Planning and Scheduling of Assembly Systems

Keynote Papers

Planning and Scheduling of Assembly Systems H.Van Brussel (1),

Department of Mechanical Engineering, Katholieke Universiteit Leuven/Belgium

ABSTRACT

In this keynote paper. the state of practice. the state of the art and the state of research concerning planning, scheduling and control of assembly systems are discussed. First, the concepts of planning, scheduling and control are elucidated and put in reladon to one another. Next the most important evolutions in computer-aided intuitive and fomal assembly planning and scheduling systems arc explained. Finally, the important issues of programming, real time control and sensor integration are dealt with.

KEY WORDS:assembly. plmning. scheduling, control. off-line programming, sensor integration.

1. INTRODUCTION

As life times and lot sizes of modem products become ever smaller, it is iiicreaqingly necessary, in order to produce economically. to aqsenihle those batch quantities, in arbitrary sequence, on flexibly automated assembly systems. Such systems are very expensive, hence the requirement of high nexibility. On the otlm hand, the technical problems encountered to meet these goals .arc still so formidable that mixed wlutions. where manual stations are co-existing with hard automation and flexibly automated stations. am still the only econoniically and technically viable solutions. The success of an attempt to flexihly automate the as.scmhly of a particular product is to a l a r p extent depending on the previous effort to design the product with automatic assembly in mind. In the present paper it is assiimed h a t tlle design for assembly methodoloy lit bcen rigorously applied. The reader is referred to [I ,%a] to learn about his timely subject. Flexihle a%sembly systems can be extremely complex. Large amounts of assemhly and/or inspection stations (manual,dedicated or flexibly automated) are to be interconnected with transportation systems, temporary storage buffers and feeding devices, in such a way that the assembly t h s are minimised. This complexity calls for an appropriate methodology for the planning of assembly systems. The result of this planning activity is an “optimal” assembly system layout and 11 assembly sequence and process plan.

The next, lower level stage in the assembly activity is to find the best way of how to get the parts thmugh the assemhly system in order to miniis e assenibly tuim airl/or costs. miis is tlle scheduling problem. The lowest level in the assemhly hierarchy consists of contrulling the assembly systeiii iii such a way that it perfectly executes the scheduletl tasks, at the right moinent. Preferahly, the control system detects eventual nialfiinctions or defects and forces tlle schetluler to reschedule the asseiiibly task, h function of the r e n i a i ~ i gcapabilities of the assenibly system. A closer look at the concepts of planning, schecluling and control is appropriate here, at the outset of tliis keynote paper. Production (Assembly) planning occurs hcfore the start of the actual production phase. It groups process planning aiid long term production scheduling. ‘& process planning activity uses thne-invariant information about facilities and products. It answers tlle questioii of how the product is going to be made and results in facility layouts and process plans (which process and which hardware for each operation). The production scheduling activity integrates time-varying infonnation of the facility on a long temi basis to generate a mclsfcr pruducriotr sclwdule. It answers the question of how to send the paRS through the system in order to reach a specifed production volume in a specified time span. It can be considered as a long term production policy. The horizon ranges from years and months to weeks. Assembly process planiiing yieltls inctlids on how to assemble prducts, production s c l d u l h g on wllen and where.

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production orders, from the moment mCy are scheduled to the moment they an completed. This activity has a time horizon of minutes to fractions of seconds. Assembly production control gives directives on when and where the process steps arc finally to be executed, basnl on the latest information on the status of the assembly system. Figure 1 puts these concepts of planning. scheduling and control in relation to each other. 2. PRODUCTION PLANNING OF ASSRLIIILY SYSTEMS 2.1. Terms of reference

In this DaDer we are noinn to restrict ourselves to ComDutcr-aided uscmblv comP”ter aided mmufaclurini planning plmiGg’mthods. Over general, has been a shih from a variant based variative/generativemethods towards a purely generative tarlY variant aPProaches were based On P U P nchnO1OgY. pans having simh featuresarc m p e d into Paa families. For each a Process Ian to be ‘PO” when quired. is gemmed and stomi in is a clear emphasis in present process planning research on creating fully generative pmccss planning systems. The idea is that a complete product as technO1o@caldata anrl stored in a model,containing geamctrical as

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Pmluct model descriptions an found in [3.8,13. 26,291. The basic vehicle for representing the product to be assembled, the assembly task and the assembly system in a computer is a CAD database. 3D - modellea provide an exnllmt geometrical representation medium, but they must be a u g m n t d with technological data in ordcr to fully represent the complete assenibly (or disassemhly) process. Most present-day CAD systenls allow thc integration of user specific procedures, so that integration of technological data is straightforward. The abject oriented approach, in wliicli proclucts are described in terms of function oriented features and attrihutcs, is getting more and more populur as a basis for cornputer assisted planning systenls. Very efficient models result, due to the multiple inhe rita m features of objca oriented models [26,41].

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Figure 1. Production planning, scheduling and control and their tinle horizon. Production (Assembly) control deals with the execution of the master praluriion schedules generated in tlle production planning phase. Operation scheduling or process scheduling starts from master production schedules and adapts these to the actual situation at the moment of execution. Indeed, even with good estimating procedures. conditions for caoacitv availabilitv. actual setup’thne a d tliic-dates will likely have chang& fr6m wlleii tliey were estiiiiated aiid used in developing the master production schedule. Therefore. the time horizons here range ‘fro& days aid hours to minutes. ~ h real c time control of IIK assembly system is at the lowest level. It is responsable for the execution illK1 monitoring of ti= activities lle~e~w for the assembly of the

Annals of the ClRP Vol. 39/2/1990

Fibre 2. Information

ofassembb’control according to Eveaheim [37].

As stated in 121, planning is a search problem that can be broken down in three basic sleps: 1. PMing a good representation of the problem, including start and goal

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states and relevant constraints. 2. Generating alternative solutions by selecting resources to perform actions that change the system state. 3. Searching for the optimal solution. It seems clear that the assembly process laming problem is too complex to be haidled h one tina. A hierarchical &composition Lit0 more niaiiageable suhproblems is a conmionly accepted snlution in those cases. Everslieini consiilers a four level hierarchy: rla order level, the product level. the assenihly system stnw?ure level and the capacity (space, staff, equipnant) level. Ilie resulting information model is shown in Figure 2.

