A model based recognition system for tactile data

North-Holland 425

Microprocessing and Microprogramming 2 4 (1988) 4 2 5 - 4 3 2

A Model Based Recognition System for Tactile Data

H

. Gulheux,

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I INTRODUCTION

A c e n t r a l characteristic of advanced a p p l i c a t i o n s o f r o b o t i c s is t h e p r e s e n c e of s i g n i ficant u n c e r t a i n t y a b o u t i d e n t i t i e s and p o s i t i o n s of o b j e c t s in t h e r o b o t ' s w o r k s p a c e ; t h u s this s e n s i n g of t h e e x t e r n a l e n v i r o n m e n t is an e s s e n tial c o m p o n e n t of r o b o t systems. Although vision t e c h n o l o g y h a s b e c o m e quite s o p h i s t i cated, t a c t i l e s e n s i n g is u s e f u l f o r l o c a t i n g and identify ing o b j e c t s , determining the texture, h a r d n e s s and t e m p e r a t u r e of o b j e c t s and a l s o w h e n visual i n f o r m a t i o n is n o t readily available as for e x a m p l e , in u n d e r w a t e r m a n i p u l a t i o n and during t h e p r o c e s s of g r a s p i n g an object f r o m say, a bin o f p a r t s . The p r o c e s s i n g of tactile d a t a is divided b e t w e e n t h e m e a s u r e m e n t of p r o p e r t i e s o f t h e objects in t h e e n v i r o n m e n t , and the i n t e r p r e t a t i o n of t h o s e m e a s u r e m e n t s . First we will d e s c r i b e t h e d a t a a c q u i s i t i o n s y s t e m and the h u m a n i n t e r f a c e a t t a c h e d to it. T h e n we will s h o w how i m p o r t a n t is t h e choice o f a t t r i b u t e s for e n a b l i n g object i n t e r p r e t a t i o n . Finally we will present a classification strategy, using a black-

board a r c h i t e c t u r e to i m p l e m e n t a s e t of d i s t r i buted knowledge sources.

II THE DATA ACQLIISITION SYSTEM a) Tactile S e n s o r s and Tactile D a t a A tactile s e n s o r is a device t h a t can d e t e c t t h e location and t h e f o r c e s of c o n t a c t with an object. We m a k e t h e d i s t i n c t i o n b e t w e e n tactile s e n s o r s which m e a s u r e f o r c e s over s m a l l areas s u c h as a fingertip, and force s e n s o r s , s u c h as a c o m p l e t e gripper which m e a s u r e t h e r e s u l t a n t f o r c e s and t o r q u e s on s o m e l a r g e r s t r u c t u r e . T h i s paper d e s c r i b e s object r e c o g n i t i o n with 2 tactile s e n s i n g m a t r i c e s i n c o r p o r a t e d in t h e end e f f e c t o r of a gripper. A m a t r i x is c o m p o s e d of an array of sensitive points with a high spatial resolution (lmm.), b u t n a t u r a l l y a low d a t a d e n s i t y ( I p o i n t every S m e . ) . The relative p o s i t i o n s of s e n s o r s w i t h r e s p e c t to t h e object are t h e a c t u a l data. F i g u r e 1 gives a d e t a i l e d d e s c r i p t i o n of t h e s e matrices.

H. Guiheux et aL / A Model Based Recognition System for Tactile Data

426

Figure

I.

Two

arrays o f

Programs w r i t t e n in 'C' c o n t r o l c o m p l e t e l y the sequencing o f the data acquisition system. First, the 6 8 0 0 0 - b a s e d board s e l e c t s an address decoder to c a p t u r e the data f r o m the required sensor. Then specific p r o c e d u r e s are carried out d e p e n d ing on t h e required task. For the tactile sensing m a t r i x , which i s capacitance based, the charge o u t p u t g e n e r a t e d by d i s p l a c e m e n t of the pin needs to be i n t e g r a t e d , amplified and finally c o n v e r t e d into digital data. The i n t e g r a t i o n p r o cedure s u c c e e d s if the i n t e g r a t o r has been r e s e t previously, o r more precisely if the capacitor o f the i n t e g r a t o r is n o t charged. The m i c r o p r o c e s s o r c o n t r o l s o f the execution o f this procedure, t h e n s e l e c t s the p r o p e r amplification gain, and t h e n initiates the analog- t o - digital c o n v e r s i o n . For the positioning p o t e n t i o m e t e r and the force sensor, a similar p r o c e d u r e is a d o p t e d . The s y s t e m has b e e n d e s i g n e d to retain maximum flexibility in the face o f p o s s i b l e modifications or f u t u r e e x t e n s i o n s . In particular, additional inputs f r o m o f f - b o a r d signals may be a c c o m m o dated at the i n p u t to the multigain s e l e c t a b l e amplifier very easily. Even a n o t h e r array of t a c tile s e n s o r s , may be i n c o r p o r a t e d into the basic s y s t e m w i t h o u t t o o much difficulty.

