Third Joint Eurohaptics Conference and Symposium on Haptic Interfaces
for Virtual Environment and Teleoperator Systems
Salt Lake City, UT, USA, March 18-20, 2009

Efficient Object Exploration and Object Presentation in TeleTA,
Teleoperation System with Tactile Feedback
Dzmitry Tsetserukou and Susumu Tachi
University of Tokyo, Department of Information Physics and Computing, Japanon body, are sensed by mechanoreceptors in the joints, tendons,
and muscles enabling us to estimate forces being applied to body[1]. On the contrary, mechanoreceptors in the skin layers are
responsible for cutaneous stimulation sensation that enables
stimuli localization.

ABSTRACT
In order to achieve awareness of collision during teleoperation and tosupport operator with valuable information (object stiffness and shape),
we elaborated approaches to object parameter identification and delivering
this information to the operator’s skin through tactile display. The tactile
sensory ability of the remote manipulator allows collision detection andexploration of contacting object. A new type of tactile display that
accounts for effectiveness of the information presentation, power
consumption, ergonomics, and usability for particular teleoperation task
was developed. The experimental results of object shape and stiffnesspresentation to the human’s forearm revealed the most efficient vibration
patterns.

Position,
Velocity
Force,
Shape,
Stiffnessa) Operator cockpit

KEYWORDS: Teleoperation, vibrotactile display, tactile feedback.
Tactile display

INDEX TERMS: H.5.2 [User Interfaces]: Haptic Interfaces; H.1.2[User/Machine Systems]: Human Factors; I.3.6 [Methodology andTechniques]: Interaction techniques.

1

Opticaltorquesensors
Tactileskin

INTRODUCTION

The paper focuses on the teleoperation system TeleTA(Teleoperetion system with wearable Tactile display on theoperator arm surface providing Awareness about slave robot
collision) designed by us to achieve high level of maneuverabilityof robot arm in unstructured dynamic environment and to perform
cooperative tasks with humans in a safe manner. We announce
several distinctive contributions in this paper. New remote robot
and sensory system for the operation in cluttered environment
were developed. Distributed optical joint torque sensors and local
admittance controllers endow our robot arm with the distinctive
capability of safe interaction with surroundings along entire
manipulator surface (including joints). The applied force vector
can be calculated from the values of joint torques and contact
point coordinates.
Master side (Fig. 1(a)) includes exoskeleton robot arm with 6
DOF (Fig. 1(b)) and tactile display BraTact to present cutaneous
stimulation when collision with object is detected. The orientationof the human elbow is controlled by the tilt sensor. Teleoperated
robot arm has 4-DOF: Roll, Pitch, Yaw joints of a shoulder, andPitch joint of an elbow (Fig. 1(c)). Each joint is equipped with
optical torque sensor directly connected to the output shaft ofharmonic drive.
Proposed sensory system of slave robot (tactile skin to detect
contact point and torque sensors distributed into each joint formeasurement of applied force) patterns on the human tactile
system. Our sense of touch can be separated into kinesthetic andcoetaneous. Kinesthetic stimulations, produced by forces exertedb) Master arm

Figure 1. Robot teleoperation system TeleTA

The robot arm is covered with Kinotex tactile sensor measuring
the pressure intensity through amount of backscattered lightfalling on photodetector [2]. The sensitivity, resolution, anddynamic range of this artificial skin are comparable to those of ahuman. It should be mentioned, however, that it is useful only forcontact area and contact point recognition (cutaneous channel).
Applied force sensitivity is very low due to large hysteresivity,
high non-linearity of the output, and limited sensing range. The
task of load measurement is accomplished by the developed
optical torque sensors aimed at torque measurement in wide range
with high accuracy (kinesthetic channel).
In order to recognize the contact region, we employed thewatershed algorithm, an image processing segmentation techniquethat splits an image into areas based on the topology of the image[3]. The accurate estimation of the contact point can be obtainedby computing the center of gravity of the contact pattern c(xc,yc)of the neighborhood Ω by:⎛ ∑ xi f i ( xi , yi ) ∑ yi f i ( xi , yi ) ⎞⎜ x , y∈Ω⎟,
x , y∈Ωc ( xc , yc ) = ⎜,f i ( xi , yi )∑ fi ( xi , yi ) ⎟⎟⎜ x∑
x , y∈Ω⎝ , y∈Ω⎠(1)

where fi(xi,yi) is the pressure intensity level of the taxel i withcoordinates (xi,yi).
In the remainder of the paper we describe the methods of object
parameter identification, techniques of object property
presentation by the developed tactile display, and the results ofuser study on object property presentation.