The above d e f i i d planning problem solving steps remain valid, wliatever solution nuthod is adopted, inckpendent frnin whether intuitive nielhntls 01 AImethods are used. As indicated by Weule [ZJ, analysing the methodology of experienced human assembly process planners can reveal inucli about tlie structure of an appropriate computer aided assenihly prcmss planner. The human planner extracts planning infomiation froin drawings and patt lists by mentally disassembling the product. From the nature of every joint, he deduces the joining action necessary to assemble the pan (e.g. insertion. screwing, press fit). This information also KNW him for chooshg the right assembly equipment (e.g. screw drivers, pan carriers, ..). The comct reversal of the disassembly process yields all feasible assembly sequences. Trying to computerise this human assembly planner tunis out to be a foniiidable task. Indeed, the, processes going on in tla human mind during tla planning operation a n almost completely unknown.

2.2. Ccncral framework fur assembly planning

comparison hetween siiniilatinn and Markov chain metlincls is also given in [IS].A sbiiilar analysis tool, based on queuing theory, is incorporated in the integrated planning system MOSYS. developed at IPK,Berlin 191. The finally obtained solution still must he subject to &tailed economical analysis. to verify whether it satisfies the ciiteria estahlislud 81 the uutset. If necessary. final modificatiuns have to be made until tlie ultimate optimum is reached. Figure 4 gives an overview of the methodology.lt is only a pncsible scenario uf il planning strategy for assemhly systems. It is very general and uuitiible for all levels of automation. Up till now, most of the critical. or call it creative phases in the nwhotlology are executed by a hunian expert. based on experrise. acquired over the years. How far computers and expert systems can take over will become clear in the near future. Some of the successful attempts will be discussed U i the sequel.

2.3. Automated planning systems. 2.3.1. Conpurcr assisted ploruiirig systenrs Many attempts have been made to (partly) automate the design of assembly syslems. Crucial is to find nuthods on how to represent the assembly problem mid how to automate the reasoning mec1i;uiisnis followed by a Inniian assembly expert. The more simple design aids only try to automate the more stnightfoward functions. like economical analysis of alternatives, or selection of a suitable assemhly method for a given process step. based on a database containing assembly expertise and on suitable selection rules.

A general methodology, bnrrowed from Wiendald [4] and Wamecke 1241. is presented l ~ r eas a valuable framework for generating assembly planning systems.

The ASSEMBLY programme. devclopecl at KULeuven [61, is a good example

As already stated above, the assembly pliinning problem is too complex to be treated in one time and tlierefore a hierarchical approach is advisable.

of this automation level. It is written in dBaseIll and Motlula-2 and implemented on IBM-compatible PC under DOS operating system (Figure 5). Several phases can be distinguished in the programme:

First a rough planning is executed. In a first step. the assembly prnhlem is analysed and IIK crireriu which the assembly system must satisfy are established. These criteria can be technokogicol (e.g. flexibility level), orgunisational (e.g. available installation turn), ecorlontical (e.g. investment cost) or personul (e.g. working conditions). Based upon t l a an;llysis of the product (described by drawings and part lists or by a CAD database). the sequence and type of all required assembly operations are detennined. A suitable representation aid for this step is a11 nssrntblj p p l r (Figure 3). At this stage. it is already possible to have a rough idea of the type of equipment needed for executing the different assembly steps and the degree of automation required or possihle. The mol t of this rough planning step is a hnsic layrct of an assemhly system. It is evitknt that the level of tlie available assembly expertise is decisive on how close this rough estimate is to the optinium.

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Phase 1: Phase2 Phase 3: Phase 4 Phme 5:

An economical screening of different assembly methods to tleteniiine the most profitable assembly method. A technical analysis of the product, based on time and motion studies and Boothroyd analyses, enabling the estimation of a$sembly timw and costs. Task assignment and line balancing. Computer assisted selection of optimal assenihly equipment for each operation. Graphical layout of the assembly system.

The conniclered assembly methods in phase I are: - manual assemhly. - manual assembly with mechanical assistance. - fixed autoniation with indexing transport system, - fixed automation with asynchronous transpnrl System. - robotic assenihly in line with feeding magazines. - rohntic assembly in line with aiitoiiiatic feeilers, - rohotic cell with feeding magazims, - mhntic cell with automatic feeders, - multi-axhl inseiiion systems with magazines, - iiiulti-axial insertion systems with automatic feedem

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Fiiurc 3. Typical assembly p a p h for a light switch.

A p r e 4. General methodology for assembly systems planthig 14,241.

Next comes the fine planning stage. Here. t l single ~ assemhly stations have to be optimised and integrated into a total solution. lniponant parameters in the choice of a solution for a single station an cycle time and conception of 11% work carrier. A popular aid in assisting with the choice is the use of so-called "nrorphologiculrubles" [4]. where alternatives are offered for all types of single operations. By combining these elementary solutions and taking care of compatibility considerations. optinrol subsysrennls result. In the integration of the subsystems (workstations) into a complete nssembly system. an important parameter is sysrent efficierir>t.The availability of the stations and the size of the eventual buffers between stations an directly influencing system efficiency. Simulation is the only feasible, hut very powerful technique available to optimise system efficiency IS.161. Mathematical closed-form solutions are indeed only possible for very simple systems. In [151. a mathematical solution method is proposed. based on the tlaory of Markov processes. This inherently statistical approach only allows the computation of average values of tinws, huffer capacities. etc.. An interesting

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Figure 5. a. Input-output model of the ASSEMBLY programme [61. b. Sensitivity analysis giving cost per product versus annual production volume for different assembly methods. c. Relative assembly costs for different parts of a product. d. Graphical layout of the selected assembly system. For e;tcli mctliocl, tla global inveWncnt cost. operator costs and niaintenance costs are ciilculotcd. bmed on a niotkl containing the technical characteristics of each 111ethod.

