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Industrial Robot: An International JournalEmerald Article: A robotic system based on fuzzy visual servoing forhandling flexible sheets lying on a table

Paraskevi Zacharia, Nikos Aspragathos, Ioannis Mariolis, Evaggelos Dermatas

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based on fuzzy visual servoing for handling flexible sheets lying on a table", Industrial Robot: An International Journal, Vol.6 Iss: 5 pp. 489 - 496

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Research article

A robotic system based on fuzzy visual servoingfor handling flexible sheets lying on a table

Paraskevi Zacharia, Nikos Aspragathos, Ioannis Mariolis and Evaggelos Dermatas

Department of Mechanical and Aeronautics Engineering, University of Patras, Patras, Greece

AbstractPurpose The purpose of this paper is to present a flexible automation system for the manipulation of fabrics lying on a work table and focuses on thedesign of a robot control system based on visual servoing and fuzzy logic for handling flexible sheets lying on a table. The main contribution of thispaper is that the developed system tolerates deformations that may appear during robot handling of fabrics due to buckling without the need for fabricrigidization.Design/methodology/approach The vision system, consisting of two cameras, extracts the features that are necessary for handling the fabricdespite possible deformations or occlusion from the robotic arm. An intelligent controller based on visual servoing is implemented enabling the robot tohandle a variety of fabrics without the need for a mathematical model or complex mathematical/geometrical computations. To enhance its

performance, the conventional fuzzy logic controller is tuned through genetic algorithms and an adaptation mechanism and the respective performanceis evaluated. The experiments show that the proposed robotic system is flexible enough to handle various fabrics and robust in handling deformationsthat may change fabrics shape due to buckling.Findings The experiments show that the proposed robotic system is flexible enough to handle various fabrics and robust in handling deformationsthat may change fabrics shape due to buckling.Research limitations/implications It is not possible to cover all the aspects of robot handling of flexible materials in this paper, since there are stillseveral related issues requiring solutions. Considering the future research work, the proposed approach can be extended to sew fabrics with curvededges and correcting the distortions presented during robot handling of fabrics.Practical implications The paper includes implications for robot handling a variety of fabrics with low and medium bending rigidity on a workingtable. The intent of this paper deals with buckling in context of achieving a successful seam tracking and not the correction strategy against folding orwrinkling problems.Originality/value This paper fulfils an identified need to study the fabrics behavior towards robot handling on a working table.

Keywords Fuzzy control, Robotics, Materials handling equipment, Textiles, Intelligent agents

Paper type Research paper

1. Introduction

The development of intelligent control systems with vision

feedback enables robots to perform skillful tasks in more realistic

environments. Flexible materials pose additional problems

compared to rigid bodies due to low bending rigidity,

non-linearity, anisotropy and structure complexity. Thus, their

mechanical behavior is rather difficult to be modeled.

A method was introduced in Torgerson and Paul (1988) for

the manipulation of fabrics towards the needle employing a

geometric approach for path-point computation related torobot motion. The deviation errors lie in the range of 3-5 mm,

while visual feedback was not used. The deformation problems

that arise towards handling of fabrics were not discussed.

The FIGARO system (Gershon and Porat, 1988, 1986)

performed handling and assembling operations using two

superimposed servo control systems. The first system

maintains a small tension moving the robot forward with

the cloth and the second one rotates the cloth about the

sewing needle to produce a seam parallel to the edges. The

system performed best with shirting and worsted woven

fabrics, which have a reasonable resistance in buckling.Robotic manipulation strategies were investigated in

Gershon (1993) for handling limp materials proposing a

parallel process decomposition of the robot sewing task

(Gershon, 1990). A robot arm manipulates the fabric,

modeled as a mass-spring-damper system, to modify its

orientation and control the fabric tension during the sewing

process, which was decomposed into four concurrent

processes within a superposition parallel architecture.The current issue and full text archive of this journal is available atwww.emeraldinsight.com/0143-991X.htm

Industrial Robot: An International Journal

36/5 (2009) 489496

q Emerald Group Publishing Limited [ISSN 0143-991X]

[DOI 10.1108/01439910910980213]

This paper is financed by the General Secretariat for Research andTechnology of Greek Government as part of the project XROMA-Handling of non-rigid materials with robots: application in roboticsewing PENED 01. This project is integrated to EU-funded FP6Innovative Production Machines and Systems Network of Excellence.

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An automated sewing system was developed (Kudo et al.,

2000) including two coordinated robots handling the fabric

on the table. The programming included hands coordination

during task execution by applying hybrid force/position

control and synchronization with the sewing machine speed.