7-3-1 Hongo, Bunkyo-ku, Tokyo.
E-mail: dima_teterukov@ipc.i.u-tokyo.ac.jp

978-1-4244-3858-7/09/$25.00 ©2009 IEEEc) Remote robot

97

2

OBJECT PARAMETERSENSINGIDENTIFICATIONTACTILE

WITH

where hi equals half of the robot link thickness.

During the first stage of control the robot links rotate until one ofthem contacts the object. The control system detects the contact
state using reference and actual torque values in the joints.
During contact transition we can acquire information about
collision danger of contacting object through its stiffness
estimation and represent this information to the operator. This canbe done by establishing stiff contact through PD control of robot
arm with high P-gain until joint torque exceeds the thresholdvalue of 0.05 Nm. The robot was commanded to follow the
trajectory in free space with constant angular velocity. An object
was placed on this path so that the second link would contact it.
The joint torque was recorded for the fourth joint while contactingwith object. The Fig. 2(a-e) shows experimental results when link
comes into contact with objects having different stiffness varying
from very low rate to very high, namely, piece of sponge, rubbersponge, rubber, chemical wood, and aluminum, respectively. The
time derivative of torque during impact is given in Fig. 2(f).

Joint torque τ4, Nm

θ4 = 0.4184 deg
τ4 = 0.05 Nm

0.04

0.02

θ4 = 0.1919 deg
τ4 = 0.0202 Nm

0.01
0.00
0.0

a)

0.1

C'

B

1

A

0.2

0.3

0.4 .000

b)

0.035

0.070

0.01

0.014

0.028

0.042

d)

0.01

0.02

0.03

Joint angle θ4 , deg

0.20
θ4 = 0.0334 deg
τ4 = 0.049 Nm

0.04

=

θ4 = 0.0237 deg
τ4 = 0.0208 Nm

0.056 .00

Joint angle θ4 , deg

0.05

ki =

Force arm length PC4 = 0.1755 m

θ4 = 0.0344 deg
τ4 = 0.0211 Nm

c)

0.15

Force arm length PC4 = 0.175 m

Fni

δi

0.01

Pick value
Time derivative of torque, Nm/s

0.016

0.024

0.00

Joint angle θ4 , deg

0.032 0.0f)

-5

5.0x10-4

1.0x10-4

1.5x10(PCi ∆θi P + h cos arctan(hi PCi )
2
i

)

.

(4)

Time t , sec

SpongeRubberspongeRubberWood∆θi 10-4 [rad]

39.53

10.82

4.12

3.84

∆τi 10 [Nm]

2.98

3.0

2.79

2.92

The total elastic deformation δi is made up of elastic
deformation caused by object compliance δo and one generated byjoint flexibility δri. That is, we can write:

It is apparent from the plots presented above that the stiffer
contacting object, the smaller angle of robot joint rotation. The
elastic deformation of the object and inherent compliance of robot
joints lead to rotation of robot arm by angle ∆θi during contact
transience (Fig. 3).
The distance, on which contact point C on the robot link surface
1 moves perpendicularly to the radius ri under torque ∆τi, is equal
to ri∆θi (since the angle ∆θi is fairly small). The unknown angle ϕican be found by taking into account that ∠C’CB=∠OCA=ϕi:⎛ hi ⎞⎛ OA ⎞⎟,⎟ = arctan ⎜⎜⎟⎝ CA ⎠⎝ PCi ⎠

Parameter-2-4

2.0x10Figure 2. Experimental results of stiffness estimation

ϕi = arctan ⎜∆τ i=PCi ∆θi ri cos (ϕi )Table 1. Findings from experimental diagrams

-0.05

0.008

PC2i + hi2

By linear approximation, we take into account only two valuesof θi for τi nearest to 0.02 Nm and 0.05 Nm. Then, from theexperimental diagrams (Fig. 2(a-e)) we obtain ∆θi and ∆τi and list
them in Table 1.