Consequently. based on an adopted investment criterion, the optimal assembly inethtrcl IS selectetl. I'lle available investment criteria are: - least ;ssemhly coat. - simple payhack, - discoutited payback. - retuni on investnlent, - net presetit value, - profitability index. In orikr to ftnd an adequate solution, reliable data are required conceniing: tlle company ( yearly cost for assembly worker,..), the equipment (average price...), the assenibly methods (average assembly tune per pan,..), the product family (production volume...). An atltkd sensitivity analysis capahility allows to enh:urce the tkcision innking power of this phase, hy studying the influence of variations of crucial aranleters (e.g. production volum) on the optimal assembly inetliod (Figure gb). Phase 2 studies the technical feasihility of the method selected in phase I . Here again , estimates are made of the time and cost associated to the assembly of a component. To this end, the feeding, grasping and insertion of each component are studied separately, making use of Boothroyd charts. by applying time and motion studies and by thumb rules. The results can be used for two purposes. First. the estimated costs allow to make a value analysis of a product design (Figure 5c). Second. the estimated times an used in phase 3 to distribute in a balanced way the tasks over the work stations. L i e balancing in phase 3 is based on following data: assembly tunes - pncedence relations (some tasks have to be performed before others) desired output voluine or cycle tune.

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Tllc halancing problem is solved in a heuristic way, based on "preceilence miitrh balmcuig with successive maximum element times" 1421. The user can choose between single product and multi-model balancing. Th+ output of this phase is: - for each product: assignment of tasks to diverse work stations. insertion devices, feeders, dead time, efficiency. - for each station: assembled parts, equipment, dead time. efficiency.

In phase 4 the right equipment is allocated, hasetl on a database of available assembly hardware. At the end of the database session. a summary is made in an investment report. This acts as an equipment order form with reference to prices and manufacturers. Phase 5 pmvides the user with a drawing aid for defining the layout of the aqscmhly system (Figure Sd). 'Ihe structure defined in pha..e 3, is automatically converted by the graphical software into an intennecliate drawing. Interactive graphical procedures are available to adiust these intermediate results, so that it complies with the given geometrical information about the meinbly equipment and assembly site. Similar systems have been developed by several other research institutes and compniies. They all have the same characteristic: they merely automate the database functions and some heuristic search functions. The more creative rea..oniiig functions, like auhrinatically ikterniining the meinbly sequence are not availahle. However, as pointed out by Seliger 171. even in this intuitive stage, human creativity and inspiration CYI be supported by expert systems. holding a large knowledge base and committing the user to drop his sometimes prejudicial behaviour.

The integrated computer aided assemhly planning systeni developed at lWF/lPK Berlin 171. is organised around a relational database and inipleniented on VAX-computer or on PC.It contains seven modules (Figure 6):

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equipment. The result of this step is a geonletrical desription of layout alternatives. 4. Evaluation. Next to the traditional investment criteria, also factors like safety and ergonomy are taken iito accuunt by ruluiulg an additional value analysis.

5 Prncess planning. The planner fixes the movements of the pads in tlie

assembly system in a so-ca led pseudo code. A graphical siniulatioit package ~ allows verification of niotions and relative positions on t l screen. 6. Simulation. An interface to the IPK simulation system MOSYS is provided to check and to optimise the line balancing and the matrrial flow tluough the assembly system. 7. Communication. The conrdinates of the motion trajectories arc downloaded ~ controllers tluough a local network. in t l robot

The systematic planning system developed by IPA, Stuttgart [24]. is very similar to that of IFA. Hannover [Z].which was already discussed above. It also follows a two-stage approach: a rough and a f m planning pliase. The rough planning includes the analysis of the assembly task and the selection of principial solutions In the time planning, these principal solutions are further detailed and integrated into a total system. Selection of the optinlal system is a supporting software prograt!unc, based on economical considerations. In [a], called PRISMA.is described. It assists the planner in an interactive way, rn all stages of the planning process, but it is especially designed to forecast the assembly times for a layout, obtained by the platurr.

WZL,Aachen [37]developed a very broad framework for computer assisted assembly system design. It is based on the so-called "STRUKTOGRAMM" for the configuration of orders and on a computer programme MONET, for the configuration of order specific assembly. Makino 1391, ckvelo d a versatility index as a quantitative measure to indicate versatility orboth an assembly process and an assembly system. Because of its high data reduction to one index. it could be useful as a fust estimate of an appropriate assenibly system, prior to a detailed analysis by a more comprehensiveplanning system.

2.3.2. Formal methods Tlle above description of computer aitkd, hot still mainly bituitive. assenibly system planning methods give a good picture of the pnsent state of practice.There are attempts to formalise OM or more of the planning steps involved. They an based on a mathematical description or a model of the product, of the process and/or of the system. Manipulation of and reasoning on these nioclels. mainly based on AI techniques, leads to more optunal solutions. Some of these techniques will bc discussed now. As discussed ahove, tlie first step in assembly system planning is the determination of the different assembly operations to be performed and their sequence. There are two efficient methods. known in literature. for tlle exhaustive automatic determination of assembly plans. The first one is known as dle liaison-sequence method [lo]; tlac seco~ld une is the suhussernhlies dec:omptwifioftmerhnd [I 1,12]. Every method of course statis from an appropriate process model. In the second method. the prduct P is modeled a.5 a 5-tuple:


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C is th set of the elementary components G is t l r set of geometrical liaisons S is the set of joinings D is the set of complementary characteristics f is a function mapping; it defines. for each joining or complementary characteristic, the set of involved pans and ChardCIeriStiCS.

A liuisori exists when two elementary components share a common surface in the assembled product. A joining adds cohesion to a liaison. Examples of joinings a n: welding, screwing,.. . The complemcnrary churacrerisrics arc mainly additional non-assembly operations, like inspection and testing, labelling. painting, cleaning, etc.. Any ussemhlv proress for a given product is a set of actions being realised in series and/oiparallel. Among these actions we distinguish dedicared acrions, which create some characteristics of the product and pusiriomal wrions, consisting of moving the pan through the assembly system To evaluate assembly plans, only the dedicated actions arc considered and the positional actions are postponed to the process planning stage.

... Figure 6. SINC~UR of the assembly planning systeni of IPK [7]. 1. Assembly sequence planning. Based on a 3D-representation of the product to be assembled aid supported by an expert system, t l l e assembly sequence is determined and represented under the fomi of a precedence graph.

2. Rough planning. In this phase. a general solution is worked out. Based on morphological tables and again assisted by an expert system, assemhly methods arc assigixd ID the different steps in the assembly process.

