Technologies of robotic and artificial vision systems

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• Review Article TECHNOLOGIES OF ROBOTIC AND ARTIFICIAL VISION SYSTEMS PHILIPPE VILLERS President, Automatix, Inc ., Billerica, MA, U .S .A . The state of robotics technology is reviewed, emphasizing those technologies that can significantly increase manufacturing productivity . Special attention is given to the role of vision systems in expanding the use or robots in a variety of applications . INTRODUCTION The purpose of this paper is to present an overview of the growing and exciting field of industrial robotics, with an emphasis on the intelligent system as opposed to the so-called `dumb robot' which cannot adapt to its environment . If a robot is thought of as being an artificial arm, the robot controller is the artificial brain . Artificial vision fits in as an input to the artificial brain in a manner comparable to eye sight in the human brain . This creates as much differentiation in the behaviour of the system as in the function of sighted individuals versus unsighted individuals . Industrial robots as they exist are really artificial arms which can manipulate tools or manipulate parts . Regardless of their shape or form, robots seem to group themselves into the four basic forms of mechanisms that are shown in Fig . 1 : the Cartesian, from a control point of view the simplest ; the cylindrical, which has some mechanization advantages ; the polar, and then an increasingly large set of anthropomorphic, of which only one form is shown here . Most anthropomorphic robots can be thought of as having the equivalent of a human waist, human shoulder, human elbow and human wrist. Irrespective of the form, they all perform the function of being a substitute for the human arm, often with a longer reach and often with a larger payload . They are tireless and capable of dealing with considerably more hostile environments than human beings . But they must rely on the microprocessor as a feeble but effective substitute for the human brain .

The most modern systems have artificial vision, providing a prime characteristic of an intelligent system by adapting to the environment . Figure 2 is a summary view of some of the more common industrial applications of robotics. In the early days, dumb robots with considerable muscle power were the dominant forms of robotics and represented the low technology end of the spectrum . 'Today, most units are in the middle section-spot welding being number one, and spray painting being among the leaders, if not number two . But the real excitement in the field is the high growth, in the hundreds of percents per year, of arc welding, now reaching several hundred units in the United States and several thousand in Japan . Assembly is still in its infancy in the United States and its childhood in Japan . Also included in this category is inspection which is lumped into robotic application . However, the last inclusion is debatable since artificial vision turns out to be useful for stand-alone automated visual inspection for which it is used in probably at least 8004, of current applications . It will increasingly be used for robot guidance . Automated visual inspection, strictly speaking, cannot be thought of as robotics, but it is often grouped in with it because of its dual mode . Figure 3 shows the trend in productivity increases and absolute productivity level of major industrial countries . Although the United States has historically had a very high absolute productivity, the cross-over points are hard upon the U .S . Figure 4 is an important curve which has been

Presented at The Ralph Cross Lecture Series . 14 April 1983 . Massachusetts Institute of Technology Cambridge, Massachusetts, U.S .A .

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Robotics & Computei-Integiated Manufacturing • Volume 1, Number 2, 1984

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plotted in ten different forms . It is a reminder that across many industries the unit cost of production goes down typically by two orders of magnitude or more, as the quantity produced per year goes from the tens to the million level . This is very important because classical automation has had very little impact in batch production, approximately a thousand to ten thousand pieces a year, And yet robotics offers that possibility . Already a few companies exist which are using robots in special situations cost-effectively, in part production levels of less titan ten parts per year (although this is obviously very much the exception) . As to the size of the robotics industry in the United States, both in terms of number of units installed and dollar volume, the curve shown in Fig . 5 gives a good feeling for it, This is an industry which has just passed the $200,000,000 level a year and should reach the $2 billion level in 1990 . End of 1982 data on the world (Fig . 6) shows that Japan, with half the industrial base of the United States, has double the number of robots . In the last few years the gap does not seem to be narrowing and in the most advanced areas, which are most crucial, it clearly is not narrowing . This is a matter which should be, and is, of great concern . JAPANESE ROBOTS Figure 7 was taken at the Yaskawa factory and shows the final test area for their arc welding robots .