Visual feedback was used to control the seam path and the

deviation from the desired trajectory and the trajectory error

was within ^0.5mm.The present work is part of a project for the development of

a robotic workcell for sewing fabrics. The project includes

handling tasks (ply separation, translation, placement, folding,

feeding and orientation), tension control and quality control of

fabrics and stitches (Moulianitis et al., 1999; Koustoumpardis

and Aspragathos, 2003; Koustoumpardis e t a l., 2006;

Zoumponos and Aspragathos, 2008). The innovation of this

work is the design of a control system that is capable of reliably

handling flexible materials towards sewing. The main

contribution of this paper is the design of a robot fuzzy visual

servoing controller forhandling fabrics on a work table,which is

enhanced using genetic-based and adaptive control. The

system works without the need for special mathematical

computations and can tolerate deformations on condition thatthey do not appear in the seam segment to be sewed.

2. Robot handling of flexible sheets towardssewing

The developed vision system is a combination of image- and

position-based control system (Hutchinson et al., 1996;

Corke, 1996). The image-based analysis is used for the

identification of the fabrics shape, the needle-point and the

sewing lines orientation. The needle position is also known in

the robot coordinate system. The end-effectors position is

unknown in the image coordinate system; however, the robot

system gives feedback to the control system of the current

end-effectors position in the robot base coordinate system.

Moreover, the relation between the robot- and the image-coordinate system is known from the calibration of the

camera. This approach makes the system more flexible and

limits the calibration errors:. The movement towards a point (e.g. a needle). First, the

cameras image extracts the vertices of the fabric. After the

starting seam edge is identified, the distance (r) between

the starting seam vertex and the sewing needle and the

orientation angle (u) formed by the starting seam segment

and the sewing line (Figure 1(a)) are computed. The linear

(u) and angular end-effectors velocity (v) are derived

through the designed fuzzy decision system (Section 4).

The velocity (u) is the resultant of the velocities in x- and

y-direction. The angular velocity (v) is referred to the

z-direction, since the x- and y-component are zero;

otherwise, collision between the gripper and the worktable

would occur. Given the time step Dt and the angle f,

i.e. the orientation ofr in the image coordinate system, thenew position and orientation of the end-effector is

computed. Therefore, the fabric is transferred to a new

position with new orientation by moving end-effector

along the direction of r (defined by f) rotated around

z-axis until the angle becomes u0. The fabric is stuck on

the gripper to avoid slipping; however, the system can

cope with slipping, but it is more probable to make some

oscillations before the fabric reaches the needle within an

acceptable tolerance.. The sewing process. During sewing, the fabric is guided along

the sewing line with a constant velocity, which should be the

same with the velocity of the sewing machine, so that good

seam quality is ensured. At each time step, the current

image of the fabric is captured in order that the orientationerror is determined and then, fed back to be corrected by

rotating the fabric around the needle.. The rotation around the needle. The robot rotates the fabric

around the needle until the next seam segment is aligned

to the sewing line. The end-effector is enforced to make a

circular motion around the needle, since it has penetrated

into the fabric. The fuzzy system outputs the end-

effectors angular velocity (v) around an axis

perpendicular to the table at the needle-point.

3. Image-based estimation of fabric location

In real-time systems, the estimation of the fabric position and

seam line trajectory must be fast and accurate. In this

direction, a novel estimator of seam lines trajectory uses asystem of two cameras. The first camera detects the fabric

location during robotic handling towards the needle and its

view covers the worktable, while the high-resolution images of

the second camera are used to detect the exact position of the

fabric edges in the area near the needle.

3.1 Detection during robot handling

The detection of the fabric position is affected by the

transportation of the fabric, the partial occlusion caused by

Figure 1 Scene of the fabric lying on the work table

(a) Without deformations (b) With deformations

Sewing line

Needle

y-axis

r

x-axis

Needle

Sewing line

r

y-axis

x-axis

A robotic system based on fuzzy visual servoing

Paraskevi Zacharia et al.


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the robotic arm and possible deformation of the fabric (Koch

and Kashyap, 1987; Ayach and Faugeras, 1986). A model and

scene description (SD) method has been evaluated on the

detection of rigid industrial parts (Ayach and Faugeras,

1986), while several tasks involving fabric handling aided by

visual information are presented in Fahantidis et al. (1997),

addressing the problem of fabric deformation, in occlusion

absence.In the proposed three-stage fabric detection method,

efficient solutions presented in Ayach and Faugeras (1986)

are modified to face both partial occlusion and deformation

problems. In the first stage, thefabric is placed into the working

area in the absence of any occlusion or deformation. The points

of high curvature, called fabric-corners, are detected by

applying Harris corner detector (Deriche and Giraudon, 1990;

Vernon, 1991) on the acquired grayscale image. The fabrics

shape is then approximated by the planar polygon having these

fabric-corners as vertices and a model description (MD) of the

fabric is generated using the image coordinates of the fabric

corner, the polygon sides lengths and angles.