An ussenthly rrer is a tree (in the graph-theoretical sense), for which the root represents the product, the nodrs represent the subassemblies or pats and tlle leuves represent the elementary pans. An assembly tree is the first description level of the assembly process. It docs not include the positional actions. The assenihly comrrainrs can be. diviikd in two categories: the operative and the strategic cotistrairrfs. The operative constraints define whether a given operation is feasible. They include geometrical, material and stability constraints. The strategic constraints arc those which deal with the operation sequencing, dictating by strategic considerations, e.g. one wants to clelay the introduction of a p m into the assembly system as long as possible due to its fragility.

Software programmes (e.g. LEGA 1121) have been develojxd for generating all feasible assrnihly freessatisfying the operational constraints. These constraints are described in a database including a set of facts (the unfeasible operations) and a set of rules which formalise some properties of the operative constraints.

3. Detailed planning. With the help o f a component datahase, the firnctional units rlctcrniitled in the previous phase are replaced hy conunercially available

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Next step. the tree sekctiort, is the the most difficult to autoinate. Even for simple pmtlucts with few components ~ h cn u m k r of possible assetiibly trees is fairly high (between 130 a i d 140 for a product with 10 components). Strategy constraints arc iiiuoduced to reduce the number of feasihk assembly trees.

upon traversing tlie graphs. 111 181. the flavor colicelit is used for product motlellitig. Agein, tlisassenihly graph are detenninerl eH1 an expert systems approach is adopted to find the optimal dissssemhly graph. The assembly task planner is based on STRIPS (a classic in planning literature).

The assemhly svstenz design aid sirrtulurioii is perfurmed in a more heuristic way, Each of the trees. selectecl in the previous step, is tlnnshinned in a functional iliagrani. using stantlarcl symhols. based on integrated NICS or on human expertise. Then, equipnxnts are assigned to the different operations. again based on heuristics. A software ALSE I 121 was developed to speed up this analysis for each tree. Each time a Petri net [ 18.191 is autoni:itically deduced and a quick simulation performed, yielding cycle time. equipmelit occupation ratio and buffer occupation. Weule 131 iises a different approach to determine the optimal prececknce graph. The utiderlying concept is to look how human erpens ileal with this prohlcm. Thcy apparently do this through mentally disassembling the product. In 131. a specially designed software, called KOMPASS. is described to aialyse the disassembly of even complex pralucts hi luiiited tune. It employs heuristic techniques. supported hy fast sorting and filtering algorithms to cktermine how every pan can be disassembled. This presupposes knowledge of all jouiing surfaces inherent in the pmdua,i.e. the outer surfaces of every single part which come in contact with other parts or whose clearance towards other p;uts is too small so that restrictions may result for assemhly. The result of this analysis are the feasible direcricms of disassemb/y For every single pan in relation to all other pans in a matrix. From these data, also hints for a possible base part for assembly and/or for convenient subassemblies can be obtained. Often, purely geometrical analysis of the product is not sufficient to obtain complete disassembly knowledge. Tcchnwlugical specifications are required in many cases. For instance. the type of fit (press fit. loose fit) detemiines the joining method. 'Therefore. the CAD-system employed for geometrical analysis must be enhanced with user specific technological procedures. Following the desnihed analysix,thc system starts to disassemhlc the procluct. Parts arc disassembled along the computed direction after a collision check and the operation is displayed for user approval. The data discribuig the disassembly, which in reverse order discribes the joining operation. is stared. 'Ilie operator can always intervene and interact with the progrrnme. Upon complete disassembly, the precedence graph is automatically generated. Spur 1131 uses a similar approach. Srarting from neutral CAD-models (using solid niodeller COMPAC) of the constitutive pans of the product, function (and assembly) oriented pruduct models are derived in an interactive way. These inodcls form the basis for checking asseniblability and functionality of a product. The determination of the assembly sequence from the product model apparently happens on an intuitive base. Ihe resulting assembly model is independent l'runi assembly hardwan. Heemskerk [2.30] uses sill a different method to automatically determine assentbly sequence plans. It has some resemblance to the method explainetl in I IZ].The product model contains a bill of material. a part positioii network and a part relation network. The output of the system is the so-called asserrihly stare trutwitim~diagram or in our present temiinulogy t l l e precedence graph o r assembly sequence graph. Reduction of the number of feasible graphs is facilitated by introducing the concept of rlusfers. A cluster is a group of parts that belong logically together because they have some asseiiihly features in cornnoti, because of which they would nortiidly he assembled together. Iliese clusters. together with application of accessibility ;ud stability heuristics illso elunbate a lot of unfeasible sequences. Slipitalni 1141 starts. like Weule 131. from the statement that disasseliil*ly is a logical starting point for cletennining the assembly sequence. He tkveloped a system where the input is tllc definition of the product Structure. i.e. the bodies comprising the S t N C l U R and their location and orientation. 'Illis information is input in the fonn of CSG (Consfnrch've Solid (iconwhy) trees.

The system comprises tluee modules:

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The first module checks t l valiclity, ~ correctness and stability of the defined SINCIUCC.

- The second module automatically generates tlie assembly sequence. - The thud module identifxs collision-free paths for the assembly robot.

Assembly expertise Equipment database Production data

2.4. Input-output mudel of assemhly planning

An computer-aitlnl assemhly planning system can he considered as a black hox. (interactively) transfonning input data into desired or optuiial output data. This black box representation is illustrated ui figure 7. Input

- Specification of the product (fandy) to be assembled: product m&l. lot sizes, assembly tiim horizons

- Knowhow on solutions (assembly expertise) m d on available equipment (existing systems) output

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The "optimal" assembly system (eventually several alternatives). This

includes definition of tools. tool interfaces. product carriers and tlxk interfaces. ..., Product definitions under the fonn of precedence graphs or similar representation methods, - Process plans for the chosen assembly system. This includes: * nptimiwd course (transfer) motion plans * fine motion plans for feeders. gripper exchangers, .. - Pmcess plans for product and accessories. This includes fine m i o n plans for the actual assembly operations eventually with provisions for sensor integration. Also fme motion plans for non-asscnihly operations are to be included e.8, testing. - Nominal schedules (master production schedules). based on estimated production numbers. used for siniulations. dimensioning and for comparing alteniatives.

