The role of intelligent robots in flexible assembly

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The Role of Intelligent Robots in Flexible Assembly 1. Introduction

R. T o m o v i d Electrical Faculo', University of Belgrade, 11000 Belgrade, Yugoslavia

In order to run a flexible assembly system in the optimal way in terms of productivity, two

D. Zelenovid a n d D. Se~lija Faculty of Technical Sciences, Institute of Industrial Systems Engineering, University of Novi Sad, 21000 Novi Sad, Yugoslavia In order to be able to justify the use of intelligent robotic stations in the flexible assembly, the answers to the following questions must be available: (a) performance specifications of the intelligent manipulator matching the flexibility potential of the whole assembly process; (b) the relative productivity gain of the intelligent robotic station taking as the reference the gripper operated manipulator; (c) the impact of the increased local (station) productivity on the system performance. In this paper, we outline a methodology by which the answers to the above questions can be derived once the design parameters of a flexible assembly line have been given.

Kevwords: Flexible assembly, Group technology, Classification system, Dextrous hand. Sensors, Intelligent robotic system.

Rajko TomoviC born

in 1919, Baja, Hungary. Professor of control theory at the Faculty of Electrical Engineering, University of Belgrade. In addition to scientific papers, has pubfished several books in France, USA, USSR, on analog computers, sensitivity theory, nonlinear control, robotics. Currently, is involved in the development of non-numerical control of robots based on the transfer of human motor skills and reflexes to the knowledge base of a computer. Elsevier Computers in Industry 15 (1990) 131-139 0166-3615/90/$03.50 "~ 1990 - Elsevier Science Publishers B.V.

Dragutin Zelenovi~ was born in 1928 and graduated from the Faculty of Mechanical Engineering in Belgrade in 1957. He received his M.S. degree at the Novi Sad University in 1972, and his doctor's degree at the Faculty of Mechanical Engineering in Novi Sad in January 1975. In the period from 1957 to 1965 he worked in the cutting tool factory "Jugoalat" in Novi Sad on various engineering jobs, to the most part on the design of technological structures and work process management. He was elected full-time professor at the Faculty of Technical Sciences in Novi Sad for the subject Design of production systems at the end of 1976. He was Dean of the Faculty of Technical Sciences in Novi Sad from 1975 to 1979, and from October 1987 to October 1989, the Rector of Novi Sad University. At the Faculty of Mechanical Engineering he set up the Department for Industrial Systems (now the Institute for Industrial Systems Engineering), which served as a basis for the development of research in industrial production systems and a base for cooperation with the industry. In addition to his main occupation, namely, teaching and research work, he was actively involved in cooperation with the industry, which resulted in more than twenty elaborated and implemented technological processes - - plants which are in operation today. He is a member of the Working Group of the International Federation for Information Processing - - IFIP, W.G. 5.7 for the development of information-management systems, member of the Editorial Board of the "International Journal of Production Research", London, member of the Editorial Board of the "International Journal of Computer Integrated Manufacturing", London. He is an associated member of the Academy of Art and Sciences of Vojvodina.

Dragan Se~lija was born in september 1955. He graduated at the Faculty of Technical Sciences, University of Novi Sad, Yugoslavia. He has received a MSc degree in 1989 at the Institute of Industrial Systems Engineering, Faculty of Technical Sciences, University of Novi Sad. Presently he is a research assistant at the Institute of Industrial Systems Engineering. He has been involved in research projects in the areas of design-for assembly, robotic assembly and dextrous multifingered robotic hands.

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conditions must be met. The dead time of the assembly line when switching from one group of products to the next one must be minimized. In addition, the flexibility potential of all stations of the assembly line should be matched, including robot stations as well. Otherwise, the least flexible and productive station will impose its inferior performance upon the assembly line as a whole. When it comes to robots, the flexibility to handle parts with the wide spectrum of shape variations is quite limited when using machines without sensory feedback and dextrous hands. Sensory driven manipulators with dextrous hands are in this regard superior to nonintelligent robots. Increased flexibility thus obtained will improve the performance of the whole assembly process. Such a general statement must be, however, quantified so that the flexibility gains due to the replacement of conventional robots by the intelligent ones can be assessed, and the investment decision justified. In order to be able to justify the use of intelligent robotic stations in the flexible assembly, the answers to the following questions must be available: (a) Performance specifications of the intelligent manipulator matching the flexibility potential of the whole assembly process; (b) The relative productivity gain of the intelligent robotic station taking as the reference the gripper operated manipulator; (c) The impact of the increased local (station) productivity on the system performance. In this paper, we shall outline a methodology by which the answers to the above questions can be derived once the design parameters of a flexible assembly line have been given.

