However the kinematic constraints of his solution

248 M. Bergamasco et al.

Fig. 1. HAPTEX scenario

limit the exploration to rigid planar garments with planarforce rendering.

When a spatial interaction is required, more complexmodels and control algorithms for the HI are needed, asoutlined in the survey by Buss et al. [15]. Several works onthe control of HI with explicit force control have been car-ried out in [1, 16, 17], but none of them have investigatedthe control of mechanical structures with very high com-pliance. A possible schematization and analysis of explicitforce controllers that takes into account the compliance ofthe mechanical system is done by Eppinger in [6] withinthe field of control of manipulators.

However, when a haptic interface is employed as forcedisplay device in textiles exploration and manipulation,the application context poses strict requirements on the de-vice performances that may be difficulty achieved with ex-isting systems: from one side manipulation of textiles re-quires multiple points of contact, each one exerting forcesup to several Newtons, on the other side fine surface ex-ploration of textiles requires of the same device a forceresolution and accuracy of few grams.

Presently, all commercially available HIs do not com-ply with these demanding requirements. This has beenthe main motivation for the development of a new device,a force sensor and the related closed loop controller. Thedevice is based on the existing GRAB system [3], that al-ready solved the issues of multi-point cooperative hapticinteraction in large workspaces.

In this paper we introduce the overall system archi-tecture that has been conceived to integrate the sensorialfeedback and discuss the technical features of the novelHI. Moreover an optimal design procedure for the closedloop control is presented. Such a procedure is based onan inverse model of the human impedance, which hasbeen estimated as a relevant component of the control de-sign. The identification of the user is based on the specificconfiguration of the device (finger hold in a thimble) andextending previous works on other given configurations:finger on a plate [10], against a wall [18], during precisiongrasping [7].

2 Architecture of the HAPTEX system

The HAPTEX system integrates three different displays:a haptic interface, a visualization system and a tactile dis-play (Fig. 1).

All of them share a common VR scenario representinga virtual textile, modeled through a square cloth.

The cloth is fixed on a horizontal stand such that, inresting condition the steady state solution is determinedsolely by the gravity force.

The physical interaction can be attained through the in-dex finger and the thumb. The user can touch, squeeze,rub and stretch the fabric, feeling the corresponding globalforces on the fingertips arising from the deformation of thetextile and the tactile stimulation due to the relative motionof the finger with respect to the deformed surface.

During the virtual simulation such forces will be dis-played by the haptic device and a tactile display attachedto user fingertips.

The system should be able to reproduce a physical ef-fect at such levels of detail such that: physical interactionwith the fingertips may occur in every part of the cloth; thetwo fingertips may independently act on the same pieceof textile; friction and slip effect between fingertips andtextile may occur according to physical condition.

The complexity of the simulation suggested to adopta two-layer architecture, using different models for eachlayer as proposed in [2]: one Large Scale Model (LSM)that evaluates the cloth dynamics in the region far fromthe contact points, and a Small Scale Model (SSM) thatmodels the contact properties in regions close to the fin-gertips.

The two models operate at different frequencies (25 Hzfor LSM, 1000 Hz for SSM) according to the require-ments of different display devices. An impedance networkmatches constraints between models at boundary regionsas shown in Fig. 2.

Clothes models have been derived for a broad rangeof fabrics (32 different samples). The measurements wereperformed using the KES-F system [13], which is the most

Fig. 2. Interconnection between LSM and SSM

High performance haptic device for force rendering in textile exploration 249

Fig. 3. Scheme of the architecture of the HAPTEX system

common method for objective evaluation of fabric. Ac-cording to this method the fabric is modeled by parametersrepresenting the measurements of bending, tensile, shear-ing, compression, friction and roughness.

The overall architecture is described in Fig. 3: thePhysical Model Simulation (PMS) delivers the renderinginformation to all the system display devices.

3 Design of the HI

The design of the novel HI took as reference the workdeveloped for the GRAB system [3]. GRAB is a 6-DOFdesktop force-feedback device with a serial kinematics.The device is composed of two identical units each ofthem providing forces at the level of one of the user’s fin-gertips as shown in Fig. 4.