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2.5. State of affairs in assembly planning 2.5 I . Statr of practice

The scope of application of present assenihly plmiing systems is confined to nxdiuin to large series of product families with few members. Tllc products are assembled hi consecutive single-product batches. The assembly systems are designed ui function of the prmluct (family). Routing md system control are sunple (buffered lines).

2.5.2.Stare ofart Computer aids are becoming availahle to design and dimension optitnised assembly systems. niey a n mainly of an intuitive nature and a n just a Ilelp in tltiit they autoinate routine coniputing jobs. The crcative md intelligent work still remains to be done by thc operator.'llx use of simulation pmgranums for fme-tuning and for comparing alteniatives is mire and more recogniscd as a vriy powerful means to get as close as possible to the optliial solution.

2.5.3.State of research As can be seen from the above discussion, serious research is under way to arrive at inregrafed forntal assenrhly process planriing systenis. These systems must allow a more complex routing: therefore the generated plans must be nontletenninistic and nonlinear and they must not overconstrain tlie system. They also must support the schcdulutg and control of the underlying system (see further).

Research is also directed towards alternative assenibly system layouts, providing more Jexibiliry by a special design and layout of the niechurtical hardware. One very promising concept in this respect is the sub batcli principle developed hy Amstroin [ 171. It offers many advantages for assemhly in small quantities. Instead of serial assembly of a number of parts after each other onto one carrier (base plate), a number of the sanw kind of parts me assembled onto the same number of base plates. The number of such base plates forms the subbatch. Combined with an appropriate layout of the assembly line it offers following advantages:

- one robot can assemble a large number of different part 'ypes. thus avoidiig

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Process plans Master production schedules

Figure 7. Input/output model of assenihly planning. A heuristic algorithm is used for tktemining tlie disassembly sequence nnd the associated disassembly directions. The efficiency of the algorithms is provided by the use of connectivity graphs. These latter have to be constructed only once for a given structure. The decisions on sequence and directions an made based

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a coupling between the number of parts in a product and the system capacity cycle time can be made short as gripper exchange times arc rcducetl and as handling distances can be short for all parts product-specific aweinbly equipment can easily be brought in and out the system.

Although Ihis sysnm might not be generally applicahle for economical reasons, it niiglit be a flexible solution for small lot size variants in a large family of protluct variants. It could, by sending amud c.vriers with tooling and cluriers with pans, avoid high uivesrments in fued peripherals at the workstations. Similar alternative as..enibly systcnw liavc been developed in Japan [ZQ]md France (Sonel). In tlle progmnune ASSEMBLY 161.the Sony "SMASH-Cell" 1201 is offered ils one of the basic assembly methods. Future planning systenw must provide for these new system layout concepts. Further, the preucnt planning systems provide fixed routings of the parts through tlie system. Provision for arbitrary routing would enable replanning upon occurrance of abnormal situations. This requires planning methods for uncertain environments as opposed to ~ h cpresent rigid planning schenies. Nonliiiear planning methods need to be developed. The ESPRIT FLEXPLAN

project is dealing with these problems. although not exclusively for assembly 1381. Coloured Petri nets are used as mathenidtical tool [ I X . I Y J . Simulation must keep track with the developmelit of these advanced planning systems. Reseaich is needed into the mathematical description of the kliaviour of complex flexible asscnibly systems. Discrete mathematics might LK a g00d vehicle to accomplish this. Figtire R summarizes pictorially tlie different modules of a future generation asseinbly planning system [3 I.

in advance. would then backtrack on tlie disruption time. and integrate disruption status antl production state into a dynamic assembly system model a i d reschedule from the new date. Schetluling of flexihle assemhly systems is first of all cliaracterised by coiiiplexity (.nuinher of orders, variety of products. variety of resources). A scheduler must work under several types of constraitits. prducr nrieiired (e.g.:clue iliite) a i d rewiirce orierrted (e.g.:liniited amount). Another feature of the scheduling problem is uncertainty, due to e.g. disntptive events (release of new orders. tlisturbeices in resources). This feature requires reactive scheduling capahility. Performance measures are essential for tlie evaluarion of 3 chosen scheduling policy. Examples of performance measures are: protluction rate, inean tanllless. ... . In short, tlie sclwdulhg problem can be sunuiiarisetl as '' the timed assigiimeiit of operutioiis r o wor-kswrioiis and resoim-cs. iir order io oprimise a giiwr ser of objectives". Many have attempted to find a mathematical solution to the flexible assenihly scheduling problem. There are siniilarities with job shop srhedirlirig, for which optimal solutions have been generated I21 I. There are also differences however, which make tlw assembly scheduling problem much more complicated. One of these differences is for instance that in a job shop a part has in most cases exactly one operation which can he executed next, while in asembly there are many c'andidare operations which can be performed next. In this mathematical approach. a schedule is a set of 5-tuples . where i denotes pan i whose operation j is processed on workstatioti k during time periotl [s.s+wJ. The mothemuticul model of the scheduling problem may now be stated as follows. Given: I, the set of all pans; Ji. the set of all operations necessary to produce pan i: K. the set of all workstations; Wi'k. the duration of operation j on pan i at workplace k Si'k. the starting tbne of dperation j on part i at workstation k, the model can be exbressed as: Minimize Maximum (s.. + Wijk), for dl i.j and k. IJk suh,ject to following constraint sets: - each workplace k cannot process more than one product at a time. - each part i cannot be processed by nmre than oiie workpldce at a t h e , as well as meet technological sequence requirements. - the requixment that among the possible operations involving part i and workstation k, exactly one is executed. The o/,jec.rivcfioi(.rioii is fomiulated as "minimizing niakespai". It has been proven that this type of flexible assembly system scheduling prohlem is NP-coniplere, which means that direct solurion of its mathematical funnulation is not feasible, due to its complexity. even for relatively simple systems. For a problem with four workstations, ten pans and ten operntions per part, the formulation has XooOO constraints. 8000 variables of 0-1 type and 400 variables of continuous type.

Figure 8. Structure of a future assembly planning system 131.