0.04

0.10

θ4 = 0.0176 deg
τ4 = 0.0197 Nm

=

∆τ i
2
Ci

0.05

0.02

0.00
0.000

(3)

through consideration of right triangle ∆CAO.
Now, we can easily solve for the total stiffness:

0.14

θ4 = 0.0457 deg
τ4 = 0.05 Nm

Force arm length PC4 = 0.17 m

0.02

0.105

Joint angle θ4 , deg

θ4 = 0.058 deg
τ4 = 0.049 Nm

0.00
0.000

δ i = ∆θi ri cos (ϕi ) .

θ4 = 0.0716 deg
τ4 = 0.02 Nm

Joint angle θ4 , deg

0.04

Joint torque τ4, Nm

koRadius of contact point trajectory ri is found as ri =0.05

Joint torque τ4, Nm

ϕibo Rni

The total elastic deformation is calculated from the right
triangle ∆CBC’ as:Force arm length PC4 = 0.17 m

0.03

e)

PCi
ri

∆θi
hi

kri

1'

θ4 = 0.1336 deg
τ4 = 0.05 Nm

Force arm length PC4 = 0.17 m

0.03

τi
O

ϕi

ri

bri

δi

C

Figure 3. The scheme for elastic deformation calculation

0.05

0.03

Fni
Fni

δi =

Fni
R
F
=δ o +δ ri = ni + ni ,kiko kri(5)where Rni is the reaction, absolute value of which equals Fni; ko
and kri are stiffness of the object and stiffness of robot link,respectively.
The coefficient kri is mainly defined by torque sensor stiffness,harmonic drive stiffness, structural flexibility, and P-gain
magnitude. Detailed examination has showed that complex

(2)

98

theoretical model of robot link stiffness can hardly provide
accurate estimation of kri. Therefore, we can set the value of robot
link stiffness close to total stiffness in the hardest contact case.
This assumption is valid because during impact with hardenvironment, such as aluminum plate, the contact deformation of
the object is too small to be accounted for (ko ≈ ∞). Thus, using
Eq. (2)-(4) and the data presented in Fig. 2(a-e) we derive
unknown value of kri:

kri =The node points obtained by employing the contact point
detection algorithm can be interpolated by B-splines. The spline
method solving the connection problem that exists with othertechniques, features superior controllability, and desired
continuity [5]. B-spline curve is expressed as a convex
combination of polygon vertex position vectors. To obtain C2
continuity, we employ cubic uniform B-spline. The segment i of
spline curve Pi(t) is a cubic parametric polynomial described by:

0.0293 Nm
N=3468.6 .
0.175 ⋅ 2.758 ⋅10−4 ⋅ 0.1776 ⋅ 0.986 m 2
m

Pi ( t ) =The object stiffness is calculated from the following equation:ko =kri ⋅ ki ,kri − ki

1 3
⎡t
6⎣

t2

⎡ −1 3 −3⎢ 3 −6 3
t 1⎤⎦ ⎢⎢ −3 0 3⎢⎣1 4 1(7)(6)
where Pi-1, Pi, Pi+1, and Pi+2 are the control points (preliminary
calculated contact points); i = 1, 2, …, n-2; n+1 is the number ofgiven control points; t ∈ [0,1] is the global parameter.
An actual spline curve is made of these curve segments Pi(t).
Let us estimate the error of object shape approximation by using
proposed approach. We assume that object has circular contour ofradius R, and the contact points PCi placed on it are equidistant(Fig. 5).

where ki is defined by Eq. (4).
Derived values of the total stiffness ki and object stiffness ko are
given in Table 2.
Table 2. Total stiffness and object stiffness
Parameter

SpongeRubberspongeRubberWoodki [N/m]

260.8

959.3

2343.82469.0ko [N/m]

282.0

1325.97223.98562.9yP3

P2

The obtained results demonstrate strong correspondence ofcorrelation among calculated object stiffness with that of real
objects. Specifically, we can define that sponge and rubber spongematerial are safe for interaction, but rubber, wood, and metal posethreat while striking the robot arm.
3