pressure

sensitive points

A positioning t r a n s d u c e r a t t a c h e d t o the articulation o f the gripper gives the d i s t a n c e b e t w e e n the two matrices. So, knowing the d i s p l a c e m e n t of each pin we can obtain a tactile data s e t well defined in t h e space.

b ) The Data Acquisition S y s t e m at P.C. L The s u p p o r t i n g h a r d w a r e enables real time data c a p t u r e of tactile m a t r i c e s using capacitance based, positioning p o t e n t i o m e t e r s giving the d i s tance b e t w e e n the 2 arrays and p r e s s u r e s e n s o r s for measuring the gripping force. A c o n t r o l l e d gripping force is a major priority f o r avoiding damage or slippage of the object, by grasping it either too s t r o n g l y or too gently, respectively. The data acquisition s y s t e m is built around a 15 bit m i c r o p r o c e s s o r ( 6 8 0 0 0 ) - b a s e d b o a r d . It is c o n n e c t e d t o a parallel I / O peripheral b o a r d via a high s p e e d bus (VME) and enables the c o n t r o l of 84 a d d r e s s a b l e I / O lines. The s t r u c t u r e o f the data acquisition s y s t e m is s h o w n in figure 2.

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c ) The Human Interface As well as providing u s e r - f r i e n d l y input and o u t p u t facilities for the operator, two key features o f the human interface are: * to configure the data acquisition s y s t e m * to allow objects in the domain o f the application to be defined

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It has been pointed o u t above t h a t the s y s t e m is characterized by a high flexibility which allows the user to make use o f gripper matrices with variable pin density up to a maximum of 512 pins, w i t h o u t modification to the hardware. The human interface allows the user to configure a feasible hardware s y s t e m . A m i c r o c o m p u t e r ( ATARI ST )is c o n n e c t e d to the data acquisition s y s t e m via a serial link ( RS 232 ). From this m i c r o c o m p u t e r the user can c o n t r o l the whole s y s t e m under a graphical e n v i r o n m e n t giving him a friendly interface with the machine.

14. Guiheux et aL / A Model Based Recognition System for Tactile Data

Figure

3. T h e

Human

427

Interface

F o l l o w i n g i n i t i a l i s a t i o n , t h e c a p t u r e d d a t a is t h e n t r a n s f e r e d f r o m t h e d a t a a c q u i s i t i o n s y s t e m to the microcomputer memory, allowing a graphical d i s p l a y a n d t h e r e c o g n i t i o n p r o c e s s to s t a r t . R e c o g n i t i o n c a n b e a c h i e v e d by r e f e r e n c e to a lib r a r y o f o b j e c t m o d e l s c r e a t e d by t h e u s e r f r o m a g r a p h i c s e d i t o r . T h i s will b e d e s c r i b e d in g r e a t e r d e t a i l in t h e f o l l o w i n g s e c t i o n . III T H E R E C O G N I T I O N S Y S T E M The dual array characterising the particular gripper enables many different ways of describing t h e o b j e c t . It m a y be n o t i c e d t h a t t h e 3 d i m e n sional d a t a f r o m t h e s e n s o r s resembles very c l o s e l y a 2 d i m e n s i o n a l v i s u a l i m a g e , in w h i c h the position of the sensor corresponds to the g r e y level o f a n i n d i v i d u a l pixel. T h u s t h e d a t a s e t c a n b e r e f e r r e d to a s a n i m a g e a n d as a c o n s e q u e n c e o f t h i s a n a l o g y , be p r o c e s s e d u s i n g vision techniques. However, tactile data interpretation using this approach must be done carefully in view o f t h a t t h e l o w r e s o l u t i o n o f t h e t a c tile d a t a , c o m p a r e d w i t h t h a t n o r m a l l y a s s o ciated with a visual image. The combination of t h e t w o a r r a y s a l s o a l l o w s t h e o b j e c t to be c u t into vertical or horizontal slices, and displayed as a s e t o f t w o d i m e n s i o n a l v i e w s . T h e s e c o n s i d e r a t i o n s give a g r e a t e r f l e x i b i l i t y in t h e c h o i c e o f p o s s i b l e a t t r i b u t e s . H o w e v e r w e will s e e t h a t the selection of the attributes depends on the t y p e o f o b j e c t s u s e d in t h e a p p l i c a t i o n .