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• PHILIPPE VILLERS

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It was not a posed picture since, at that time, they were already producing over 30 a month and since then, Hitachi and a few others arc producing in the 60 to 70 a month region, having peaked at some 80 per month-just arc welding . Figure 8 is a recent picture of the Hirata factory and it shows a few of the newest and simplest of the assembly robots . These are the so-called SCARA robots which are being built in thousands in Japan at the present time . Next major areas of robotic and artificial vision systems will be systematically discussed, starting with artificial vision . Figure 9 shows the famous Consight application ; famous because it has been widely written about and G .M . spent many years developing it in the laboratory . The idea is to sort raw castings using robots and vision in a very hostile environment-a foundry . The vision camera is overhead and is not readily visible in this photograph, but it allows the robot to identify the casting, determine its position and orientation, so that a robot can pick it up, and put it in a bin . For practical industrial situations, one should differentiate between two cases-semi-ordered bin picking and random order bin picking . In semiordered bin picking an often trivial amount of money is spent to place the parts in the bin in a semi-ordered manner such as the Ford/Germany example in Fig . 10 . The semi-ordering becomes clearer in Fig . 11 where it can be seen that the

Assembly-Hirata factory .

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Fig, 9 . Conxight foundry application .

w Fig . 10 . Bin picking at Ford .

transmission castings are placed in an approximately fixed orientation in layers . Semi-ordering makes the vision problem easy . So, in fact, it is now used in production at Ford in Germany where the camera determines the exact position and orientation of the casting, and the robot just picks it up . There are no

tangled parts because they are in layers . That is a good solution to the problem and an example of the useful simplifications done in industry . As to the rest of the bin picking problem, I divide it into industrial and nonindustrial aspects . In industrial situations my recommendation is to hire a man

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Fig. 11 . Bin picking-close-up,

Fig . 12 . Autovision 4 .

to dump the contents of the bin on a conveyor belt and then proceed . For academic purposes (the University of Rhode Island has already spent five years on it), I recommend a careful and exhaustive study because it's an absolutely fascinating problem . This leads to the next topic of commercial artificial

vision systems . Those developed at Automatix will he used as examples . Figure 12 is an Autovision 4 system which is built into a special industrial cabinet to work in a harsh environment . It is actually a sealed cavity in which the electronics operate for both RFI and contamina-

Rulx,no, & Computer-Integruled Manufacturing

tion reasons . It uses a small heat exchanger-there is a fan bringing in air in a 'chimney' along the back wall, so that the interior cavity has no exterior air circulation whatsoever . This device supports up to 16 cameras . Its capability includes not only black and white binary vision, but gray tone capability with up to 64 shades of gray . It is a classic example of desirable cooperation between academic research and industrial use . The algorithms on which this is based were developed under National Science Foundation sponsorship at the Stanford Research Institute in California and were found to be an outstanding foundation to build on . The structured lighting that was used was developed in several places but particularly at the National Bureau of Standards . This is a case of going out of the research environment into industry in a period of less than five years, which is far better than average . Figure 13 shows some of the interesting range of more than 100 applications of this artificial vision system currently in use in industry . This figure shows the rapid growth of the field . Some 30 U .S . firms are involved in commercial vision systems, although we believe that at this point we may have the largest number of vision systems of any of the commerical firms . The champagne bottle is shown because French law requires that there be at least 750 centiliters of champagne in the bottle, even after the impurities have been removed . It is automatically verified that there is at least 750 centiliters and, for good meas-

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ore, that the label is correctly positioned . There is a miscellany of parts including keyboards, gears, complex parts and speedometers where calibration is verified . This, then, gives some idea of the range of applications . Not shown here, because it is not yet in production, is a proposal in Israel for using the system for sorting fruit and a number of other food industry applications of that general type . Figure 14 is a block diagram of the system . There are multiple cameras-up to 16-as inputs . They are normally standard commercial solid state cameras . Solid state cameras are drift free . They have multiplexing and A/D conversion to take the gray tones and code them into six bits, giving us the 64 possible gray tones . Now there is a vision preprocessor, as it is called . Traditionally, very fast logic chips arc used because a tremendous amount of data compression is necessary to get the information in a TV frame in 1/30th of a second . A technique known as run length encoding is used . In our most recent system, the Autovision 4, this has actually been done with powerful dual microprocessors, thereby making that part of the system readily programmable, an important step forward . Our earlier systems followed the SRI approach, using hard wired logic with 4-hit bipolar microprocessor slices . After the information is crunched, it is analyzed in the main processor using a Motorola 68000-32 bit processor to extract the features which are iden-

Fig . 13 . Range of vision applications .