1 ⎤ ⎡ Pi −1 ⎤0 ⎥⎥ ⎢⎢ Pi ⎥⎥ ,0 ⎥ ⎢ Pi +1 ⎥⎥⎥⎢0 ⎦ ⎣ Pi + 2 ⎦P1R
2π m PMSHAPE RECOGNITIONO

Studies on human tactile perception show that edge or contour
following is one of the common exploratory procedures thatpeople use for the determination of object geometry [4]. We
propose the tactile object shape recognition method based on theacquisition and formulation of information as image primitives.
Tactile images are generated when robot arm registers a series ofcontact points with an object. Graphical modeling of the
movement of the robot arm following along round contour isshown in Fig. 4.

πm

P0

x

R
B-spline curve

Pm-1
object contour

Figure 5. Method of object shape approximation

The coordinates of contact points PCi are defined by:

L2

L1

⎛
⎛ 2π i ⎞⎛ 2π i ⎞ ⎞ , (8)PCi = ( R cos θi , R sin θi ) = ⎜ R cos ⎜⎟ , R sin ⎜⎟⎟m⎝⎠⎝ m ⎠⎠⎝
where m is the number of control points (equals n+1), i= 0, 1, …,
m-1. For the sake of simplification of further computations, we
assume the radius R of one unit.
The worst approximation is obtained midway between control
points, i.e. Pi(0.5). The midpoint of a cubic segment, however, iseasily derived from Eq. (7) as:

Robot
ObjectPM = P1 ( 0.5) =Figure 4. Circular shape following

99

1( Pm−1 + 23P0 + 23P1 + P2 ) .
48(9)It can be seen from the papers presented above that, typically,
the huge amount of actuators are arranged in regular grid pattern.
It should be mentioned here, that heavy tactile display placed on
the arm surface degrades the mobility and increases joint muscle
loading. Therefore, our objective was to find out the approach toeffective presentation of tactile information.
Several attempts were made to explore the human tactile
patterns for effective communication with mobile devices [13],[14]. Chen et al. [15] examined the human ability to localize asingle vibration source on dorsal and volar sides of the forearm
near wrist. For the experiments a 3-by-3 tactor array was placedon the dorsal and volar parts of the wrist, respectively. An
important finding was that on average only 4 tactor locations
could be correctly identified on both sides of the wrist.
The developed tactile bracelet BraTact (Bracelet with Tactors)
incorporates six vibration motors with holders linked by elastic
band. Proposed shape takes advantage of the facts that we alreadyused to wear bracelet-shaped watches and accessories, and thatsuch shape of the tactile display can fit to the humans of different
sizes. To enhance the localization ability, we arrange the tactors ina zigzag pattern (Fig. 6).

After substitution of the control point coordinates from Eq. (8)to Eq. (9) we have:⎛ (1 + cos θ )(11 + cos θ ) sin θ (11 + cos θ ) ⎞ , (10)PM = ⎜
,
⎟
24
24
⎝
⎠
where θ = 2π/m.
The deviation E (%) from a true circle is expressed as:)(

E = 1 − PM2 ×100 =12 − (11 + cos ( 2π m ) ) cos (π m )12× 100 .

The findings of deviation for different number of control points
are listed in Table 3.
Table 3. Deviation from a true circle
m

12

16

20

24

28

32

36

E [%]

4.49

2.54

1.63

1.14

0.836

0.64

0.51

Vibration motor
Holder

The proposed approach based on B-spline can therefore provide
an excellent approximation to a circle. Since the robot angular
iterations are sufficiently small, the number of control points ishuge. This fact results in a very precise contour recognition.
The curvature of the curve segment i is calculated as:

Pi =Pix′ Piy′′ − Piy′ Pix′′ .

( P′ + P′ )
2
ix

2
iy

3/ 2

Cover

Dummy tactor(11)

Wiring hole

The algorithm of contour recognition of the rectangular-shaped
object is as follows. In presence of vertex, the lines intersect in
one point. When coordinates of intersection point change
drastically, the subsequent vertex is recognized. The line
connecting two vertices defines the edge of the object.
More complicated shapes can be detected by combination of Bspline interpolation, vertex, and edges recognition. If the curve
defining vectors Pi-1, Pi, Pi+1, and Pi+2 lie collinear, the curve
segment defined by those four position vectors degenerates to aline segment. This line segment connects continuously to theneighboring curve segments with continuity up to curvature vector.
This feature is extremely useful in the case of complex shaperecognition.
4

DEVELOPMENT
BRATACT

OF THE

Lugs

Figure 6. Tactile display BraTact

The inner surface of the holder has concave profile to match thecurvature of human arm surface. The designed tactile bracelet isshown in Fig. 7.