In the modified design of the device, the first 3 DOFs(RRP) are required to track the position of the fingertip in3D space and the remaining 3 (RRR) are required to trackits orientation. All DOFs are provided with high resolutionangular sensors. Moreover, motors on the first 3 DOFsmay exert on the user’s fingertip forces of arbitrary orien-tation and modulus, whereas the last 3 DoFs are passive.The three motors are remotely located in order to reduce

Fig. 4. Kinematics and photo of one arm of the GRAB

Fig. 5. A detail of the force sensor in the GRAB modified version

the overall weight and inertial effects: the first two actua-tors are mounted on the fixed link (base), while the thirdactuator is mounted in the equilibrium center of the sec-ond moving link. Forces are transmitted from the actuatorsto the joints by steel cables routed on idle pulleys. Thismodified version was equipped with a new high resolutionthree-axial force sensor, purposely designed to read forcesexerted by the fingertip on the HI contact point, as shownin Fig. 5.

4 Control design

The control loop has been implemented on a 850 MHzPentium III, equipped with an ISA I/O board (ServoToGoModel II – 8 axis); additional 16 bit ADC electronics hasbeen implemented for converting force sensor data. Mo-tor power is supplied by 6 PWM drivers (Elmo Violin10/100).

Figure 6 shows the general scheme of the control ar-chitecture for each DoF that has been selected for the

Fig. 6. General control scheme


implementation of the explicit force control: it consists ofan inner velocity loop-outer force loop scheme. This one-dimensional scheme can be simply generalized for imple-menting a three-dimensional force control: it is sufficientto transform the velocity reference, obtained as output ofgain Kf, in a joint speed reference by simply multiplyingit with the inverse of the Jacobian matrix, which describesthe inverse differential kinematics of the device.

This solution allows general control architecture to besafe and simplifies the interface procedure with user.

4.1 Physical model of the mechanics

The mechanical architecture used for the haptic interfaceallows the following schematization for the dynamics ofeach DOF: two masses representing the inertia of mo-tor and link; a spring, connecting the motor and the linkreduced masses, representing the transmission stiffness;two dampers, connecting the motor and the link massesto the ground, representing the viscous friction locatedin moving parts (mainly bearings); a mass-spring-dampersystem, connected rigidly to the link, representing the hu-man finger dynamic. A schematic representation of thejoint dynamic model is shown in Fig. 7.

All the model parameters and variables introduced be-low are expressed with respect to the same axis. Moreover,the linear moving elements indicated in the scheme ofFig. 7 could schematize also rotating parts. The transferfunction between the motor force Fm and the motor pos-ition Xm is:

Xm

Fm= 1

Ku

×s2

s2g+ scgs

s2g

+1

s4

s2r s2

u+

(scm+scg

su

)s3

s2r su

+(

s2r +scmscg

s2r

)s2

s2u+ sctots

s2u

+1

(1)

where

su =√

Ku

Mtot; sr =

√Kt

Mr; sg =

√Kt

Mg + Mu;

scm = Cm

Mm; scg = Cg +Cu

Mg + Mu; sctot = Cm +Cg +Cu

Mtot;

Mtot = Mm + Mg + Mu; Mr = Mm(Mg + Mu)

Mtot.

The frequency sg is called anti-resonant frequency ofa two-mass system while sr is the resonant frequency.

The following assumptions can be applied to this kindof haptic interface:1. The motor mass is much lower than the joint mass, so

sg � sr.2. The motor and the joint damping factors are much

lower than 1, so scg � sg and scm � sr.

Fig. 7. One DOF mechanical model

These assumptions allow to simplify Eq. 1 in:

Xm

Fm= 1

Ku

s2

s2g+ scg

sg

ssg

+1

s2

s2r

s2

s2u+

(scm+scg

su

)s2

s2r

ssu

+ s2

s2u+ sctot

su

ssu

+1

Furthermore it is generally true that Ku � Kt. Conse-quently the transfer function between the force transmittedto the human finger Fu and the motor position Xm reducesto:

Fu

Xm= Ku

s2

s2u’

+ scu’su’

ssu’

+1

s2

s2g+ scg

sg

ssg

+1(2)

where

su’ =√

Ku

Mu; scu’ = Cu

Mu

In Fig. 8 the asymptotic magnitude Bode plots of Eqs. 1and 2 are reported.