Technnlneiee of robotic and artificial vision systems a PHILIPPE VILLERS

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tified . Rach hounded entity in the picture (called a `blob') is convened into up to 60 features or parameters each having a numeric value . The rest of the information is thrown away . The 60 parameters are mass properties such as center of gravity, moment of inertia, major and minor diameter of an equivalent ellipse, perimeter, area, and number of holes, Typically, the best six are selected and compared to an unknown object in the field of view with all the stored objects . In terms of six selected best features, nothing more is done than to measure the distance, in what aright be called six-dimensional image space, between those of the unknown part and the previously measured parts, using a statistical technique called the chi-squared test . That tells how, close the nearest neighbor is . At set up time, it has been decided how, close is close enough for the unknown part to be declared a good part of type a,b,c or d, etc . Structured lighting is important for certain classes of application because some of the information is in the image, but some of the information can be in the reflection of a light source whose shape has been carefully controlled . Figure 15 shows that if a bar of light, that is literally a line, is reflected from a surface, then the distonion caused by the shape of the surface is a measure of height difference, giving a so-called "2 1/2 d" or 2 1/2 dimensions . The distor-

tion in the image call be measured and translated to find the height difference . To quite reasonable tolerances the height distance can be measured by parallax measurement in a conventional manner using either one or two light sources, depending on the technique used . Let us consider now some practical applications of the system . Figure 16 is taken in a Chevrolet factory, looking down on top of a three-storey stamping mill . The close-up in Fig . 17 shows a part which is the front end of a Chevrolet pickup truck. This part has about 90 holes of different sizes and shapes, stamped out one every 3 seconds . It used to be inspected once every hour . If there is tool breakage or wear, it is questionable what can he done with 1200 defective front ends . That represents a lot of money . The new method is for the Autovision to use eight cameras, each centered around a portion of these holes, to verify the correctness of each of these holes, If it is bad, it is displayed on the CRT screen . In Fig . 18, "X" shows that the hole has been correctly located and identified . If it were bad, a box would be drawn around it, and the quantitative values would be displayed to the left of the defective or missing hole . After three bad parts in a row, the press is automatically shut down . Shutting down the press is significant because it is the simplest form of process feedback . Process feedback is very, very important in terms of advanced vision applications . Figures 19 and 20 show an automobile instrument cluster for which Autovision can he used in one of two ways : (1) to make a go, no-go inspection while the instrument clusters are fed simulated inputs to check both the analog and the digital readings, and (2) in certain applications to make calibrations . An example of calibration in a Japanese watch application is as follows . A manufacturer wanted to set watch time in the factory so a little special purpose device was built to press the buttons that set the

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Fig . 16 . Truck front end inspection_

Fig . 19 . Instrument cluster inspection_

Fig . 17 . Truck front end .

Fig . 18 . CRT display of results .

time . How many times should the button he pressed? With an Autovision system monitoring and comparing the watches' display time to its real time clock, the system can serve as the process control for this special purpose machine . Figure 21 shows a rather different application . This is a final inspection of a keyboard which is mounted on a computer X/Y table . Figure 22 is a remarkable application . The part is an automobile connecting rod . Manufacturer's surveys revealed that many consumers preferred that the connecting rod which connects engine to drive shaft remain connected for the life of the car . To further this admirable goal, a technique is used which is a combination of the old and the new . The standard magnetic particle technique is used in which the part is magnetized, dusted with iron filings



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Fig . 20 . Screen display of cluster .

