PRUEBAS Y RESULTADOS
3.1 IDENTIFICACIÓN DE PARÁMETROS.
3.1.2 CONFIGURACIÓN CON DOS CONTROLADORES
L aboratory investigation for a m ine design and planning involves a n um ber of indoor tests of rock samples and rock models. A large num ber of m easurem ents over th e o b je c t’s surface under lim ited space and tim e conditions is required to analyse th e deform ation behaviour of rock sam ples /m odels. Equivalent m aterial m ine m odelling (Figure 4.2) is a typical exam ple to dem o nstrate th e lab oratory testin g environm ent of a rock m echanics investigation. M ining engineering design and analysis involves a num ber of such tests of rock and m etal sam ples /m odels. T he stress-strain relationship and the n atu re of failure of these sam ples /m o dels helps in estim ating the behaviour of in situ m ining stru ctures. T he extensive deform ation m easurem ents involved during such tests are difficult by conventional m ethods due to tim e and space lim itations of th e experim ent.
Figure 4.2: D eform ation around a m ining excavation (physical m odel). T h e im age d a ta of different stages, taken w ith a fixed cam era position
C hapter 4 CCD based vision for m ining
and orientation, were processed [Singh, 1993] analytically using projective correspondence between the plane of the mine model and th at of the photographs using the following relations:
^ aTi^T + < 2 y f + < 3 , .
^ + 1
where { X j ^ , Y p ) and are the plane co-ordinates of measured points in object and image space respectively, while Z is constant. The projectivity param eters ( a i i ,a i2....û32) are computed at every moment ( “m ” ) of evaluation through the co-ordinates of control points fixed on the rigid frame around the model. The calculated plane tensile strains of different measuring horizons at three different stages of excavation in a mine model are shown in Figure 4.3.
This measurement was done using a film based camera (Zeiss UMKIO) and stereocomparator where image co-ordinates were measured with 0.01 mm accuracy. The photogrammetric technique suited this measurement extremely well because the whole area was measured simultaneously^ and the photographs provided a dense amount of quantitative and qualitative information. However, due to the use of a film based camera, the measurement remained discontinuous in tim e and the results of the measurements did not come to hand immediately. Film processing and the manual measurement of the target points took a considerable amount of tim e which sometimes does not m atch the speed of the experiment which is required.
Commercially available formats of a CCD chip can easily cover such small objects during laboratory tests and so the film processing tim e can be eliminated. Further high contrast signalised targets can be placed on the surface of samples / models as measuring points whose image co-ordinates can be measured autom atically in nearly real time. Considering this strong possibility of CCD based autom ation of deformation studies of a mine model, a test measurement was done by digitising the hard copy images of different stages of workings in the model. The
^No p o ssib ility o f differential m ovem en t b etw een any tw o m easu rin g p o in ts due to h igh ly u n sta b le n ature o f th e stra ta around th e excavation.
Chapter 4 CCD based vision for mining 150 125 100
I
s 50 25 O a L o n g w a l l i n g o f t o p s e c t i o n S c a l e ; . 5 0 m m /m \ \ \ \ \ ' I \ \ \ \ I I ' \ \ \ \ \ \ V \ \ \ \ \ I I I » / / / / II
/ / / X I / / / / / , / / / / / \ \ \ \ \ I I I / / / / / \ \ \ \ \ \ \ \ \ X \ \ / / / / / / / / / / 5 m 9 0 m E x c a v a t e d a r e a 7.5 m _T 5 0 m m /m S u b le v e l c a v in g se c tio n S cale: — \ \ \ \ \ ' ' ' ' / / ' / / / / / / / / z \ \ \ \ \ ' ' ' ' ' ' / / / / / / / y / / \ \ \ \ \ ' ' ' I / z / / / / / / / / / ' I / / / / / / / '\ \ \ W
9 0 m E x ca v a ted area 7 .5 m 1 50 I 25 lOO 75 e .s 50 g 25 a O In te g ra l c a v in g s e c tio n S c a le :---lOO m m /m \ \ \ \ \ \ \ \ ' ' ' ' l ' " ' / / / / / / \ \ \ \ \ \ \ \ \ ' ' l ' ' ' ' / / / / / / ' / / / / / / yAW'
\ \ \ \ \ \M' B ro k e n s tra ta" f t
yZ 1 6 0 m E x c a v a te d a re aF igure 4.3: P lane tensile strain at different stages of working.