2. Manipulator Specifications The flexibility potential of an intelligent manipulator can be evaluated by taking into account the following features: Dextrous hand: number of available grasping modes. Sensors: touch, position, pressure, 2D vision, 3D vision. Manipulator: number of degrees of freedom. Control: numerical, expert system, learning capabilities.

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A unique and general way to assess the flexibility potential of an intelligent robot is not commonly accepted. For the application in view, it is not essential. As pointed out above, we are taking the point of view that robot specifications should be the outcome of the flexibility of the assembly line, and not vice versa. A robotic station with flexibility larger than needed with respect to the flexibility of the assembly process is. clearly, overinvestment while the station with smaller flexibility than needed will slow down the whole line. Consequently, robot properties should be determined by the system engineer planning the required flexible facility.

3. Plant Identification Any flexible assembly process consists of common hardware and software elements (flexible stations, robotic stations, transportation equipmenL basic assembly operations, etc.) whose performance must be specified in the planning stage. Modern approach to the planning of flexible assembly systems relies on two principles: the group technology approach and the system identification. Since the nature of assembly systems differs essentially from that of the control systems, specific identification methods had to be developed for this class of dynamic processes. The methodology to identify in a formal way the model of an assembly plant based on group technology i.~ available to the designers in the form of classifica.tion systems [1]. Such a model is then used for synthesis and optimization purpose,~. Classification systems rel'~ ,~n lhe fact that group products to be assembled and the corresponding assembly technology are decomposable into a limited number of prinutive object shapes and operations. Classification systems differ in the way how the process decomposition into elementary shapes and operations is structured. In terms of general system theory, a classification method is actually equivalent to plant identification in control engineering. In fact, classification model serves for synthesis purposes and optimization of the assembly process. In this context, the classification system is used to derive the specifications of the intelligent robotic station in the planning stage. The classification system I1S-08-MH. designed

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b) Fig. l. Classification system IIS-08-MH (a) Workpiece oriented: 8 attributes, each attribute having 10 different values. (b) Process oriented: 14 attributes, each attribute having 10 different values.

in the Institute of Industrial Systems Engineering, Novi Sad, Yugoslavia, has proved to be very convenient for our purposes [2]. The system consists of two parts; the workpiece-oriented subsystem and the process-oriented subsystem. T h e workpiece-oriented subsystem consists of 8 attributes, while the process-oriented part deals with 14 attributes. Each attribute has 10 fields, from 0 to 9. The structure of classification system IIS-08MH is shown in Figs. l(a) and (b). Workpiece weight affects the choice of the manipulator pay load performance. The fields of attribute 2 are used to identify the geometric primitives needed to

describe product parts, subproducts and products. The shape and size features of relevant geometric primitives are described using attributes 3-5. An example of such a schema representation for the cylinder is shown in Fig. 2. Schemas of all necessary geometric primitives are stored in the database. The full view of the workpiece-oriented classification look-up table is shown in Fig. 3. The structure of the process-oriented classification subsystem is shown in Fig. 4. As pointed out, the classification system is being used here as a tool to determine the level of robot intelligence matching the required process flexibil-

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ity. In order to be able to serve such a purpose, the original classification system had to be extended with data pertinent to the robot flexibility.

4. Robot Station Identification In the absence of generally valid criteria to define the flexibility of intelligent manipulators, a

pragmatic approach has been applied. The availability of grasping modes, specifically the ones which are essential for assembly, is, evidently, a useful flexibility measure. Automatic adaptation to arbitrary target shapes is another desirable performance feature of dextrous hands. The richness of sensory information is equally important. All these features are incorporated in the Belgrade USC multifingered hand which was selected to

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R. Tomovid et al. / Intelligent Robots in Flexible Assembly

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replace the gripper of an ASEA I R B - L 6 / 2 manipulator with five degrees of freedom and a payload of 60 N [3,4]. The Belgrade - USC hand is an autonomous terminal device not requiring remotely located actuators. Consequently, the exchange of the gripper for the dextrous hand is straightforward. The fitness of the Belgrade - USC robot hand to handle parts in group assembly has been determined in the following way. In the first place, a broader class of group products for which the flexible assembly line is being designed must be specified. The trend to cover very broad classes of products may produce correct general answers

which are, however, of no use when it comes down to quantitative decision making• In our case, the flexible assembly pertains to small-sized mechanical parts. More specifically, to a spectrum of valve products. The group technology study of the above products has shown that only three grasping modes must be performed by the dextrous hand; the power grasp, the pulp pinch, and the lateral grasp. This is, however, the first step in the identification of the dextrous hand performance. A subdivision of grasping modes into a discrete set of hand openings is desirable for practical reasons to avoid unnecessary specifications of the finger control