". . . . . . .

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Figure 4. G r a s p e d

objects of different sizes

W e will c o n s i d e r t h a t t h e d a t a s e t is n o t d e n s e enough to achieve recognition by only applying a t r a d i t i o n a l s t a t i s t i c a l a p p r o a c h . W e will s e e later that attributes such as compacity, volume, contours and moments, which are quite reliable f o r o b t a i n i n g a p r o p e r i n t e r p r e t a t i o n a r e realised as independent knowledge sources forming part of a multiple expert system approach to the problem of object classification. Some potential attributes of objects are described below.

a ) Compactness

D e s c r i p t i o n o f Slices

Compactness is c e r t a i n l y o n e o f t h e s i m p l e s t m o r p h o l o g i c a l a t t r i b u t e s a n d is q u i t e e a s y t o compute. It is d e f i n e d b y c = 4 H ~ s / p 2, w i t h s b e i n g t h e cross sectional area of the slice and p the perimeter of the slice. By a p p l y i n g S t o k e ' s t h e o r e m [1] to a s t a n d a r d line i n t e g r a l t h e c a l c u l a t i o n o f t h e a r e a o f a n irregular shape can be made. n-I

O n e o f t h e m o s t d i f f i c u l t p r o b l e m s in t h e d e s i g n o f a p a t t e r n r e c o g n i t i o n s y s t e m r e l a t e s to t h e selection of a set of appropriate numerical attrib u t e s o r f e a t u r e s to b e e x t r a c t e d from the object of interest for purposes of classification. T h e s u c c e s s o f a n y p r a c t i c a l s y s t e m d e pends critically upon this decision, although t h e r e is l i t t l e in t h e w a y o f a g e n e r a l t h e o r y to guide the selection of features for any arbitrary p r o b l e m [2]. T h e i n t e r p r e t a t i o n o f s p e c i f i c a t t r i b u t e s c a n be r e l e v a n t f o r c e r t a i n o b j e c t s a n d n o t for others. This means that attribute measurem e n t s will d e p e n d o n t h e n a t u r e o f t h e p r o b l e m a n d m a y n o t be a d a p t e d to a d i f f e r e n t a p p l i c a tion easily.

Area=l/2

I~. k=O

(x.y k

k+l

Xk+l

Yk) l

T h e a t t r i b u t e c, v a r y i n g f r o m t h e v a l u e 1 f o r a circle a n d 0 f o r a line, g i v e s i n f o r m a t i o n a b o u t the shape of circular regions between the two e x t r e m i t i e s , a s s h o w n o n f i g u r e 5, b u t m a y be usefully applied to regions of irregular shape.

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(b)

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F i g u r e S. F o u r s h a p e p r e s e n t i n g f r o m I (a) t o 0 (b)

a compactness

H. Guiheux et al. / A Model Based Recognition System for Tactile Date

428

b ) C o n t o u r Description o f Slices The c o n t o u r C o f a region R gives morphological f e a t u r e s of the p r o f i l e of a slice. It can be r e p r e s e n t e d using a p o l a r c o o r d i n a t e s y s t e m with the c e n t e r o f gravity o f the c o n t o u r as the origin [2][3] . The graph indicates the variation of t h e module o f the radius p with changing direction ~ with r e s p e c t to a r e f e r e n c e axis.

c o n t o u r o f the tactile s e n s i n g array is t e s t e d against the s t o r e d model, which p r o d u c e s a s e t of s i m u l a t e d s e n s o r values for a number of given model rotation. The i n t e r c o r r e l a t i o n function is used as a m e a s u r e o f a l i g n m e n t b e t w e e n the model and t h e real object.