Chapter 4 CCD based vision for mining
labels placed on th e model for scaling and region differentiation were used as targ ets to estim ate th e perform ance of te m p late m atching^ in th e absence of prefixed su itab le targets. Due to bad quality of th e targ ets th e m easurem ent accuracy over these images was rath e r poor and it deterio rated for larger excavations in th e m odel (Table 4.1). T he use of a sim ulated tem p late of sim ilar grey values w ith a sym m etrical n atu re also did not solve the problem due to th e varying surface te x tu re of different labels (Figure 4.4). This problem becam e m ore pronounced due to planar rotation of rectangular labels around high m ovem ent zones. T exture
from p.
Figure 4.4: A sam ple of sim ulated and ex tracted tem plates.
around th e m easuring point is th e m ost im p o rtan t factor for th e accuracy of least squares based te m p late m atching. Sim ilar te x tu re around m easured points can be achieved in laboratory conditions by placing sym m etrical targ ets over th e object. T h e differential m ovem ent of the m odel surface upon which targ ets are fixed ro tates the targ ets and so, if these targets are non-circular, this ro tatio n effects th e m easuring accuracy of tem p late m atching which is clear from Table 4.1.
Table ,1: S tatistics of deviations during rep eatab ility test.
Excavation N um ber M inim um M ean M axim um r.m .s. Std. dev. condition of points pixels pixels pixels pixels pixels
Top section 72 0.0 0.315 1.273 0.452 0.551
Sublevel caving 72 0.0 0.384 2.024 0.569 0.686
Integral caving 72 0.0 0.465 2.351 0.649 0.799
^Discussion about different techniques to locate the centroid o f a target and the algorithm of least squares based tem plate m atching is presented in Chapter 5.
C hapter 4 CCD based vision for mining
4.2
G uidance o f a coaling m achine
A CCD based vision system is used for guidance of underground m ining vehicles like Load-H aul-D um p (LHD). T he m otion and m ovem ent of a LHD could be guided rem otely [St-A m ant et ah, 1991] by placing a CCD as an optical line d etecto r over th e m achine and a retro-reflector on th e roof to define an optical line along th e desired p ath . A system based on grey level inform ation of th e n atu ra l o bject may, relatively, be m ore suitable for coal m ining where black coal is sandw iched betw een relatively brighter sandstone strata. Figure 4.5 shows ju st reverse of th e situ atio n , where brighter sandstone has intruded into a coal seam. Taking advantage of the n a tu ra l presence of sharp contrast features, th e boom of a coaling m achine can be guided precisely and rem otely during its operation in dangerous areas. O nly a suitable threshold (Figure 4.6A ) of th e b yte-fo rm atted digital im age m ay solve the problem .
A
100.0 200.0
D N v a l u e (O to 2 5 6 )
Figure 4.5: Coal seam w ith foreign m aterial and its histogram of grey level.
Thresholding m ay not always be th e right solution. For low co ntrast im ages, an effective algorithm is required to ex tract different features of th e ob ject w ith subpixel accuracy. For such objects, different edge enhancem ent techniques have to be applied on th eir digital images. M ost of th e feature ex tractio n algorithm s are based on th e detection of intensity changes across th e features. M arr and H ildreth (1980) defined an operator, A^G, to locate edges at th e zero crossing of a second
Chapter 4 CCD based vision for mining
order differentiation. The G aussian function (G) is w ritten as; G[x^y) = e 27Tcr2
where a is stan d ard deviation and x and y are p lanar co-ordinates.
(4.3)
Figure 4.6: T hresholding and edge detection for foreign m aterial visualisation.
T h e convolution of the im age function ( / ) w ith Laplacian (A^) of a G aussian d istrib u tio n was considered an im p o rtan t step for featu re ex tractio n before C an n y ’s approach [ Canny, 1986] form ulated the optim al op erator on th e following criteria: (i) good detection, (ii) good localisation, (iii) only one response to a single edge and (iv) good noise suppression. He considered only first derivatives of th e G aussian function and incorporated th e effect of scale of th e operator. P etro u and K ittler [Petrou & K ittler, 1991] fu rth er extended C anny’s work utilising num erical m ethods which provided im proved qualitative and q u a n tita tiv e results and th e developed filter is known as th e CPK (Canny, P etrou and K ittler) filter. An application of th e C PK filter resulted in m ore accurate location of th e intrusion band in a coal seam as shown in Figure 4.6B. T he edge inform ation obtained by C PK filter m ay be com bined w ith region filling algorithm for edge and region m erging [Newton, 1993] which m ay be m ore suitable for th e au to m atio n process.
