A Geometric State Sensor for Robot Workcell Monitoring

  • Christopher H. Orgill
Conference paper


A processing scheme is given that provides data at a point along a sensory continuum which is distinct from the region normally occupied by those data provided by the sensing devices more conventionally used in robotic assembly. In contrast to the operation of typical binary sensors such as contact probes and beam-interruption devices, which give an unambiguous datum about a local event in the cell, the sensor described is sensitive to deviations of any component in the workcell volume with an implementation dependent resolution, presenting no other information except that a deviation has occurred.

Where required in. the assembly sequence, a signature is derived by a simple off-line teaching procedure. Although the use of the sensor in an analytic role is not expected to be tenable, it provides a cue for the onset of error diagnosis in a cell supervisory computer, operating either in stand-alone mode or integrated with other sensory input.

An argument from the field of statistical communication theory is presented, showing the eliciration of system behaviour from a communication theoretic formalism to be inappropriate for robot domains due to the nature of the knowledge representation. The use of domain specific knowledge is discussed with a view to establishing likely bounds for probabilistic descriptors of the scene.

The A.I technique of ‘evidential propositional calculus’ is applied for the combination of multiple independent sensor readings.


Assembly Sequence Industrial Robot Contact Probe Assembly Task Object Wave 
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. Albus J.S., “Brains, Behavior and Robotics”, Chapter 5, BYTE Publications, Peterborough, N.H. 1981.Google Scholar
  2. Brooks R.A., “Symbolic error analysis and Robot Planning”, Int. J. Robotics Research, vol.1, no.4, pp29–68, 1982.CrossRefGoogle Scholar
  3. Barnes D.P., Lee M.H. and Hardy N.W., “A control and monitoring system for multiple-sensor Industrial Robots”, Proc. 3rd Int. Conf. on Robot Vision and Sensory Controls, pp471–479, Cambridge, MA, November 1983.Google Scholar
  4. Lee M.H and Orgill C.H, “Machine constraint taxonomy and the free-transfer problem”, TR-RRG-68/86 ,Computer Science Dept., UCW Aberystwyth, 1986Google Scholar
  5. Hallam J.C.T, Kwa J.B.H and Howe J.A.M, “Rule-based surface classification using specular sonar reflections”, DAI-288, Dept. of Artificial Intelligence, university of Edinburgh, Scotland.Google Scholar
  6. Middleton D., “Introduction to Statistical Communication Theory”, chapters 18–20, McGraw-Hill, 1960 .Google Scholar
  7. Requicha, A. A. G.,“Representation of Tolerances in Solid Modelling: Issues and Alternative Approaches”, in “Solid Modelling by Computers. From Theory to Applications”, Pickett M.S. & Boyse J.W. (ed.), pp3–22, Plenum Press, 1984Google Scholar
  8. Ambler A.P, & Popplestone R.J, “Inferring the positions of bodies from specified spatial relationships”, Artificial Intelligence, vol.6, pp 157–174, 1975.CrossRefGoogle Scholar
  9. Parzen E. “Stochastic Processes”, Theorem 1A, pp 16–17, Holden-Day, 1962Google Scholar
  10. Garvey, T.D, Lowrance, J.D, and Fischler, M.A., “An inference technique for integrating knowledge from disparate sources”, Proc. 7th IJCAI, pp 319–325, Vancouver, 1981.Google Scholar
  11. Shafer, G., “A mathematical theory of evidence”, Princeton University Press, Princeton, New Jersey, 1976.Google Scholar

Copyright information

© Springer-Verlag New York Inc. 1988

Authors and Affiliations

  • Christopher H. Orgill
    • 1
  1. 1.Dept. of Computer Science UCW AberystwythAl & Robotics Research GroupDyfed, WalesUK

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