The frequency of the zero of the human dynamics hasbeen estimated to be lower than the frequency of the trans-mission pole. The red arrows shown in Fig. 8 indicate thata gain peak due to the resonance effect is expected in thereal Bode plot.

Experimental validation. The dynamic response of thehaptic interface has been investigated exciting each DOFof the HI separately. For each DOF, two separate sets ofexperiments have been carried out in order to validate andidentify the theoretical transfer functions described above,using non-parametric methods, such as spectral analysis.For both sets of experiments a pseudo-random binary sig-nal has been selected as an excitation signal for the actua-tor.

The first set of experiments was devoted to the valida-tion of the transfer function in Eq. 1. During this experi-ment it was observed that the location of the poles and thezero of the transfer function is not significantly affectedby the presence of the user impedance applied at the end-effector. This is attributable to the negligible value of thestiffness of the mass of the human finger when comparedto the corresponding parameters of the HI.


Fig. 8. One DOF theoreticalBode plot

The second set of experiments was devoted to the val-idation of the transfer function in Eq. 2. Since in this casethe impedance of the user plays a major role in the pole-zero location, five different users were involved. The userswere required to apply their own right-hand index fingerin the gimbal of the HI, as shown in Fig. 5. The empiricaltransfer function estimation is computed as the ratio of theoutput Fourier transform to the input Fourier transform,using the Fast Fourier Transform (FFT) algorithm. More-over, a smoothed version of the transfer function estimatealgorithm has been used, obtained by applying a Ham-ming windowing transformation to the FFT of the outputsignal and to the absolute square of the FFT of the input.

Figure 9 shows the Bode plot of Xm/Fm experimen-tal transfer function. A good agreement between the pre-dicted asymptotic plot and the experimental one can beobserved. The high resonant peak of double transmissionpoles sr and zeros sg confirm the assumption of low damp-ing factor. The absence of resonances for the pole su indi-cates a human damping factor close to one.

In Fig. 10 the experimental Bode plots of the trans-fer function Eq. 2 relating to three of the five different

Fig. 9. Experimental Xm/Fm transfer function

users is shown together with the theoretical asymptoticplot. The behavior represented in the figure can be in-terpreted as follows: the general trend is in accordancewith the model only for frequencies over 10 Hz; forlower frequencies, the mass of the palm of the handand the forearm contribute to the measured impedanceand for this reason this cannot be represented by a sim-ple second-order system; a sixth-order system should benecessary for representing the real dynamic response.The differences between the Bode plots demonstratesignificant variability of the dynamic parameters(Ku, Cu, Mu).

The accuracy of the experimentally evaluated Bodeplot increases with the level of the energy introduced inthe system and with a proper selection of the maximumfrequency content of the exciting signal. For both experi-ments a force signal switch from 0 to 8 N was used. Themaximum frequency was set at 200 Hz.

Identification of the dynamic parameters. Only a subset ofthe model parameters has been estimated by experimentalidentification.

Fig. 10. Experimental Fu/Xm transfer function


The motor and the joint masses can be estimated withgood accuracy by CAD models of the mechanical parts.Also, the transmission stiffness constant has been esti-mated using a theoretical model for the cable transmission(an elastic beam with a proper Young’s modulus). The the-oretical value has been validated with the experimentalvalue, obtained by double zero transfer function where:

Sg ≈√

Kt

Mg

Experimental identification has been performed for theviscosity coefficients and human model parameters. Thedevice viscosity coefficients have been determined by fit-ting the model with the experimental Bode plot of Fig. 10.

Human model parameters have been estimated byusing experimental Bode plots as follows:

Human stiffness could be estimated by considering theasymptotic value of Fig. 10 for s → 0, in accordance withthe theoretical transfer function Eq. 1, resulting in:

Xm

Fm

∣∣∣∣s→0

= 1

Ku

Observing the low frequency behavior of the Bodeplots shown in Fig. 10, human damping factor can be as-sumed to be close to unity.

Human mass can be empirically estimated by corre-lating the Bode plot of Fig. 9 with the theoretical one.The measured parameters of the system are reported inTable 1. The reported stiffness value is the maximum ob-served among users while the obtained values of the fingermass remain almost constant for different persons.