Chapter 4 CCD based vision for mining
asm
Figure 4.7: Interpolation of m anually ex tracted edges from a digital image.
4.3
B lock size estim a tio n
Using a linear CCD based scanner, th e hardcopy photographs of failed rocks, taken by a 35 m m cam era, were digitised (Figure 4.7A) at a high resolution (600 D PI) in H IPS form at. T he grey scale byte-fo rm atted digital im ages were processed in th e UNIX environm ent of a Sun w orkstation. To visualise th e perform ance of a u to m atic feature detectors, th e surface boundary of rock blocks (> 5 cm ) in th e digital im age were digitised m anually (Figure 4.7B) and were converted into a binary im age (Figure 4.7C) to separate th e block size inform ation. This m anual tracing of block size from digital d a ta is fu rth er processed by a H IPS su broutine to o btain a floating point im age indicating th e distance of any p articu la r pixel from th e nearest edge (Figure 4.7D). This interpolation is done to com pare th e perform ance
Chapter 4 CCD based vision for mining
of th e au to m atic edge detector. T he C P K filter was applied on th e original digital im age for au to m atic ex traction of th e edges which picked up inform ation about discontinuities (Figure 4.8A, only top left portion of Figure 4.7A) of th e ob ject space m uch m ore th a n th e requirem ent in real practice. E x tractio n of all local m axim a from th e im age gave a spurious result, and so this edge m ap was thresholded to a su itable grey value (Figures 4.8B & 4.8C) in accordance w ith th e requirem ent.
. T h r e s h o l d e d a t 6 4 C T e ÿ 4 e v e l
X %
C % ^ Æ m è s h p l d e d a t 3 2 e r l a y i n g o f m a n u a l & Ç J a u t o m a t i c e d g e d a t a y
F igure 4.8: A utom atic edge m apping and its com parison w ith m anual d ata. F eature ex tractio n is a basic problem for im age und erstan ding because digital im ages contain com plicated variations of grey values in and around a single object. At th e m om ent, this problem can only be handled w ith pre-acquired inform ation
Chapter 4 CCD based vision for mining
ab o u t th e object. T he auto m atically obtained inform ation of edge stren g th needs knowledge based editing [Newton, 1993] [Giilch et ah, 1990] for segm entation and utilisatio n in real practice. Figure 4.7C was overlaid on F igure 4.8B to visualise th e deviation between m anual and au to m atic edge extraction (Figure 4.8D). T h e best achievable accuracy by m anual edge m apping on a digital im age cannot go beyond one pixel while th e CPK filter m aps edges up to subpixel accuracy. F u rtherm o re, it is very difficult to control th e m ovem ent of th e mouse of th e w orkstation along th e edges w ith one pixel accuracy during m anual edge tracing from digital images. T h e autom atically ex tracted edge d a ta were m atched w ith th e processed d a ta of m anual edge m apping (Figure 4.7D) to obtain th e q u an titativ e inform ation ab o u t th e deviation between th e two types of result. A to tal of 1539 edge points were used for this m atching and th e deviations between th e two types of d a ta were com puted up to two pixels distance (Table 4.2). Using th e scale of th e
1 5 . 0 r g
I
B 10.0 5 . 0 0.0 0 . 1 0 . 2 5 0 . 5 1 . 0 1 . 5 2 . 0 D i a m e t e r o f a n e q u i v a l e n t c i r c l e ( m )Figure 4.9: H istogram of block size distrib ution (exposed blocks).
photography, th e estim ation block size of failed rocks at a m ine site was assessed (F igure 4.9) by com puting the num ber of blocks (equivalent d iam eter > 5 cm ) and th e ir surface dim ensions from th e outlines of the edge m ap. This analysis of block
Chapter 4 CCD based vision for mining
Table 4.2: M atching of m anually and au to m atically d e te c ted edgels D istance (pixels) N um ber of pixels 0.0 1203 0.4 64 1.0 10 1.4 10 1.8 10
size distribu tio n helps in estim atin g th e bulking factor of th e caved m aterial inside a goaf^ which ultim ately is used to design support density around th e m ining face.