ERGONOMIC TACTILE DISPLAY

In order to deliver the sense of object touch to the operator, theforce-feedback devices [6], [7] (generating kinesthetic stimuli)
and tactile-feedback system [8] (evoking cutaneous stimuli on
local area of the skin) are widely applied. The devices of former
type are able to exert strong forces on human body and limit user
motion in a natural manner. However, they are cumbersome, havenarrow workspace, and enable to generate force only at the humanhand. When it comes to the contact presentation on the humanarm, tactile displays are preferable ones. They can convey contact
cues [9], direction [10], and distance [11] information. Recently,
haptic gadgets displaying information on a large area of thehuman body are gaining increased attention by researchers. In[12], reconfigurable, wearable system TactaVest for delivering
vibro-tactile stimuli is presented.

Figure 7. Tactile bracelet

The small flat coreless vibration motors FM34F with diameterof 12 mm and thickness of 3.4 mm produce tactile stimulation onhuman skin. The control signal is generated by PC (Fig. 8).
As it follows from the graph of vibration motors FM34F motor
characteristic, the relationship between voltage (current) andfrequency is essentially linear. Therefore, the level of current intactor circuit corresponds the level of vibration. The tactile display
was connected to the Motor Driver Unit controlled by the signals
from D/A board.

100

PC

presented by simultaneous activation of all three tactors, but with
time-varying intensities with duration of 2 sec. Moreover,intensity level in the middle tactor was larger than in neighboring
ones. To draw the line, we tried to reproduce the apparent motion
illusion. For that, two tactors were activated simultaneously butwith opposite time-varying intensities levels. That is, when thefirst tactor had the maximum vibration intensity the opposite
tactor had the lowest intensity and vice versa. The point waspresented by activation of single tactor with ramp signal.
Stiffness presentation. In Static Stiffness Experiment all tactors
were activated simultaneously with the same intensity level of 2
sec. duration. Five vibration intensity levels (Level 1 (167 Hz)(Pattern A), Level 2 (188 Hz) (B), Level 3 (208 Hz) (C), Level 4(229 Hz) (D), Level 5 (250 Hz) (E)) representing object stiffness
from very low rate to very high were chosen. The darkness levelof gray color in Fig. 10 represents the vibration amplitude level.

D/A

Wires

Wires
Motor Drivers

Tactile display

Figure 8. Control system of BraTact

5

USER STUDY METHODOLOGY

5.1
Apparatus
A developed tactile display BraTact was used to present the shape,and stiffness of object. The smallest participant’s forearm
circumference was 21 cm, so the distance between tactors always
was 3.5 cm or more, i.e. larger than a single receptive field (about
2.5 cm [16]).

Level 1(Sponge)

5.2
Participants
A total of 5 males and 1 female with no previous knowledge about
experiment were examined. Their age varied from 23 to 31.
The participants were recruited among the students and staff ofthe Tachi Laboratory of the University of Tokyo and did notreceive any compensation for their participation. None of thesubjects reported any sensory difficulties.

Level 4 (Wood)

Rectangle (B)Triangle (C)Arc (D)Line (E)

Point (F)Level 3 (Rubber)

Level 5 (Metal)

Figure 10. Tactile presentation of the object stiffness

5.3
Procedure and Stimuli
Object shape presentation. Object shape information isimportant for operator. While colliding, the objects with smooth
shape attenuate the impact forces. Thus, knowledge of object
shape can improve performance of motion planning in clutteredenvironment. The intuitive approach of representation of simple
object shapes (circle (Pattern A), rectangle (B), triangle (C)) andshape primitives (arc (D), line (E), point (F)) is presented in Fig. 9.
The dashed ovals depict the active tactors.