During robot handling, grayscale-images of the fabric are

acquired (Figure 2(a)) and thresholded using the ISODATA

method (Ridler and Calvard, 1978) (Figure 2(b)) segmenting

the binary image into objects using the eight-connected

components criterion (Haralick and Linda, 1992), and

keeping the largest object (LO) (Figure 2(c)). Each pair of

gray-scale and binary images is used to generate a SD

containing all corners detected in the scene image. The novel

method of detecting potential fabric-corners is accomplished

by applying the Harris algorithm to both images and selecting

only the common corners.In the last stage, multiple hypotheses for all triples of SD

corners are created by selecting the MD elements with error

in polygon angle and sides length less than the corresponding

threshold that are experimentally derived. For each

hypothesis, the corresponding fabric corners are used to

define the optimum rigid transformation of the fabric withrespect to the least square error. The rigid transformation of

the fabric with maximum common area to LO results to the

estimation of the actual fabric position. In the proposed

method, since only segments of the fabric are compared, apart

from coping with partial occlusion, a certain amount of

deformation is tolerable as well. Actually, if at least two

successive vertices of the fabric are not deformed or occluded,

the method can generate a valid hypothesis.

3.2 Detection of fabric edges near needle

In the case of the second camera, only a part of the fabric is

contained in the image and no occlusion occurs. Therefore,

after applying the Harris corner detector, all the detected

corners belong to the fabric and the one closest to the needle

is defined as the first sewing corner. The first sewing corner

and the two corners that appear at the image borders define

two sides. The side with minimum absolute differencebetween its slope and the slope of the sewing line is selected

as the desired sewing side of the fabric and the border corner

of that side is defined as the second sewing corner. In the rest

video images, the Harris corner-detection process is repeated

and the sewing corners of the new image are the detected

corners closest to the sewing-corners of the previous image.

4. The fuzzy control system

Since modeling the mechanical behavior of fabrics for real-

time applications is rather difficult due to their low bending

rigidity, an approach based on a fuzzy logic controller (FLC)

is developed considering the robustness and the fast response

requirements. The block diagram for the control system isshown in Figure 3, where the symbols in parentheses stand

for the fuzzy system that controls the orientation of the

end-effector. The controller inputs are the position error

(er) and the orientation error (eu), which are computed on the

image, and their time rates (e0

r) and (e0

u). The linear (u) and

angular velocity (v) of the end-effector around z-axis are the

outputs.

The membership functions for the two inputs (Figure 4(a)

and (b)) and the output of the system that controls the

translation of the fabric towards the needle (Figure 4(c)) are

experimentally tuned with various fabrics. Expert knowledge,

often afflicted with uncertainty, is summarized in the

proposition:

The larger the distance/angle and the smaller its change rate is, the faster the

fabric should move/rotate.

The fuzzy associative memory (FAM) of the system that

controls the translation of the fabric (Table I) is derived after

studying the behavior of the human workers towards sewing.

For the rule evaluation, the min operator is used and for

the aggregation mechanism the max operation is used.

The centroid defuzzification method is used to determine the

linear and angular velocity. Owing to space limitation, only

Figure 2

(a) (b) (c)

Notes: (a) Original grayscale image acquired by the first camera; (b) binary image after thresholding; and (c) binary image after selecting the largest

eight-connected object




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the study for the system that controls the translation is

presented.

An analytical model of the robot and the fabric is not

necessary, since no geometrical characteristics are taken into

account and there is no need for special mathematical

computations for different fabrics. The proposed technique is

feature-based, since only the features, r and u, are necessary

for handling the fabric and can cope with the uncertainties

that arise due to deformations.

The proposed controller can handle the possible

deformations without changes in its structure achieving the

desired accuracy on condition that the seam segment targeted

for sewing is undistorted. Figure 1(b) shows the fabric of

Figure 1(a), which has been deformed except for the seam

segment targeted for sewing. It is clear from Figure 1(b) thatthe presence of deformations does not affect the control

system, despite the fact that the shape of the fabric has

significantly changed. It is worth noting that the intent of this

paper deals with buckling in context of achieving a successful

seam tracking and not the correction strategy against folding

or wrinkling problems.