Fig . 21 . Keyboard inspection .

mixed with a fluorescent dye, and then washed off . Small amounts of iron remain clinging to any crack-which can he seen in ultraviolet light because the dye fluoresces . The same technique is used except that it is automated and the fluorescence is observed by the Autovision system which never gets

bored . The process is done simultaneously on two sides of this rod, illuminated by a strobe light, at the rate of ten a minute . This is a great improvement because human beings are absolutely terrible at detecting flaws in high quality parts . if you have one part in ten that is easy one out of a hundred fair, and



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Fig . 22 . Connecting rod flaw inspection .

one out of a thousand poor, and one out of ten Figure 23 is the most sophisticated of the SPC thousand then you almost cannot use human inspec(Statistical Process Control) applications . The idea is to use a bank of cameras and strobe lights, plus the tion . The Autovision does not have the same characteristic, and results have shown a significantly better Autovision system, to measure the gap at a number detection rate for flaws . of points between car door and car body, and between the car hood and car body . From this data, real time statistical analysis is performed, resulting in the displays on the Autovision CRT . The resulting trend information is fed to the upstream station on the assembly line, where the doors and hoods arc mounted . Before the process drifts out of tolerance, corrective action can be taken with the goal of "no bad automobiles" down the lint . This philosophy of statistical process control in a real time environment is clearly a future trend of great importance . ROBOT ARC WELDING AND RELATED APPLICATIONS Now I would like to look at the area of arc welding which is one of the other important areas of intelligent systems . To do so, I will start with the robot as a terminal concept . The A132 Controller shown in Fig . 24, in silhouetteform . usesthe Motorrola6R00032 hit Robots us tenninuts CAC inter lace RS 232

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Technologies of robotic and artificial vision systems • PHILIPPE VILLERS

Fig . 25 . Robot controller-product montage .

Pig . 26 . AID 900 robot arc welding .

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microprocessor, the most powerful currently available . It is designed with communications specifically in mind because it is a modern computer on the factory floor . 'I his amazing 32 hit single chip processor, which costs well under $100, is comparable in power to an IBM 360 computer . It is called 68000 because it has approximately 68,000 transistors in it . Some of the interfaces which are required in the overall area of computer-integrated manufacturing are visible in the figure . The product montage in Fig . 25 shows the A132 robot controller . It looks very similar to the Autovision, which is an important demonstration of a basic principle in product design-if there are two products for very different purposes, manufactured by the same manufacturer, they should be as similar as possible . 'They cannot fit into an identical housing because a lot of width is needed in the base for the servo amplifiers that drive the robot, so `fat man' is the A132 controller, 'thin man' is the Autovision artificial vision system . They are the sane generic design and share a good deal of commonality . There would have even more commonality if our engineers had not succumbed to the usual thought that the second time you design something, you have got to have some `better ideas' . Fieure 25 show some of the various arms it currently controls, hence the term `universal controller' . Since it is a very intelligent

controller, by putting in different software and/or changing the servo amplifiers, a variety of arms can be driven . The artificial vision system also ties into it and is, in fact, available as an integral part by using extra slots in the robot controller in order to combine vision and robot control . Figure 26 shows a better view of the largest arm, the AID 900 . This arm was added because in a good many applications we could not reach far enough with our standard arc welding arm . Figure 27 shows the German Kuka spot welding robot . A major automotive customer told Kuka that they would be happy to buy some of their spot welding robots if they were interfaced with an Automatix A132 controller . So we are interfacing the Kuka spot welder to the AT32 controller . Some of the reasons a manufacturer such as General Motors might like to standardize on one, or a few, robot controllers regardless of the arm are obvious : standardization of spare parts and case of operator training . Other, not so obvious, reasons have to do with advanced features of which an important one is the CAD/CAM interface capability which shows the growing unity in the field of CAM . The next few figures show some practical applications of robot arc welding . Figures 28 and 29 show Steel Case Furniture's welded chair frame . The payhack on robot arc welding has been phenomenal,

Fig. 27 . Kuka spot welding robot .