3. PRODUCTION CONTROL O F ASSEMBLY SYSTEMS 3.1. Terms of reference

The plnnnetl assemhly system has finally to be put into operation, for which mi assembly cell control system is needed. A lot of requirements are put on such a systeni to assure that the assembly system behaves like plruied, but also that eventual disruptions do not stop the system. Already the start-up or initialisation phase is imponant. Considerable time is nomially lost during start-up of a new system. The main issues during start-up are: ininiinisc start-up turn, -easy crash recovery. -easy htroduction of new suhsysteins (feeders,..), - easy introduction of new products (new family meinlwrs). - fast UUHI effective fuie tuning. ~

The cumiit practice in this respect is mainly manual. Programming is hy teachin. separately for each single sration or device. Adjustment is by trial and error. Error recovery is normally accomplished by "start again from zero". Solutions are being studied to automate or facilitate start-up hy introducing sensors in the system, for nionitoring the system state such that start from zero is no longer required. Sensors for calibration of the workstation environment enable the introduction of off-line programming of the workstations. The introduction of sensors is not straightfonvard however. Inclustry does not like them, mainly because of their high price and also because of the coinmon belief that they slow down the operation. As will be seen funher, however, apart from being very useful in the start-up phase, sensors can play a very important role in enhancing the operational flexibility of the assembly system.

During normal operation, a g o d assembly cell control system must schedule operations and dlocate resou~ceshi order to optunise oiie or more user-defined criteria. like maximum throughput, ininimum w o k in progress, certain priorities. Moreover, this opthisation must be able to Iiiuide variation in loads. cycle times, setup times, system availibility or other "slow" variations.

3.2. The scheduling problem Given a set of production orders, coming from the production planning level. tlie scheduler must assign operations to workstations, allocate resources uid build a .schedule over a predetermined length of time. It then mwt pass this sclieilule to the real time system controller as an "advised" productioii plan to be followed. The controller then sequences the operations hi the system. according to the plan passed by the scheduler. A good scheduler must have reaclive capabilities, i.e. it must be able to deal with disruptions occuring at system level. A good way of tkdlmg with disruptions is to treat them first at system controller level. Depending on their importance, they are reported to the sclteduler. together with their type and disruption time. The scheduler. working

Therefore, heuristic, rule based solutions are k i n g sought. in order to generate good start solutions. fiillowecl hy iterations of "bottle neck itletitification and relwation" cycles until a "best" solution is found. In [ 291. a hierarchical heuristic approach is followed. which is representative for the modem approach of pmluction system scheduling. The developed scheduler allows for ntulriple prfkfiict types to he awenibleti simultaneously. It features reucfiite ,clrrdulirig in tlie sense that it resclrdules. based on the present system state. It does not schedule in advance. so that the new schetlule is not optimal. It is essentially designed ro work in a MRP eiiiVroiwiertr. The MRP system generates the master production schedule (Leirl I ) , rtsultllig in lot sizes, production mix, and component due dates for the scheduling horizon (typically I week). hi the scheduler (Level Z), a schedule decision is again viewed as a S-tuple, consisting of a productbatch designation. a1 operPtion designation, a workplace specification, a statling tune and an operation duration. The scheduling decision prohlem requires finding a schedule S which optimizes a minimum makespm objective. The heuristic scheduling approach computes a schedule in two steps. In Step 1 it is detennined whether a set of batcNoperation/workplace assignment decisions should be made at time t. In step 2, the decision step, executed if required by step I , the batches and operations are determined which should he processed next by the workstations. Technical operation sequencing constraints are thereby observed. Repeated execution of step 2 detemiines for each workstation a sequence of hatchhperation couples to be executed consecutively by the workstation; in other words. a schedule is computed. Level 3 is the dispatcher or real tinr cotit roller.

3.3. A case study: SESFAC/FACCS A g o d example of a state-of-the-art rule-based reactive a.wembly operation scheduler, which incorporates sonie of the above stated features, is SESFAC. developed at KULeuven [32].

SESFAC is p;ut of a two-level assembly control system: the operatim ,sc/iedii/iiiglevel (SESFAC) antl the real rinte roiirrol sysreni (FACCS). The two levels exchange information. as illustrated in figure 9 . A customer o n k r enters the system as a master production schedule or directly at tlie operation scheduling level as urgent orders or as new orders to he scheduled. The first task of the scheduler is to divide the orders into production orders. A production order consists of a limited numher of instances of products grouped together, having the same due date and assembled hi a iioninteriuptible way. At system level, it is generally represented hy a pallet. Given a set of production orders. tlie scheduler assigns operations to workstations. allocates resources and builds a schedule over a predetermined length of time. It then passes the schedule to the real time controller (FACCS) a s an "advised" production plan. FACCS sequences the operations in the cell according to the plan passed by SESFAC. It also can make autonomous decisions. e.g. when the schedule

641

/Master

production smedules\

The glohal databaqe. comprised in the declarative knowledge. is divitktl into the static and dynamic database. The static database contains the tinieinvariant description of he assembly system (workstations, mobile and static resources tms rt system, setup times). Product information is contained in the pruduct efinition. The most commonly used represeiitation is tlx precedence graph. In SESFAC (and FACCS), an adaptation of this concept to tlie real tinle environnlent is proposed: the dynamic precedence graph. hi a noniial precedence graph, selection of an operation (ncxk) automatically activates all outgoing edges. In a dynamic precedence graph, the executing operation activates one or more outgoing edges explicitly by their position (left to right). It will e.g. select tlifferent edges for a success, a soft failtire an11 a fatal crash. Figure I l b shows a possible execution trace for the product in figure Ila. Further, a set of possible operdtions is tletimd for each workstation, with for each operation a 1Lt of resources. The dynamic database contains the ~ status. llie workstations, the static and mobile system states and t l production resources have their own dynamic definition. The production tlynaniic status is represented by the production order states (waiting. processing, done). Two more tuguments make up tlie dynamic ddtabm: the scheduling titile and tlle workstation loads.

8"

Process scheduling (SESFAC) Process! c ~ u t e

Real-time control System slatus

Control Assembly

system

I

I

Figure 9. Two-level assembly control system SESFACPACCS [32.40].