RIO~] _ ~ _ ~

observed

contour l u notion

O

F i g u r e 8.

F i g u r e 6.

Contour

Intercorrelation

Function

Further r e s e a r c h is needed to find an optimal s o l u t i o n t o the p r o b l e m of r o t a t i n g the model on the aligned p o s i t i o n . The use of Fourier analysis is being c o n s i d e r e d in this c o n t e x t .

o f a Slice

This f u n c t i o n is invariant by t r a n s l a t i o n b u t very sensitive to rotation. By comparing the unknown polar signature to a r e f e r e n c e signature ~ , the maximum c o r r e l a t i o n may be f o u n d . This very helpful f e a t u r e will d e t e r m i n e the r o t a t i o n of the object also. We can see on t h e graphs t h a t if a c o r r e l a t i o n e x i s t s b e t w e e n 0othe model and p the sample, it is d e p h a s e d by O .

c ) Volume Calculation The volume of an object is an interesting feature because of its simplicity. The calculations are not very accurate, but they can be used to m a k e a good distinction between t w o objects of very different sizes. W e use four sensors on each

array t o d e t e r m i n e t h e e l e m e n t a r y volume. If we c o n s i d e r t h e s e n s o r s 11, 12, 21, 22 as s h o w n in the figure b e l o w for t h e f i r s t array and the same f o r t h e s e c o n d array, t h e n an elementary volume o f the o b j e c t can be defined.

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12

21

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22

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between

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sample

and

The i n t e r c o r r e l a t i o n is defined by :

R[e]=ECpo(e).~l(o-e) The model is w h o s e contour direction, in the ing a collected

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description of t h e amnmlng m a t r i x

the m o d e l

]

stored as a geometric model, C m a y be reconstituted in any simulation mode. W h e n compardata set with the model, the

It should be n o t i c e d t h a t t h e e l e m e n t a r y volume can be divided into t h r e e d i f f e r e n t p a r t s : the main f r a m e d e f i n e d by t h e minimum value of the e x t r e m i t i e s o f each array, and the two volumes remaining a t each extremity. The main frame is a simple p a r a l l e l o g r a m and the t w o e x t r e m i t i e s are polyhedra. The polyhedra can be calculated because we know the p o s i t i o n of the f o u r s e n s o r s in the space. Considering the minimum value o f the s e n s o r s as a r e f e r e n c e

H. Guiheux et al. I A Model Based Recognition System for Tactile Data

p o i n t P0 and the o t h e r t h r e e s e n s o r s respectively as Pl, P2 and P3, (see figure below) the calculation of this volume is achieved in two s t e p s .

t

.~-C)

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,

i

,,i,,1t,5,

image t r a n s l a t i o n and rotation. Extending the application o f t w o dimensional m o m e n t invariants to t h r e e dimensional m o m e n t invariants allows the object m e a s u r e m e n t s to be i n d e p e n d e n t of size, o r i e n t a t i o n and p o s i t i o n [12].

/ I~i

Main

<-><-

429

Frame

i

In the space (x,y,z), t h e t h r e e dimensional c e n tral m o m e n t gpqr o f o r d e r p+q+r o f a density p( x, y, z) are defined in t e r m s of the riemann integral as :

><->

" q (z-~)r p(x,y,z) dx dy dz [Lpqr = J" 7Jo,..Jr ,..(X_p (Y-Y) Figure 10.

Model

o f em e l e m e n t m ' y

volume

First the p o l y h e d r o n (Po,Pt . . . . . P6) is divided into two g e o m e t r i c a l o b j e c t s At( Po, P1, P2 P*,P6) and A2( Po, Px,Pa, Ps,PD. Po being the l o w e s t value o f the four s e n s o r s , PoP2 is defined as the diagonal of the t e t r a h e d r a ( Po, Pt,P2,Pa ).

P5

P3

,/Y, Pq •

Figure t1.