Technologies of robotic and artificial vision systems • PHILIPPE FILLERS

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Fig . 28 . Steelcase robot .

and the reason is that a person can see or weld, but not do both at the same time . The required welding helmet practically `destroys' a person's vision in order to save it . So arc welding productivity is very low if you measure it as `arc on' time, that is, the

time you're melting filler metal . Ten to twenty per cent productivity per R hour day is common in industrv . With a rohot and a man sharing the job you can get 70-90% and also remove the human being from the worst of the fumes . A clear step forward with a

Fig . 29- Steelcase product .



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Fig, 30, LEG welding with AID 6.01) robot

Fig . 31 . TIG welding svaIern .

typical total system cost of $100,000 the payback is often less than one year . The uniformity of the welding results are also a great deal better . This means fewer bad welds which are not so important for the chair frame as in some other arses . Another advantage is fewer secondary operations such as reduction

or elimination of grinding, because there can be much more uniform bead . The weld can be uniformly good or uniformly had . After a while it is likely to he uniformly good . Figure 30 shows our AID600 assembly robot being applied to welding applications . The applica-

Technologies of robotic and artificial vision

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Fig.

systems • PHIIJPPE VILLERS

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tion is tungsten inert gas (TIG) welding which is a precision process . 'therefore the Cartesian coordinate medium reach robot is much stiffer and has better precision . Also, it's a beautiful problem in systems design because TIG welding is a notorious RF (radio frequency) generator . Its high frequency high power A .C . can `blow the mind' of computers at remarkable distances . We've had to go to great lengths to provide RF shielding for this application . It turns out to be a nontrivial problem . Figure 31

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contains the rest of the 'I IG system which are turntables, etc ., since in welding the robot and the part `positionei on which the part is fixtured has to be coordinated while being welded in many applications . Figure 32 shows one such computer-controlled turntable we manufacture . Notice the physical separation, so that the human being loads and unloads during the time the robot welds on the other side . In Fig . 32 there is a person on the other side of the screen Figure 33 shows another view of such a sys-



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Fig. 34 . Two ruhol weld cell .

tern . This shows that, where necessary, additional degrees of freedom can be provided, all computercontrolled, from the same robot controller . Figure 34, an advanced concept called the weld cell, shows a demonstration of the principle . It is still somewhat futuristic, and is a recognition that the welding process really involves a `triad' . First, the

parts are inspected, then two or more parts are assembled in their correct geometry before you weld them . Then they are welded and the results inspected . This completes the reoccurring triad . Conventionally the first two operations are done by a human but why couldn't they be done by a robot? And in some circumstances why couldn't the robot

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Technologies of robotic and artificial vision systems • PHILIPPE VILLERS

Fig . 36 . Robot arc welding with autnvision optical seam tracker .

itself hold the part and therefore be the 'fixture', since there is no concern about the robot burning its 'fingers'? In any case, this is a demonstration of cooperative behavior between two robots, monitored by an artificial vision system, so that the parts can be picked up both before and after welding . In Fig . 35, a Weld Cell schematic shows the logical concept implemented . You start with a CAD/CAM system, you design the welded assembly and determine the weld path . Then the process information is provided and transmits the information to an intelligent (advanced) robot controller, Now there are a series of weld cells for successive assembly steps each involving the basic triad of weld, assemble, inspect . There is also a process flow from weld cell to weld cell . The beauty of this concept is that it is possible to start such a concept at the lowest level and gradually build the hierarchy over time . Note a dotted line in Fig . 35 leading to inventory and parts coming in, acknowledging that the material handling and associated control also has to he integrated into the infrastructure of computer-integrated manufacturing . Nothing looks dumber than a robot that's run out of parts . Figure 36 shows one of the most exciting fields of advanced application-the marriage of vision and robots in a very sophisticated way . The problem is that a large number of parts, roughly 30°% of all such

Fig . 37 . Laser seam illumination detail .