5 Dimensioning procedure

For dimensioning the controller parameters of the generalscheme shown in Fig. 6, first the inner velocity loop is di-

Table 1. Experimental values of model parameters

Parameter Value Unit

Mm 0.062 kgMg 0.450 kgMu 0.050 kgKt 37.70×103 N/mKu 100 N/mCm 4.713 Ns/mCg 38.915 Ns/mCu 2.683 Ns/msu 2.1 Hzsg 43.7 Hzsr 131.4 Hz

mensioned and then the parameters of the outer force loopare optimized.

5.1 Velocity loop

Observing the Bode plot of Fig. 9, two possible strate-gies can be followed: place the bandwidth of the velocityloop sV below the double transmission zero sg or over thedouble transmission pole sr. The first solution has beenadopted since the second one can be used only for systemsequipped with velocity sensors like tachometers or alter-natively with high resolution position sensors. Indeed inthe case of limited resolution of the position sensors, thevelocity feedback estimated by derivation from the pos-ition signal has to be filtered to relatively lower frequen-cies in order to avoid noises incompatible with the finalapplication. In the case of the selected solution (bandwidthbelow sg) the maximum theoretically reachable bandwidthis sg. In order to achieve this maximum a filter FV1 has tobe inserted in the forward path of the control loop for at-tenuating the resonant peak in correspondence of sr. Thisfilter should match the following requirements:

(1) Keep the magnitude above 0 db for frequencies overthe velocity bandwidth sV;

(2) have a minimal phase lag at sV (in order to guaranteea certain phase margin).

For selecting the best filter for this purpose, an ana-lytical evaluation of different filters has been carried out:given the allowable phase lag φP and the desired attenua-tion at sr, the different filters have been evaluated by con-sidering the difference sr − sp, where sp is the frequency atwhich the filter phase lag is equal to φP. Indeed the min-imum value of the difference sr − sp indicates that filteris able to produce the desired attenuation with minimalphase lag at lower frequency. A filter of a different type,order and cutoff frequency has been considered: the third-order Chebyshev Type I filter resulting as the best one.

The Chebyshev filter has been dimensioned in order tohave an attenuation in sr of 30 db and a phase lag of 60degrees: on the basis of the specifications, the value forsp was 27.8 Hz. The proportional gain KV is then fixed inorder to obtain the maximum bandwidth (open loop mag-nitude of 0 db for s = sp) Kv ≈ Mtot

sp.

Finally, the filter FV2 is introduced for limiting thenoise of the velocity signals derived from the encoderposition. Its pole must be placed on a sufficiently high fre-quency for limiting its contribution to the phase lag.

Final velocity control parameters and performances arereported in Table 2.

Figure 11 shows the Bode diagram of open and closedloop velocity controller.

5.2 Force loop

For a frequency lower than its bandwidth the inner vel-ocity controller has a transfer function close to the identity.


Table 2. Velocity control parameters

Parameter Sym. Value Unit

Velocity gain Kv 98.4 Ns/mFv1 cut-off frequency fv1 500 HzFv2 cut-off frequency fv2 59.6 Hz

Fv2 ripple R 1 –Bandwidth sv 20.6 Hz

Phase margin Pmv 60 DegreeGain margin Gmv 8.2 Db

So, the open loop transfer function of outer force con-trol is strongly affected by the human response. Referringto the Bode plot of Fig. 8 the human zero introduces anamplification which is very important for force controlperformance and stability. Asymptotic constant amplitudefor s > sg does not depend on the human stiffness:

Fu

Xm

∣∣∣∣s=sg

≈ KtMu

Mg + Mu

For this reason, assuming that the mass of the human doesnot vary sensibly from subject to subject, the worst casefor the stability corresponds to the condition of maximumhuman stiffness. Indeed, the higher the human stiffness,the higher the human zero frequency is and therefore thehigher the phase lag. Nevertheless, force control perfor-mances increase if human stiffness increases: this is anintrinsic characteristic of the chosen control architecture.The haptic device can reach desired interaction force, bymeans of end-effector movement, as much as external en-vironment offers resistance to end-effector movement. So,in order to maximize the force control performance in theworst case, a second-order Butterworth filter is inserted inthe forward side of the controller. Centering the filter cut-off frequency in su’,max (i.e., for maximum value of Ku),the asymptotic Bode plot, again, becomes a constant linetill sg. This filter allows to attenuate the open loop mag-nitude for frequencies over su’,max and so to increase theforce constant gain. Nevertheless, unavoidable variationof human stiffness modifies the Bode plot as shown inFig. 12.