The scheduling decision phases are divided into tllrcc decision modules. Each module represents a separate decision level. They are the assign module, the allucation niodule and the sequence module. The assign niodrle is used at the launch stage of product orders into tlle system and treats the reservation and allocation of mobile resources to product orders. The al/ixatiofi nrodulc treats the allocation of mobile resources to production steps, once the product ortkr is already in the system, and allocates producr orders to workstations. It is responsible for bottleneck detection. The segirertce nionltle is located at workstation level; it sequences the product order in the workstation, using local dispatching rules.

I Figure 10. Structure of the rule-based scheduler SESFAC. passed by SESFAC is not executable, FACCS can look for ad hoc solutions. It also can use unused opportunities, e.g. idlhig of a station. Disrupions occuring at system level arc also treated by FACCS. Depending on their importance. they arc reported to SESFAC, with the type of disruption and thne of occurence. The scheduler, working in advance, backtracks on the disruption tulle, integrates the disruption status into the dynamic model of the assembly system and reschedules from the new date.FACCS controls the actual assembly system. It gets scheduling advise from SESFAC and executes this advise whenever possible. When unpredicted events prevent this, it will handle the new situation in real time. SESFAC will be informed so as to synchronise its simulation. II

Load Bas

E3-4 Insert-

Scheduling is a discrete event decision making activity, changes of system states occuring only at dicrete times. Therefore the system is driven by a simulated asynchronous clock. Depencling on the type of event that occu~s, the clock controls the different scheduling decision phases. It is the heart of the scheduling system. The systeni can advance forward or backward in time. depending on the scheduling mode. SESFAC thus evaluates different scheduling alternatives without actually performing them. It utilises a living model (simulation) of the real assembly system. This simulation is t o be regularly synchronised with the real world, thmugh feedback from FACCS. in order to predict accurately the future behaviour of the assembly system. Tlle scheduler activities are seqiientially organised and they are: activity scanning, mode branching, tune updating, activity processing and generating of new events.

The schediilrr works in one of four modes: normal, controlled, disruptive or backtracking. Each mode is characterised by a set of specific rules. The notion of mode is linked with the o c c u r e i ~of~disruptions, necessitating rescheduling. Tlle mrnial nude is active when no prior schedules are made; then the system searches forward in time, based on global and local dispatching rules. It is the most sophisticated operational modc. The conwded nvdc is short terni based. In this mode, tlle scl~dulerfollows tlle decisions made on-line by the real time. controller FACCS. The disritpfive nude is the resclieduliitg iiiotle. It uses information from the previuus schedules to rescl~tliilein the most effective way. TIE backtrackitrg mode is characterised by a disiuption (late and an event. The system must then backtrack to the nearest system state prior to disruption. After that, tlle scheduler is switched to controlled mode. SESFAC and FACCS share some. major functions. The fmt one, the product definition has already been mentioned above. A second s h m i function is resource management. The operations must be assigned to one of the workstations capable of performing them. Each workstation has an operat ions repertoire. It needs local resources (e.g. a feeder) to perfonn an operation. When these resources are down (feeder jammed or empty) tlw repenouuc is diminished by one or more of its operations. Sensors arc needed to observe the state of those resources.'Ihe operation assignment hnction must adapt to this automatically. Workstations also use mobile resources - such as the pallet carrying the product - to perform operations. These must be allocated to the relevant stations. This is typically an allocation for a short period of time. (Mobile) resources that arc transformed by operations must also be bound to product oriicis. 'lliis binding typically lasts until the product order is finished. Resource management must hantlle all this without allowing deadlock or starvation 1401. Pially. SESFAC and FACCS have to coopcrate in order to optimise tile assembly system performance. The allocation decisions have a major hnpact on the system performance. When the allocations an done, a final sequencing of operations must be carried out (e.g. to miniinise gripper exchanges). These optunisations arc normally done by SESFAC and executed by FACCS. The occurencc of unpredicted events or badly estiniated performance will trigger actions by FACCS to handle the particular situation in red time. 3.4. Programming and control issues.

L

-

1

Figure 11. a. Produa ckfiinition grdph. b. A production ordcr execution trace. SESFAC is a rule b a d scheduler. Its structure is shown in figure 10. A knowledge base is composed of declarative and procedural knowledge. The declarative knowledge consists of facts about the assembly system configuration and the production status. It further contains production objectives, system performance meawns and the various scheduling rules. The procedural knowledge contains the knowledge used for the application of the scheduling rules (meta knowledge).

642

Whatever the planning and scheduling system used. fiuially the components of the assembly system ( robots, pallet transport systems. sensor systems, etc.) have to be commanded in their proper languages. There arc as many different Ianguages as r o b 6 and the structure of a PLC programming language is completely different from that of a robot or a inachim tool. Conventionally. to programme an assembly system, one has to write a set of independent programmes. one for each workstation, and then integrate them into a running system. This is itnpossible for systems with reactive scheduling capabilities. like the ones described in section 3.3. Indeed, since an assembly system is a distributed system, the different wokstation progranunes will run in parallel, exchanging information for synchronisation and coordination purposes. Soon the task of the progratnmcr of such a system becomes equivalent to programming a modem (multi-tasking) computer without the services of an operating system. At KULeuven. a pragmatic approach was adopted for programming flexible

assrmbly systems r.1 I]. Workstations are considered as peripherals of the system contml computer. They are "instalktl" into the system by connectbig them to the control computer through a "device driver" (Figure 12). 'Illis is a translation programme, working a,, an interpreter (not as a post processor). translating the workstation commands. ststul in a coninion, general purpose task specification language, into local conunands in the local workstation language.

i

In tliir second level of involvement, two regimes could be distinguished. First, the sensors could be used for monitoring, outside the control loop, allowing the assembly system to ti in the nominal. fast mode. The sensor action is delayed as it gives advice on how to perform better next cycle. This mode does not slow down the action. It can also give advice to the scheduler for eventual rescheduling. Second, the sensor could be integrated in the control feedback Imp of the assembly system, making it behave as an intelligent. flexibk. adaptive. autonomous system. online assessing and curing process variations. This mode is normally complicated to implement and it slows down the assemhly process. For these reasons, it has not been generally accepted by industry. Moreover, the standard controllers on the market do normally not allow to readily incorporate sensors in the control loops. The existing robot pmgramming languages do normally not provide. or in a very priniitive way, for sensor-based programming. One of the most advanced task specification system for "compliant motion" tasks has been worked out in [28]. It is based on external sensor loops (around the existing robot positioning loops), so that the method can be applied to any (position controlled) robot. Task specification is in terms of goals to achieve. A typical task specification for an insertion operation under force control would be: "Move compliantly in xdirection, such that all forces and torques remain zero" (successful insertion). "until the x-force becomes ION" (end of insertion). It is clear from this example that motion is specified in the nominal dinctions (ideal world). while all errors are c o m t e d autonomously by the lower level force control system. As already stated. industry does not like yet sensors. However, the more autonomous assembly systems will be in the future. rhc more sensors are going to become hlispensahle. In [27], Siemens presents concepts and strategies for sensor programming in multisensorial environments, showing clearly the increasing awareness of industry of the importance of sensors in flexible awembly.