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Fig .3N, RVIIA seam tracker-close-up,

parts, cannot be welded blindly by robots because of part-to-part variations, involving either the original location of the seam or the part itself, or in some cases the thermal distortion during the actual welding process. Many people have studied this problem . Automatix, in fact, has implemented and tested at a customer site this seam tracker where the solid state camera observes the reflection of a slash of light, a laser beam . The reflection is analyzed to determine the position of the groove to be welded and even its width and depth . This information in turn is used to modify the behavior of the servo, using the seam tracker data as a position vernier on the preplanned path . Figure 37 is a close-up of the reflected laser light . The reflected light which otherwise would have been an arc is distorted by the existence of the groove . 'That is the information the camera uses, Figure 38 contains another close-up with the welding torch and the camera . the camera of course cannot get too close to the arc or there would not be a camera . A great deal of the art is to he able to operate the system even in the presence of the arc, a problem that has been solved due, in part, to spectral filtering and other details for which there is a pending patent . Figure 39 shows a schematic . The camera system provides the vernicr input . The teaching system uses the RAILL language . There is also the servo loop, a classical D .C . servo loop with both position and vei-

ocity feedback to which the seam tracker's optical feedback system output is added . Figure 40 shows the details of the seam tracker . Figure 41 shows a related application to arc welding . The same arc welding robot, by changing the tool, can be used in some interesting ways, in this case, plasma coating . A company uses robots for a run of one or two parts . They actually are reworking

Fig . 39 . Scam tracker-schematic diagram_



Technologies of robnIic and artificial vision systems • PI IILIPPE VILLERS

worn parts from giant paper mills which have been worn by erosive processes . They need to build up these parts and then remachine them . They build them up by plasma coating or by repair welding . This sometimes requires hours of controlled operation, because the welding should he done continuously for thermal reasons . A robot is uniquely suited for this task, particularly if you have an intelligent system, so it doesn't have to he trained to do each and every step . In fact the geometric properties of the area to

be filled and the number of layers required are described . This is one of the very first applications where robotic systems can pay for themselves working in lot quantities of one's and two's . Robot arc welding of [ot quantities of as few as Len will probably become reasonably common in the next few years as the need for doing full individual part programming is eliminated by using CAD links . Lot sizes of one or two is of course a relatively rare exception and Fig . 41 shows one of those exceptions .

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SMALL ROBOTIC ASSEMBLY AND TEST SYSTEMS The assembly area is the future leader of robotic applications . Economics is a good place to start . The parametric curve shown in Fig . 42 is the correct shape . Quantitatively, the exact limits can always be argued . It shows that hard automation shows further savings with increasing volume, which on a log curve looks like a straight line as volumes rises with more and How hree assembly methods c=ore

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Fig . 43 . TI robotic test line for caluulaturv .

Fig . 44 . Westinghouse APAS system .

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Volume l, Number 2, 1984

more specialization . On the other hand, because of design standardization, more robots can be in parallei so the curve tends to level off . Manual techniques, of course, have essentially by definition, little or no efficiency improvement as a function of volume . So the intersection area is the conceptual area where robotics has a major role to play . The reality of that conceptual area is, in fact, that robotic assembly is beginning to be used in so-called mixed production, where a single robotic line is used to handle more than one variant of a product family . Figure 43 is a mixed example . The historical distinction between assembly, testing and inspection to maintain impartiality or independence no longer has its logical place . That reason is gone . Here is a Texas Instrument application where the part, which is a calculator, is picked off the line by a servoed robot with vision input from a camera in the ceiling . The vision system determines orientation and model type . At the various test stations different test operations are performed, the most interesting of which is a special purpose robot pecking at the key while another vision unit reads the display . If the calculation is correct thee unit goes back on the line and in the last operation they put the calculator in the right box by using again a combination of a vision system and a robot . TI is one of the largest U .S . users of robots, most of which are manufactured in-house . Figure 44 shows the widely publicized Westinghouse APAS system, really a demonstration system, partly funded by the National Science Foundation . It is a demonstration program to show mixed production on a single line under program control without retooling or shutdown between models of a family of electric motors, each of a different size . As a demonstration program it has been extremely useful . Since then there have been some systems which are still kept under wraps by their users, which do mixed production in a production worthy system . The Westinghouse system is one which is in the public domain, and about which a good deal of interesting information is available . It is, however, really a precursor system to the industrial cost-effective ones . Figure 45 shows an early Draper Laboratory's System in Cambridge and one concept of assembly systems . There are some interesting lessons to this one because there are mixed technologies, hard automation and robotics involving a great many different part feeders converging on a station which assembles all the parts . It looks cluttered and a good deal of special engineering goes into it . The Japanese have decisively rejected this model, and we believe the approach that is favoured in Japan in probably more representative of what will be used in production .