Polyhodra

Using f u n c t i o n s of c e n t r a l m o m e n t s on the basis of ternary q u a n t i c s , it has b e e n s h o w n [12] t h a t o b j e c t m e a s u r e m e n t s were i n d e p e n d e n t of orientation. E x p e r i m e n t s with d i f f e r e n t o b j e c t s as a solid rectangle, pyramid or cylinder have c o n firmed the invariancy o f m o m e n t invariants.

f ) Normal

Vectora

Each face of an For example, in object s h o w n is vectors. This is the g e o m e t r i c a l

"Pl

object by each normal vector. figure 13 we observe t h a t the c h a r a c t e r i s e d by three normal a p o w e r f u l way o f c l a s s i f y i n g objects.

Model

The volume o f t h e t w o o b j e c t s is t h e n calculated as follows:

F'0

/-/

a

~

P2 Figure 13.

Obler'vatlon o f n o r m e l l on

•

vl~tore

fa~

la! Figure 12.

Solid Trliuagle

The volume o f the t w o solid triangles is:

V

f P1 +

6

P3 +

6

P2

3

Considering t h a t the array (i,j) is a t w o d i m e n sional image defined in a (x,y) plane and o f f e r i n g d i f f e r e n t grey levels f(i,j) 'for each pin, it is p o s s i b l e to define two s l o p e values for each pin.

l a2

!

gx(i,j) The p r o c e d u r e is c o n t i n u e d on for each e l e m e n tary volume t o obtain the final volume o f the g r a s p e d p a r t o f the object. e ) Moment

Invm'iantJ

Hu [11] derived a s e t o f m o m e n t f u n c t i o n s which have the desired p r o p e r t y of invariance under

df(i,j) = - dx

df(i,j) gy(i,j ) = - dx

f( i÷l, j) - f (i, j ) dx f(i,j÷l)dy

f(i,j

)

H. Guiheux et al. / A Model Based Recognition System for Tacti/e Data

430

gv

gx

Figure

14 .

Obsqvrvatlon

of

vector

farallles

This m e t h o d e x p o s e s family groups o f v e c t o r s , r e p r e s e n t i n g single faces, which may be e f f e c tively replaced by a single vector r e p r e s e n t a t i v e of the w h o l e group. It can be o b s e r v e d o b s e r v e d t h a t t h e r e are t h r e e n o r m a l s for the object s h o w n in figure 12.

IV CLASSIFICATION A N D B L A C K B O A R D ARCHITECIZII~

a) Cluslflcatlon by clustering The p r o b l e m at this s t a g e is to find a p r o c e dure to classify an object s a m p l e in accordance with a s e t o f p r e d e f i n e d object classes. The approach c h o s e n is to m a t c h several d i s t i n c t f e a t u r e types in i n d e p e n d e n t s o f t w a r e m o d u l e s eg c o m p a c t n e s s - v o l u m e , m o m e n t invariants, c o n t o u r s and n o r m a l s etc... This approach is chosen because of its flexibility and because it allows the s e t o f m o d u l e s to g r o w and change. The r e s u l t s o f the i n d e p e n d e n t classification can be c o m p a r e d w i t h an agreed solution. In o r d e r t o evaluate the r e s u l t s , f e a t u r e s are plotted in a m u l t i - d i m e n s i o n a l f e a t u r e space ( one f o r each module ). For example, f i g u r e 1S i l l u s t r a t e s a d i s t r i b u t i o n o f object s a m p l e s in a two dimensional f e a t u r e space. F2

FI figure 1S.