4.1 The proposed genetic fuzzy logic controllerThe parameters of each membership function are determined

using genetic algorithms (Michalewitz, 1996) as tuning mode

(Figure 3) to improve the efficiency of the system, to

overcom e the subjectivity and extensive m anual

experimentation. The optimum values for the parameters of

Figure 4 The membership functions

Small

Very small Very smallSmall

SmallMedium MediumLarge Large

Degreeofmembership

Degreeofmembership

Degreeofmembership

Degreeofmembership

1

0.8

0.6

0.4

0.2

0

1

0.8

0.6

0.4

0.2

0

1

0.8

0.6

0.4

0.2

0

1

0.8

0.6

0.4

0.2

0

0 50 100 150 200 250 300 0 50 100 150 200 250 300

Position error (pixels) Position error diagram (pixels/s)

Small Medium MediumLarge LargeVery large Very large

0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100

Error velocity (pixels/s) Error velocity (pixels/s)

(a) (b)

(c) (d)

Notes: (a) The position error; (b) the position error rate; (c) the linear velocity after manual tuning; and (d) the linear velocity after

o timization with GAs

Figure 3 The block diagram of the fuzzy logic control




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the output membership functions are determined, so that the

total time required for the fabric to reach the needle is

minimum and the deviations of the seam segment from the

needle and the seam line are less than the predefined

acceptable limits.The evaluation mechanism. The total time ttotal required for

the robot to move the fabric until reaching the needle is given

by the summary of the total steps satisfying the constraints:

jrj # e1 pixels and juj # e2, where e1 and e2 are the maximumacceptable error limits for the desired seam quality.

Encoding. The problem variables are the parameters

defining the shapes of the membership functions of the

output of each fuzzy system. The triangular fuzzy sets aredefined through three parameters and the trapezoidal fuzzy

sets are defined through four parameters. Therefore, the

chromosome can be generated by concatenating the

parameters of the membership functions using the binary

alphabet. For example:

1st fuzzy set 2nd fuzzy set 3rd fuzzy set

4th fuzzy set 5th fuzzy set

1 11..0 001..0 100..1 000..1 .....

000..0 001..1

..... ..... ..... ..... .....

..... ..... ..... ..... .....

d5c5b5a5b4 c4a4

b3 c3a3b2 c2a2d1c1b1a1

The genetic fuzzy logic controller (GFLC) was simulated and

tested for various combinations of the genetic algorithm

control parameters. The whole procedure is off-line, since the

required time for convergence is prohibitive for an online

application, and the resulted fuzzy sets of the output is shown

in Figure 4(d). Comparing Figure 4(d) to Figure 4(c),

the support of the membership function Very Small

is broadened in order to resolve the oscillations that appear

when the vertex is very close to the needle. The support of the

fuzzy set Very Large is narrowed and the membership

function becomes more abrupt, which urges the fabric to

make greater movements when it is distant from the needle.

The supports of the fuzzy sets, Small, Medium and

Large have also changed, so that the minimum time is

achieved.

4.2 The proposed adaptive fuzzy logic controller

Adaptive fuzzy logic controllers (AFLCs) (Passino and

Yurkovich, 1998; Patcharaprakiti et al., 2005) provide fast

response and can change the fuzzy parameters for improving

the control systems performance. An AFLC is also designed

for robot handling fabrics. Again, the goal is to alter the

supports of the fuzzy sets of the outputs, so that the

performance of the system is improved. The proposed AFLC

uses only qualitative knowledge of the plant and consists of

two parts: the fuzzy knowledge-based controller (Section 3.1)

and an adaptation mechanism, which tunes the output

(Figure 3) through a supervisory fuzzy system.

The adaptation mechanism resembles to the fuzzy

knowledge-based controller in the sense that it has the same

inputs (Figure 4(a) and (b)) and the same rule base, but the

output is a scaling factor (Figure 5) lying inside an

experimentally derived interval [1, 2.5]. The parametersdefining the membership functions of the output have been

optimized using the genetic algorithm presented in Section

4.1. The scaling factor changes the universe of discourse of

the output of the fuzzy knowledge base controller and

consequently, the support of each fuzzy set is changed by the

same ratio according to:

The larger the distance from the needle and the smaller its change rate is, the

greater the increase of the scaling factor.

The alteration of the universe of discourse by a ratio greater

than one results in the acceleration of the sewing process.

In addition, the AFLC has the advantage of the online tuning.