3.6. State of affairs in assembly control 3 6 I. State of practice Figure 12. Programming an assembly system through "device drivers'' [31]. Similar approaches lieve been worked out at McGill University 1331 and at IVF in Linkoping (2.51. through the creation of what they call "abstract or virtual sensing and manipulating devices". This resulted at McGill in the WRAP environment. supporting environment motlellbig, sensor integration and progrmuning of distributed workstations. Related work is found in NJST, with their AMRF system, based on a seven level hierarchy (361. The system to be controlled is viewed as three separale hierarchies: a task decomposition hirrarch.v (H), a nwld inodd hierarchy (M) and a seiwvr processing or frrdback hierarchy (G). To programtne the system in a simple way, the 11, M. and G modules are iniplenlented as finite state automata. At every clock cycle. the input vector (coninlaid + feedhack) is read and an oucput vector and a next state. are computed. In this way. the system is progriunnied using Slate transition tables instead of traditional procedural robot programs. In 137). the hierarchical approach is claimed to beconle too complicated fur complex manufacturing (assemhly) systems. As iui alternative, a heterarchical control architecture is proposed. By Iocathg decision making wllere Lifoniiation originates, global infonnation is clained to be reduced to a minimuin, scheduling becomes dynanlic, machults and parts become "intelligent" entities thst cooperatively interact, and the overall system is decomposed into functionally simplified, modular parts. Mahnnn developed the "activity controller" theory to coordinate multiple robuts and other niachines 1351. His system assuciates a resource supervisor with every shared system resource. A resource supervisor prevents access to the resource if it is occupied. checks the eligibility of the request, and tlien queues and prioritizes them. At Linkoping University [W], the COPPS system is being tkvelopd. It is a software system for defining and controlling actions in any mechanical system involving actuators and sensors. e.g. also assembly systems. It considers questions such as how to represent dependancies between sensor data and output data and how to specify whether actions should be executed ui parallel or in sequence. Tliere is a tendancy to use 3D-simulation systems, not oiily for assisting with layout design of miuiufacturing (assembly) systems, but also for off-line programming. The tenn graphical programming has been coined in this respect. Some commercial systems arc already making inroads (e.g. ROBCAD. GRASP). 111 1221, a 3D-simulation system is described, supporting the design of manufacturing systems (planning and layout) and the off-line programming of the system elements (mbots, machines).

3.5.Sensor integration issues Sensors intervene a? two levels in assemhly systems. At tlle more primitive level, they illc used for moniloring and control futictions e.g. to notify tlle real time controller and/or the scheduler of any disruption in the normal functionality of the system elements. These sensor data are relatively easy to cope with at PLC level and also at scheduler level. The second, much higher level of involvement of sensors is in the generation of fine fine motions of the assemhly operations themselves. Examples are vision sensor controlled object lorlisatioti prior to grasping or. still more difficult: force controlled insertion motions. Otller advanced uses of sensors in assembly systems are the use of force and position sensors for calibration of the robot and of its environment (e.g. detemiining pallet or feeder poqitioning errofis).

Current practice is off-line scheduling (master production scheduling), without any feedback from the system state. Simple decision methods, like linear pmgramming. an used. Production is mainly in terms of single product batches. with relatively large lot sizes. 3.6 2 State ofresrarch. Future solurbns

Future solutions must provide on-line, reactive, opportunistic scheduling of multiple prnducls simultaneously, based on a detailed system model. Synchronous simulation must be provided in order to enabk fast rescheduling. Intelligent decision methods must be used so as to take multiple criteria into account. Then is an urgent n d for research in the area of sensor integration into the programming and control of flexible assembly systems. Within the EC, a considerable amount of research money is spent on integrated projects in assembly automation. One good example is the Esprit project 384 on "Integrated Information Processing for Design, Planning and Control of Assembly" 1291. Within the EUREKA programme, the FAMOS initiative deals exclusively with research and development in all aspects of flexible assembly. Information IS disseminated in the regular FAMOS workshops. held all over Europe. 4. CONCLUSIONS

By its complex narure, assembly fiercely resists the many attempts to flexible automation. Fully integrated solutions, enabling the automatic design of optimal computer-integrated assembly systems and processes, basal on computer-based product models, arc not yet available. This paper described some promising partial results, already obtained by the important researchers in the field. It is to be expecred that the object oriented approach to product modelling, together with rule based methods for planning and scheduling and growing acceptance of sensor integration will lead to fully integrated solutions in a not too distant future. 5. ACKNOWLEDGEMENTS

rile success of an undertaking like writing a keynote paper is to a large extent depending on the contributions from specialists in the field. I was lucky to receive an overwhelming amount of valuable information from colleagues CIRP members and others. I thank everybody who contributed. by sending material or by giving comments. I am especially thanLtul to P. Valckenaen, my coworker for a long time, who helped me substantially, and my CIRP colleagues G. Sohlenius, E. Agennan, A. Amstrom, W. Eversheim, H.P. Wiendahl, H.J.Wamecke, G. Duelen. G. Spur, C. Heemskerk. G. hgermiiller. H.Makmo. V. Milacit. The fmancial support from the Belgian Mmistexy for Scientific Policy (DPWB). for the project on Robotic Assembly (1983-1988) is also gratefully acknowledged. Many of the rcsults obtained through this grant have been used in this paper.

6. REFERENCES [I] [21

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