Three

object classes

defined b y

in the s p a c e

the features

FI a n d

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By definition, object s a m p l e s o f a particular class are c l u s t e r e d in a particular region of this space. The m e t h o d is b a s e d on the fact t h a t the distances b e t w e e n the points in the p a t t e r n space are the m e a s u r e s o f similarity b e t w e e n the actual p a t t e r n s and can be s u m m a r i z e d as c l a s sifying a point as a m e m b e r of the class to which its nearest neighbour belongs. This approach has b e e n analysed in [6] where it is shown t h a t t he n e a r e s t neighbour rule is a suboptimal procedure; t h a t is, will usually cause an error rate g r e a t e r than the minimum possible, the Bayes rate. But it has been shown t h a t if the number of s a m p l e s is very high the error rate will not be w o r s e than twice the Bayes rate. When the c l a s s e s are equally likely, classification is made by the n e a r e s t neighbour rule and not the Bayes decision b u t the probability of error is approximately ( l - l / c ) for b o t h with c being the number of c l a s s e s [6]. An e r r o r bound c a n n o t be d e t e r m i n e d in the case of a limited number of samples. The choice of nearest neighbour rule for this problem, is due to its ease of i m p l e m e n t a t i o n compared with the parametric m e t h o d s where one has to deal with probability density f u n c t i o n s e s t i m a t e d parameters, etc... Also a small number of samples makes parametric m e t h o d s quite unreliable and the n e a r e s t neighbour rule is preferable in large number of applications. b ) Blackboard Architecture Having a s e t of i n d e p e n d e n t modules which contain specific k n o w l e d g e it is logical to make use of a blackboard architecture t h a t can incorporate and e x t e n d the traditional statistical m e t h o d of making decision. The blackboard shares a data s t r u c t u r e to which all the modules (called knowledge sources) have access . The blackboard also s c h e d u l e s the knowledge dynamically. H y p o t h e s e s and decisions created by the Knowledge Source are r e c o r d e d on the blackboard for an explanatory purpose. Knowledge Sources are written separately to make the p r o b l e m solving easier. F u r t h e r m o r e the Knowledge s o u r c e s can run in parallel to increase the processing speed. A knowledge source called C r o s s Checker tries to find a unique s o l u t i o n by evaluating the s e t s of candidates p r o p o s e d by the individual k n o w ledge s o u r c e s using the m u l t i - d i m e n s i o n a l feature space p r o p e r t i e s described above.

H. Guiheux et aL / A Mode/BasedRecognitionSystem for Tactile Data

431

References

[1] Rene Stolk and George E t t e r s h a n k , C a l c u l a t ing the Area o f an irr egular Shape, Byte Magazine, Feb. 1987 [2] J.G. Postaire, De l'image a la decision, Ed. Dunod , 1987

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Figure 16. A Blackboard a r c h i t e c t u r e As a supervisor the c r o s s checker evaluates the d i f f e r e n t s o l u t i o n s and will ask the blackboard to activate the four modules again with a new t h r e s h o l d until a unique solution is found.

V CONCLUSION This p a p e r has d i s c u s s e d how to s e l e c t p o t e n t i a l a t t r i b u t e s from tactile d a t a and a m e t h o d f o r recognizing the t h r e e dimensional p a t t e r n o f o b j e c t s by grasping t h e m by tactile m a t r i c e s has been p r e s e n t e d . M o m e n t invariants, c o n t o u r s , invariants and n o r m a l s have been d i s c u s s e d as p o t e n t i a l l y f e a t u r e s o f o b j e c t s in this c o n t e x t . Much of the p o w e r of m e t h o d s p r o p o s e d f o r recognizing o b j e c t s f r o m tactile data described above are derived f r o m analogus image p r o c e s s ing activities. The b l a c k b o a r d a r c h i t e c t u r e has been p r o p o s e d as a m o s t effective m e t h o d o f organising large k n o w l e d g e b a s e s which i n c o r p o r a t e s a n u m b e r o f d i s c r e t e k n o w l e d g e domains.

Acknowledgement The a u t h o r s wish t o a c k n o w l e d g e the e f f o r t s of F. C o n t r e i r a s in the p r e p a r a t i o n o f this paper.

[3] C. Laurgeau and M Parent, Les machines de vision p r o d u c t i q u e , E.T.A. , S t r a s b o u r g p271, 1985 [4] M. Hu, Visual p a t t e r n recognition by m o m e n t invariants I.R.E. T r a n s a c t i o n Inform. Theory vol I.T. pp 179-187 Feb. 1962 ES] P. Hall Three dimensional m o m e n t invariants, IEEE T r a n s a c t i o n s on P a t t e r n Analysis and Machine Intelligence, Vol. PAMI 2. No2 pp127-136 , March 1980 [6] A.Vahit Sahiner, Character recognition s y s t e m Msc T h e s i s , Middle East Technical University, Turkey , 1982 [7] Pattern classification and Interscience , 1973

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