Circle (A)Level 2 (Rubbersponge)In Dynamic Stiffness Experiment all tactors were activatedsimultaneously for given level but with time-varying intensity.
The time-dependent pattern was chosen to represent the contact
point motion during contact transient (when we collide with stiff
object our arm immediately stops moving forward). The stiffest
material, aluminum, was presented as short ramp impulse withduration of 0.3 sec. The less stiff material the longer duration andsmaller amplitude of impulse were presented to human`s forearmskin. To mimic nonlinearity of the sponge material (see Fig. 2(a)),
the sine wave pattern was presented with duration of 2 sec.
The experiment procedure is as follows. To mask auditory cuesof the tactor vibration, subjects wore headphones producing pinknoise of 65 dBA. They were asked to sit down at the table andgrasp vertical stick at the edge of the table to establish staticreference position. The arm with attached display BraTact was not
limited by physical constraints and was relaxed. Theseexperimental conditions imitate normal position of the operator’sarm. The elastic belt embedded in BraTact device provided tightcontact of motors and skin. Subjects were informed that theexperiment aimed at testing their ability to discriminate between
various patterns. Additionally, they were shown a diagram ofpossible patterns of shape and stiffness presentation. All theparticipants were given 18 trials practice sessions before
experiment for object presentation and 15 trials for stiffnesspresentation (each pattern was presented 3 times). They wereallowed to look at the visual representation of the patterns at all
times of practice session and to identify them.
In total, 30 stimuli (6 patterns were repeated 5 times in arandom order) were presented during object shape discrimination
experiment and 25 stimuli (5 patterns were repeated 5 times in arandom order) for object stiffness recognition experiment. The
total number of stimuli sensed by participants equals 110

Figure 9. Tactile presentation of the object shape

Dynamic Shapes Experiment was aimed at presenting theshapes in a more transparent manner. The duration time of tactor
activation for all shapes was the same - 2 sec. For round,rectangular and triangular shape, the tactors were activated one by
one in counter-clockwise direction. This allows the human todraw in his mind the path represented by tactor. The arc was

101

7

(including static and dynamic mode). After each stimulus, thesubject marked the table cell corresponding to the pattern had
been detected. The subjects were limited to answer within 10
second. The average duration of four sessions of experiment was
40 min.
6

RESULTS AND DISCUSSION
The results of user study are listed in Table 4 - Table 7.
Table 4. Group Mean Percentage of Recognition of Shapein Dynamics
Group Mean
Percentage, %Actual Pattern
A
B
C
D
E
F

Subject Response
A
100.0
0
0
3.3
3.3
0

B
0
83.3
3.3
0
3.3
3.3

C
0
13.3
96.7
0
3.3
0

D
0
0
0
73.3
3.3
0

E
0
0
0
10.0
86.7
3.3

F
0
3.3
0
13.3
0
93.3

Acknowledgments. The research is supported in part by a Japan
Society for the Promotion of Science (JSPS).

Table 5. Group Mean Percentage of Recognition of Shapein Static Condition
Group Mean
Percentage, %Actual Pattern
A
B
C
D
E
F

REFERENCES[1]