Stabilizing force control loop for Ku,max will ensurethat the control loop will be stable for all subjects withlower stiffness. In ideal conditions, force constant KF,which allows a force bandwidth sF equal to velocity band-width sV, results in:

KF = sV

Ku,max

In general, this is only an upper limit value; in fact, thephase lag of the inner velocity loop imposes a reduction ofthe real force bandwidth.

Final force control parameters and performances arereported in Table 3 and Fig. 13 shows the Bode diagram ofclosed loop velocity controller.

Fig. 11. Open loop Bode plot of velocity

Fig. 12. Bode plot in presence of human

Table 3. Force control parameters in presence of human

Parameter Sym. Value Unit

Force gain Kf 0.52 m/sNFf cut-off frequency Ff 7.1 Hz

Ff order Nf II -Bandwidth sf 23.6 Hz

Phase margin Pmf 54.4 DegreeGain margin Gmf 5.9 Db

Regarding the force control performances, it’s import-ant to note that in ideal static conditions (end-effectorspeed equal to zero), percentage force error is given


Fig. 13. Asymptotic Bode plot of transfer function due to presenceof human

by:

Fdes − Fu

Fdes= 1

Kv Kf= 1.96%

during the movement at zero desired force, supposing themechanical device is completely transparent, the distur-bance force introduced by the controller is

Fdist = −XuKv

1+ KvKf≈ −Xu

1

Kf

For example, a disturbance force of 0.2 N corresponds toan end-effector speed velocity of 0.1 m/s.

6 Field test

In order to experimentally evaluate the performance ofthe explicit force controller dimensioned according to themethodology described above, two sets of experimentshave been carried out. In both sets, the subjects were askedto move their finger back and forth at an almost constant

Fig. 14. Force measurements infield tests

velocity, resting for a while at the reversing of the motiondirection.

The first test was carried out with a zero force controlreference: the forces measured during the test are shown inFig. 14a. In the second set the same test was then executedwith a 1 N force control reference (Fig. 14b).

The tests show that the system is highly accurate instatic conditions (as expected, error is less than 2%), whileerrors proportional to the end-effector velocity are intro-duced in dynamic conditions. This is an intrinsic charac-teristic of the selected basic architecture of the controller.In order to reduce the values of the resistant forces duringthe tracking at constant velocity the force gain KF shouldbe increased but this would lead to instability of the sys-tem.

7 Conclusion

In this paper we presented a novel haptic system and thedesign of its controller for an optimal combined hap-tic/tactile rendering in multi-sensory exploration of virtualtextiles. The novel control design methodology is basedon the identification of human and device parameters andits performances have been assessed both with theoreticalanalyses as well as with experimental validation.

An architecture to integrate this system in a multi-layerarchitecture that is suited for reproducing interaction withvirtual clothes has been discussed and its implementationis available in our laboratory.

The overall architecture has proved to performingenough to support a wide range of feedback forces. Max-imum accuracy has been assessed in both textile interac-tion condition: exploration and manipulation. In the firstcase, contact force is approximately 0, the force-feedbackaccuracy is below 1 gram in static condition while it holdswithin 10 grams in dynamic conditions; in the second casethe force accuracy has proven to be within 5% of the over-all applied force both in static and dynamic conditions.


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MASSIMO BERGAMASCO is a Full Professor ofApplied Mechanics. He teaches the mechanicsof robots and also virtual environments. His re-search activity deals with the study and devel-opment of haptic interfaces for the control ofthe interaction between humans and virtual en-vironments. In this field he is interested in thekinematics aspects of the design of haptic mech-anisms. He has been the scientific coordinator ofseveral national projects and EU projects. Mas-simo Bergamasco is presently the Coordinator ofthe ENACTIVE Network of Excellence and theSKILLS Integrated Project. He has been authoror coauthor of more than 200 papers in interna-tional scientific journals and conferences.

