“Predicting Performance Times of Robot Systems”

  • H. J. Lewis
  • D. R. Towill
Conference paper


It is essential that for efficient design of manufacturing systems, reliable techniques are available for predicting the performance of mechanical handling equipment including robots. Because of the analogies frequently drawn between robot and human operator anthropomorphism(1), it is logical that one approach to performance prediction should be via Robot Time and Motion (RTM) studies(2). This leads to the concept of a computer based RTM analyser, the essential components being;
  1. (a)

    sets of element tables for given robot designs

  2. (b)

    computational performance models for given robot designs


The task input to the RTM analyser is the RTM specification of work method for a given industrial robot, whilst the output is the performance prediction for the specified method. Reference 2 uses a linear regression equation to predict elemental RTM times as a function of the distance travelled during a specific motion. However, the reference also indicates that there is a surprisingly large variation about the mean trend line when measurements are made on the Stanford Arm. It is also possible to show that the approach can lead to large prediction errors when the robot motion pattern contains a number of small segments(3).


Industrial Robot Model Reference Adaptive Control Quadratic Regression Model Large Prediction Error Active Force Control 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. (1).
    McDaniell, E. and Gong, G.,(1982). The language of robotics: Use and abuse of personification. IEEE Trans. PC-25 (4), 178–181.Google Scholar
  2. (2).
    Letchman, H., and Nof, S.V.,(1983). Performance time models for robot point operations, IJPR, Vol.21, No. 5, 659–673.CrossRefGoogle Scholar
  3. (3).
    Towill, D.R. (1984). A production engineering approach to robot selection. Omega. Vol. 12, No. 3, 261–272.CrossRefGoogle Scholar
  4. (4).
    Gold, B. (1982). Robotics, programmable automation and international competitiveness. IEEE Trans.EM29, No. 4, 125–145.Google Scholar
  5. (5).
    Fohanno, T. (1982). Assessment of the mechanical performance of industrial robots. Proc.6th Int.Conf. on Ind. Robot Tech. Paris. 349–359.Google Scholar
  6. (6).
    Engelberger, F. (1980). Robotics in Practice, Kogan Page, London.Google Scholar
  7. (7).
    Rodgers, P.F.,(1978). A time and motion method for industrial robots. Industrial Robot, Vol.5, No. 4, 187–191CrossRefGoogle Scholar
  8. (8).
    Nof, S.Y., and Letchman, H. (1982). Now its time for rate fixing for robots. The Industrial Robot, Vol. 9, No. 2, 106–110.CrossRefGoogle Scholar
  9. (9).
    Hewit, J.R., and Burdess, J.S. (1981). Fast dynamic decoupling control for robotics using active force control. Mechanism. Mach.Theory. 16 (5) 535–542.CrossRefGoogle Scholar
  10. (10).
    Dubowski, S., and Des Forges, D.T., (1979). Application of model reference adaptive control to robot manipulators. IEEE Trans. AL25, No. 3, 468–479.Google Scholar
  11. (11).
    Lee, C.S.G.,(1982). Robot Arm, Kine- matics, Dynamics, and Control IEEE Computer, Dec. 62–80.Google Scholar
  12. (12).
    Asada, H., Kanade, T., and Takeyama, I., (1983). A direct drive manipulator in B. Rooks “Developments in Robotics 1983”, IFS Pub. Bedford, U.K.Google Scholar
  13. (13).
    Lewis, H.J. (1985). M.Eng. Thesis, UWIST, Cardiff. (in preparation).Google Scholar
  14. (14).
    Drazan, P.J. (1984). Development of Placemate Pneumatic Robot. Journal of Robotic Society of Japan, Vol. 2, No. 4. 22–25.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1985

Authors and Affiliations

  • H. J. Lewis
    • 1
  • D. R. Towill
    • 1
  1. 1.University of Wales Institute of Science and TechnologyCardiffUK

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