E. R. Kandel, J. H. Schwartz, and T. M. Jessell. Principles of Neural
Science. New York: McGraw-Hill, 2000.
[2] Optic fiber tactile sensor Kinotex, Nitta Corporation. [Online].
Available: http://www.nitta.co.jp/[3] J. Serra. Image Analysis and Mathematical Morphology. London:
Academic Press, 1982.
[4] R.L. Klatzky and S. Lederman. Intelligent exploration by the humanhand. Dexterous Robot Manipulation. S.T. Venkataraman and T.
Iberall, Ed. New York: Springer-Verlag, pages 66–81, 1990.
[5] F. Yamaguchi. Curves and Surfaces in Computer Aided Geometric
Design. Berlin: Springer-Verlag, 1988.
[6] PHANTOM line of haptic devices. [Online]. Available:
http://www.sensable.com[7] CyberGrasp. [Online]. Available: http://www.immersion.com[8] J.B.F. van Erp and H.A.H.C. van Veen. A multi-purpose tactile vest
for astronauts in the international space station. In Proc. Int. Conf.
EuroHaptics, pages 405−408, Dublin, 2003.
[9] R. D. Howe, D. A. Kontarinis, and W. J. Peine. Shape memory alloy
actuator controller design for tactile displays. In Proc. 34th IEEE
Conf. on Decision and Control, pages 3540−3544, 1995.
[10] H.A.H.C. van Veen and J.B.F. van Erp. Providing directional
information with tactile torso displays. In Proc. Int. Conf.
EuroHaptics, pp. 471−474, Dublin, 2003.
[11] R.W. Cholewiak. The perception of tactile distance: Influences ofbody site, space, and time. Perception, 28(7):851−875, 1999.
[12] R. W. Lindeman, Y. Yanagida, H. Noma, and K. Hosaka. Wearable
vibrotactile system for virtual contact and information display.
Virtual Reality, 9:203−213, 2006.
[13] I. Oakley, Y. Kim, J. Lee, and J. Ryu. Determining the feasibility offorearm mounted vibrotactile displays. In Proc. Symp. on Haptic
Interfaces and Teleoperator System, pages 27−34, Alexandria, VA,
2006.
[14] E. Piateski and L. Jones. Vibrotactile pattern recognition on the armand torso. In Proc. First Joint EuroHaptics Conf. and Symp. on
Haptic Interfaces for Virtual Environment and Teleoperator System,
pages 18−24, Pisa, 2005.
[15] H-Y. Chen, J. Santos, M. Graves, K. Kim, and H. Z. Tan. Tactor
localization at the wrist. In Proc. Int. Conf. EuroHaptics, pages
209−218, Madrid, 2008.
[16] R.W. Cholewiak and A.A. Collins. Vibrotactile localization on thearm: Effects of place, space, and age. Perception and Psychophysics,
65:1058-1077, 2003.

Subject Response
A
60.0
33.3
10.0
0
0
0

B
26.7
26.7
33.3
3.3
10.0
0

C
6.7
26.7
43.3
6.7
20.0
0

D
3.3
3.3
0
76.7
16.7
0

E
3.3
10.0
13.3
10.0
53.3
3.3

F
0
0
0
3.3
0
96.7

Table 6. Group Mean Percentage of Recognition of
Stiffness in Dynamics
Group Mean
Percentage, %Actual Pattern
A
B
C
D
E

Subject Response
A
100.0
0
0
0
0

B
0
80.0
16.7
0
0

C
0
13.3
73.3
20.0
0

D
0
6.7
10.0
70.0
3.3

E
0
0
0
10.0
96.7

Table 7. Group Mean Percentage of Recognition of
Stiffness in Static Condition
Group Mean
Percentage, %Actual Pattern
A
B
C
D
E

Subject Response
A
73.3
13.3
0
0
0

B
20.0
50.0
16.7
0
0

C
6.7
16.7
50.0
23.3
6.7

D
0
20.0
20.0
53.3
23.3

CONCLUSION

The increasing complexity of tasks performed by teleoperation
systems requires to deliver and to display tactile information to
the human operator effectively in order to ensure safe andefficient interaction with environment. The sensory system of thedeveloped slave robot supports the local admittance controllers
generating compliant motion with information on exerted force.
The methods of object stiffness estimation and object shaperecognition were elaborated. These approaches can also be
extended for object exploration in 3D space.
The tactile display BraTact supporting the presentation oftactile information by means of the bracelet with tactors wasdeveloped. The results of the user study revealed that the Dynamic
mode of presentation of the object properties was more intuitive,
and, therefore, resulted in very high level of discrimination
accuracy. The future step is elaboration of the concept of“Dynamic tactile drawing”, when graphical information ispresented by tactile patterns generated discretely in time.

E
0
0
13.3
23.3
70.0

The ANOVA results revealed that it was significantly easier forparticipants to recognize stiffness of materials B (p=0.001<0.05)
and E (p=0.04<0.05) during Dynamic Condition than in Static
Condition. Shape patterns A, B, and C were significantly easier torecognize during Dynamic Condition than in Static Condition(p=0.006<0.05,
p=0.002<0.05,
and
p=0.007<0.05,
correspondingly). The important point is that the recognition rateof the line presented by apparent motion illusion was much morehigher than that of line presented in Static mode. Based on thisevidence, we can use this effect to draw more complex shapes.

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