Technologies of robotic and anificial vision systems • PHILIPPE MILLERS

14.5

Fig . 45 . Draper Lath robot assembly demunstraioa .

Fig . 46 . Cyhervision PC board component assembly .

My next subject is Robotic assembly applications . Figure 46 shows a Cybervision System, our system for inserting nonstandard components on printed circuit boards . Figure 47 is a close-up, You can also add a third tool, the camera, to provide vision feedback with a `lampshade' around it to provide good illumination (Fig . 48) . In this example, the customer

wanted to verify the nameplate data on the relays, and also make sure that they are not reversed 180 degrees . If necessary, this. system provides X and Y offset information to deal with part-to-part variation between outside dimensions and location of its electrical contacts . Westinghouse uses vision feedback in one of our systems in Baltimore in very much this

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Robotics & Compurer-integrated Manulactiming a Volume 1 . Number 2, 1984

manner . Inspection takes place from underneath so that the leads can be observed as they conic through the holes to correct pin entering . This is done partby-part for nonstandard components . Figure 49 is one of the most significant applications because it shows production in batches of keyboards with a variety of layouts and legends . At station 1 (Fig . 50) vibratory feeders are feeding keycaps, using a combination of standard automation and robotics under control of a vision system . Four units are required to keep up with the demand, filling tubes with an Autovision system inspecting Fig . 47, PC hoard-do e-up.

Fig . 48 . Cybcrvision vision option .

Fig . 49 . DEC keyhoard assembly application .

Technologies of robotic and artificial vision systems • PHILIPPE VII i .E.RS

a's, b's, c's, etc . and rejecting wrong or defective keycaps . This battery of tubes then goes to a second station seen in Fig . 50 . It goes from one station to the other by a very ingenious material-handling system known as a young man, who supervises the entire system . There the AID 600 assembly robot is doing a very interesting thing : robots as compared to human beings are not as dexterous and naturally, if used the same way, are a little hit slower. However, a robot can use 22 fingers, pick up 22 key tops at a

147

time, and insert them in groups and that's why robots can do in 50 seconds what humans can do in 8 minutes despite the fact that the unit operation per finger is actually slower. Figure 51 shows the marriage of two techniques . On the right is a CAD/CAM system . You specify on the CRT where and what it is that you are trying to assemble, in this case some bolts are being torqued down in a window roll-up assembly for a car door . The exact position of the bolt heads is not known

Fig. 50. DEC keyboard assembly-station 2 .

Fig . 51 . Robotic assemhly-car door .



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Rohutic, & Computer-Integrated Manufacturing • Volume I, Number 2, 1984

Fig, 52 . Close-up of car door bolt tightening .

because they are in slots to accommodate assembly tolerances . The vernier offset is commanded by the same ubiquitous camera mounted on the end of the robot which provides 'vision offset' . In Fig . 52 it is seen close up . The robot arm has the circle of light around it and within the light ring is the solid state video camera . The camera determines precisely where the torque wrench should he applied and then the bolt head is actually torqued down . MEGASSEMBLY SYSTEMS Figure 53 is an artist's conception of a simple `Megassembly' system . 'Megassembly' is a new term

that we have coined as a generic term to describe the cooperative behavior of ten or more robots typically performing a series of interrelated assembly operations as in progressive assembly . Production economics suggest that Megassembly is the way of the future . The Japanese are already using Megasscmbly in very significant ways, often using SCARA robots such as the Ilirata unit in Fig . 54 . The SCARA robot is a very inexpensive ingenious robot because it is quite simple . It eliminates one of the most difficult requirements, dealing with a changing gravity vector, since it is hinged in the horizontal plane . In its minimum configuration it has two

AI- 2 contro .ler SpeCmt purpose Pick and test st 51 box rework Finished ports

4 .'sic^ units

8sr,chroncus rcrsport system

Fig . 53 . Artist's conception of small megassernbly system .