CARLO ALBERTO AVIZZANO is a Senior Re-searcher at Scuola Superiore Sant’Anna, andexternal Prof. of Mechatronics at University ofPisa. He graduated in Informatic Engineeringwith summa cum laude from the Universityof Pisa in 1995, and received the UniversityDiploma in Engineering (1995) and Ph.D. inRobotics and Teleoperation in Virtual Environ-ments (2000) from Scuola Superiore Sant’AnnaAnna.Carlo Alberto Avizzano research interests relatethe automation and digital control of robotic sys-tems, and includes the design applied system inthe fields of haptics control, teleoperation, sim-ulators, mobile robotics, and advanced learningsystems.He has authored about 100 publications in inter-national scientific journals and conferences.

ANTONIO FRISOLI is an Assistant Professorof Mechanical Engineering at Scuola SuperioreSant’Anna, where he is currently head of theVirtual Reality & Telerobotic Systems Divisionof Percro Laboratory.He graduated in Mechanical Engineering withsumma cum laude from the University of Pisa

in 1998, and received the University Diplomain Engineering and Ph.D. in Robotics and Tele-operation in Virtual Environments from ScuolaSuperiore Sant’Anna Anna, respectively in 1998and 2002.His research interests are in the fields of hapticrendering and haptic interfaces design, robotics,automatic control, theoretical kinematics; inthese fields he has authored more than 70 pub-lications in international scientific journals andconferences.

FABIO SALSEDO since 1994 he has been re-sponsible for the coordination of the researchand development activities of the MechanicalDesign Division at the PERCRO. Since 1995 hehas played a major role in several national andinternational RTD projects.At present he is the project manager of the na-tional RTD project Body Extender and to the EUfunded RTD project HAPTEX (HAPtic sensingof virtual TEXtiles).His main interests are in the mechanical de-sign of advanced haptic and human computerinterfaces. He is coauthor of more than 10 in-ternational patents relating to haptic interfaces,mechanical motion tracking devices and roboticsurgical tools.

EMANUELE RUFFALDI is currently a researchfellow of the PERCRO Lab of Scuola Superi-ore S. Anna, Pisa, Italy. He obtained his Ph.D. inPerceptual Robotics in 2006 from Scuola Supe-riore S. Anna discussing a thesis on perceptuallyinspired haptic algorithms. He received a M.Sc.degree in Computer Engineering in 2002. His re-search interests are in the fields of virtual reality,information visualization, haptics, and physicalmodeling.

SIMONE MARCHESCHI was a Ph.D. studentat Scuola Superiore Sant’Anna until 2004; he

collaborates as a control engineer with PERCROLaboratory on the same research topics. He hasacquired competencies in mechanical designduring the academic courses and in the realiza-tion of a haptic device for the office automation.During the Ph.D. courses he carried out a re-search program focused on the analysis anddesign of mechanical systems for haptic inter-faces and the synthesis of control systems forvirtual environments.He was involved in several EU projects, i.e.,GRAB, CREATE, VIRTUAL, and PUREFORM.He is coauthor of several publications for themost important international conferences in thefield of robotics and automation.

MARCO FONTANA received his degree fromthe University of Pisa in 2003 with a diplomathesis on the design of a low-cost mechanicalhead tracker. Since then he has collaborated withPERCRO in several internal and EU projects.His research interests are in the field of mechan-ical design haptic interfaces and hand-held toolsfor surgical applications. Currently he is a Ph.D.candidate in Robotics for Virtual Environmentand member of the research division of Percep-tual Devices at PERCRO.

FEDERICO TARRI received his degree in Me-chanical Engineering at University of Pisa inDecember 2004. He developed his thesis atPERCRO Laboratory; the title of his thesiswas “Analysis and design of an innovative ac-tuation system for a millirobotic end-effectorto be used in minimally invasive surgery.”He has continued his work at PERCRO Lab.and has started his Ph.D. in Robotics withScuola Superiore Sant’Anna of Pisa in 2005.Now he is working on MISAT and HAPTEX(http://haptex.miralab.unige.ch) projects.

high performance haptic device for force rendering in textile exploration

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