5. Experimental results

The experiments were carried out using a robotic manipulator

with six rotational degrees of freedom (RV4A) and controlled

by a PC. The robot is programmed in Melfa-Basic language

in Cosirop environment, while the analysis of the visual

information is performed with Matlab 7.1. The vision system

consists of a Pulnix analog video camera at 768 576 pixels

resolution RGB and an analog camera of the same resolution,

which are fixed above the working table in a vertical position

(Figure 6). The vision is transferred to the second camera

when the position error becomes less than ten pixels

(

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leg. The curved edges of the cloth parts are approximated

with straight lines as shown in Figure 7, since this paper

focuses on the handling of fabrics with straight edges. The

fabrics that are selected for the handling experiments are

prone to small or relatively large deformations due to

buckling, such as wrinkling or even folding.In our work, buckling modes during robot handling

are supportable on the condition that it does not appear

in the segment to be sewed. However, it still remains a

problem that should be avoided. Thus, the position where the

gripper touches the fabric is estimated in terms of reducing

the possibilities for deformation appearance, taking into

account the properties of the fabric (Koustoumpardis and

Aspragathos, 2003). It is worth noting that the intent of this

paper deals with buckling in context of achieving a successful

seam tracking and not the correction strategy against folding

or wrinkling problems. The experimental tests also showed

that deformations are likely to appear close to the gripperposition on the fabric, and not on the edge.

Since there is no standardization for deformations, it is

difficult to be quantified. However, the greater deformation

observed in buckling is about 30 mm, which is the height

between the highest and the lowest point of the fabric. In some

cases, fabrics were partially folded. In other cases, the

wrinkles induced the fabric to fold due to its own weight

forming a loop at one side of the fabric. In some experiments,

the fabric presented simultaneously both wrinkles, and folds.

Fir st, the FLC, th e GFLC and the AFLC are

experimentally tested for several times changing the initial

arbitrary location of the cloth part on the table. In some

experiments, the fabric undergoes wrinkling or partially

folding due to buckling and its shape deforms. Table II

presents the mean absolute values for the steps required toreach the needle and the position and orientation error for the

four different fabrics with and without deformations when

reaching the needle, which are derived from 15 iterations for

each tested case starting from a different location on the table.

The total steps needed for the robot to move the fabric until

its edge reaches the needle are decreased using the GFLC

and the AFLC compared to the simple FLC. For example,

the total steps for the case of the sleeve have been decreased

using the GFLC by:

14:58422 11:6667

14:5842100% < 20%

while they have been decreased by:

14:

58422 10:

0000

14:5842100% < 31:4%

using the AFLC. However, no direct comparison between

GFLC and AFLC concerning the performance is possible due

to the fact that the GFLC has been tuned off-line through

simulation tests, where some factors affecting the system,

such as the camera calibration and the robot accuracy, are

ignored.

Comparing the experiments carried out using fabrics with

and without deformations, only slight changes are observed in

the mean total steps, the mean absolute position and

orientation error. Consequently, the existence of

deformations on the fabrics does not degrade the controller

performance and does not decelerate the sewing process. Thisis the experimental proof that the FLC exhibits flexibility and

robustness against deformation, since the performance of the

controller is not affected by possible deformations.

In the following, some indicative diagrams are presented for

the total sewing process of the pocket using the GFLC. In

Figure 8(a), the position error is presented while the robot

moves from the random initial location towards the needle.

Figure 7

(a) (b) (c) (d)

Notes: (a) Sleeve; (b) trousers leg; (c) pocket; and (d) shorts leg

Figure 6 The experimental stage




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The change between the cameras happens at the seventh step,

where a slight increase in the position error is observed due to

the higher resolution of the second cameras image. The

position error in the last step is three pixels (

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6. Conclusions

The main focus of this work lies on the design of an innovative

visual servoing manipulator controller based on fuzzy logic

that aims to enable robots to handle flexible fabric sheets lying

on a work table. The designed visual servoing control system

can deal with a variety of fabrics that are likely to deform and

can cope with possible deformations due to buckling(wrinkling and folding) towards handling without degradingits performance, on condition that the seam segment to be

sewed is undistorted. The desired accuracy is achieved in

approaching the needle even in cases where deformations

appeared.

Besides the conventional fuzzy controller, which is manually

tuned, a GFLC and an AFLC are proposed and tested for

different fabrics. The experimental results show that the

proposed approach is effective and efficient in guiding the

fabric towards the sewing needle, sewing it and rotating it

around the needle. After extensive experimentation, it has

been proved to be rather simple, flexible and robust due to its

capability to respond to any position and orientation error fora variety of fabrics that are likely to present deformations.

Since it is not possible to cover all the aspects of robothandling of flexible materials in this paper, there are still

several related issues requiring solutions. Considering our

future research work, the proposed approach can be extended

to sew fabrics with curved edges and correcting the distortions

presented during robot handling of fabrics.

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Corresponding author

Paraskevi Zacharia can be contacted at: zacharia@mech.

upatras.gr




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