Technolugm of robotic and artificial vision systems • PHILIPPE VILLERS

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fig. 54 . Hirata SCARA robots .

sifFAI7i

Fig . 55, SCARA robot with vision .

degrees of freedom, all in a plane so you don't worry about the vertical stiffness in fighting gravity . As a maximum you have two more degrees of freedom, a rotation at the wrist and an up-and-down motion either at the wrist or at the base . Fig . 55 shows an Automatix SCARA robot with a vision system .

It turns out that for a great many simple assembly tasks, most operations are stackable, that is vertical mounting . The SCARA recognizes this preferred assembly axis and yields a low-cost stiff robot which is being used in very large numbers in Japan (about 1500 in 1982 alone) .



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Rolxnies & Computer-Integrated Manufacturing a Volume 1, Numhei 2, 1984

One such installation is the JVC line for assembling video cassette recorders . There are about 70 stations of which more than 20 use SCARA type robots, Most of the rest arc simple X/Y tables under numerical control or simple automation . At each station, unlike the Draper example, you put in only two to four parts . 'Then the assembly fixture or cart goes

to the next station . This requires an asynchronous transportation system that stops at each station . Each station is controlled independently in Japan . That is, it starts work when it receives a cart . It stops when it has completed its work . and releases it to go on to the next station . These Megassembly systems can do mixed pro-

Fig . 56 . Aerospace assembly of composites .

Fig, 57, Airplane wing with robotic drilling .



Technologies of robotic and artificial vision systems • PHILIPPE VILLERS

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Megassembly as done in Japan in the last 18 months is spreading fast . In the United States it is essentially nonexistent although there may be isolated examples in a few companies who are not yet prepared to talk about it .

Fig . 58 . CAD/CAM link-AVII .

duction . In some cases 20 or more different models on such a line under program control, and therein lies the secret of its economics . That is why the second'Battle of Poitier' is being fought in France . We know that in France the French Customs requires video cassette recorders to be imported only through the town of Poiter in central France, to slow down imports . Why have they done this? If you look at the JVC line or the Panasonic or Hitachi line, you'll see that conventional assembly methods are obviously noncompetitive with these Japanese Megassembly systems . So the alternative to doing Megassembly is to fight a rear guard action such as the Battle of Poiter.

AFROSPACE APPLICATIONS Figure 56 shows aerospace industry work with composites . Composites can be assembled using robots for batch production . They can be cut out using ultra high pressure water jets as water knives . These are some of the areas being closely looked at, but not yet used in regular production . Figure 57 shows a robot drilling holes on a wing during assembly . This is very important because the aerospace manufacturer is always plagued with thousands and thousands of jigs and templates . Drilling, riveting and inspection without templates using robots with vision feedback systems represents a mammoth savings potential . This is a first and rather crude demonstration of that capability which is not yet used in production in the aerospace industry . CAD/CAM LINKS Figure 58 shows a gentleman designing a welding path on a CRT screen . In Fig . 59 a Robovision 11 system executes the design immediately using a CAD/CAM link to transfer the weld path data to the intelligent robot controller .

Fig . 59. Robuvision welding-CAD/CANS link_



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Robotics & Computer-Integrated Manufacturing

There is a universal language of data exchange between CAD/CAM systems called Interactive Graphic Exchange Specification (IGES) which was designed to solve the Tower of Babel problem . It is now a national standard supported by the National Bureau of Standards . IGES facilitates integrated systems because information needs to change between different manufacturers' products at both ends of the CAD/CAM chain .'1'he forthcoming IDES 11 should

• Volume 1, Number 2, 1984

provide a useful basis for a standardized transfer of information . This paper has reviewed robotics technology and illustrated many applications where robots have increased manufacturing productivity . Note : Autovision, Cybervision, Rohovision, AID, RAIL, and Weld Cell are all Registered Trademarks of Automatix Inc .