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vector. The rationale for such a subdivision of the grasping modes is suggested by the characteristic thumb opposition angles with respect to other fingers as indicated in Fig. 5. Fixing the set of thumb opposition angles, entails the hand opening values for the selected grasping modes. The list of feasible grasping modes, hand openings and corresponding control variables is given in Table 1, In addition to grasping mode selection, the target approach direction of the manipulator must also be specified. It turns out that for the class of assembly operations in view only three approach

directions are relevant. They are shown in Fig. 6. Relying on above data, a list of rules giving the set of nonfeasible and the set of preferred grasping modes for a given geometric primitive is prepared. These rules are derived by geometric and functional considerations or, if needed, by experiments. Such a list of grasp mode selection rules for a cylinder is shown in Table 2. The performance specification of the dextrous hand necessary for evaluation purposes is attained by associating the relevant items of the workpiece-oriented classification subsystem and

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Table 1 List of feasible grasp m o d e s for Belgrade - U S C hand. G r a s p m o d e s are defined with the angle of r o t a t i o n for t h u m b and each pair of fingers. ~ m e a n s that b o t h pairs m o v e together Grasp mode

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feasible, i.e., preferred of such an output is the case that assembly into elementary oper-

Table 2 Rules for deriving list of feasible grasp m o d e s for g e o m e t r i c p r i m i t i v e c y l i n d e r in upright position. C o l u n m 2 gives list of a'~ailable grasp m o d e s for Belgrade - U S C hand, D i m e n s i o n s of cylinders that are not possible to g r a s p with p a r t i c u l a r grasp are given ia c o l u m n 3. C o l u m n 4 r e c o m m e n d s the r a n g e of c y l i n d e r d i m e n s i o n s to be g r a s p e d with a p a r t i c u l a r g r a s p No.

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ations like pick-up action, transportation, insertion, positioning, alignment, the robot identification procedure is completed.

5. Flexibility Gain Evaluation In order to evaluate the flexibility gain due to the introduction of intelligent manipulators into assembly lines to be meaningful for quantitative decision making, the underlying assumptions should be explicitly stated. To begin with, the reference robotic station must be specified. In our case, it is the gripper fitted manipulator without sensory feedback. The proposed evaluation procedure is generic so that it can serve the purpose when some other robotic system is taken as reference. Namely, the problem how the acquisition of the reference robotic system has been justified is not the subject of this evaluation procedure. It is also essential to understand the crucial difference which exists when evaluating a monorobotic assembly process and a multicell assembly system. In the first case, station-to-station effects must be only determined while in the second case the impact of the local flexibility gain on the system flexibility must be assessed. The difference will be best understood if evaluation criteria for the two cases are considered. For instance, in the station-to-station case, a rather simple evaluation criterion may be very helpful. Let us assume that for a group assembly process the exchange operations of monofunctional grasping tools will take totally n time units. Assuming that a multifunctional terminal device can handle all these operations without the dead time, then the complete dead time interval will become productive so that the increased value of the output can be directly derived. Evidently, such an approach is of no use in system studies where local gains on the system output are decisive. Thus, instead of mathematical expressions, assembly line simulation must be involved in order to arrive at the specific quantitative answers. Another basic input to the evaluation process is the cost of the intelligent robotic station. It was pointed out that a choice of the complexity level of such machines is in the user's hands. This

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entails, clearly, a range of acquisition costs. In some cases, the use of a dextrous hand requires the acquisition of special computer facilities and other related costs. Such a state of affairs is an additional reason to study carefully the role of intelligent robots in the planning stage of the production system.

6. Conclusion The aim of this paper is to promote the studies of the impact of intelligent robots on the flexible assembly using tools of computer and systems sciences. Formalized process descriptions, identification and other such tools can be very helpful in this domain of system studies as well. They help to reduce costly tests on real objects. Such tests cannot be, clearly, bypassed but simulation studies serve as reliable guidelines in the design of intelligent production system. In the next phase of this research, attention will be paid tO measurements of parameters needed to describe the performance of an intelligent manipulator in a metric way. For instance: switching times when changing grasping modes, average enclosure time for typical product parts, cost/effectiveness analysis, etc. At this moment, such data can hardly be found in publications. Once they are available for different designs of intelligent manipulators, simulation studies will produce more realistic results.

References [1] D. Zelenovi6, I. Cosi6, D. Sormaz and Z. Sigarica, "An approach to design of production systems of higher effectivity level", in: H.J. Warnecke and H.J. Bullinger (eds.), Towards the Factory of Future, Springer, Berlin, 1985, pp. 699-706. [2] D. Se~lija, Intelligent robots and flexible assembly, Masters Thesis, University of Novi Sad, 1989. [3] G. Bekey, R. Tomovi6 and I. Zeljkovi6, "Control architecture for the Belgrade/USC hand", in: T. Iberall and S. Venkataraman (eds.), Dextrous Robot Hands, Springer, Berlin, 1990. [4] R. Tomovi6, "Advances in the design of autonomous dextrous robot hands", J. Robot. Comput. lntegr. Manuf, in press.