Skip to main content
Log in

An intelligent assembly robotic system based on Relative Pose measurements

  • Published:
Journal of Intelligent and Robotic Systems Aims and scope Submit manuscript

Abstract

One of the major problems in realizing intelligent assembly robotic systems has been the difficulty of compensating for uncertain and erroneous situations. In particular, pose (position and orientation) uncertainties lead to a challenging problem. In this paper, we outline a Relative Pose-based Strategy for effective dynamic end-pont control of manipulators in uncertain environments. The introduced strategy imposes new requirements on the higher levels of the control. Besides appropriate planning, control and sensing strategies, an advanced supervision system architecture needs to be provided. In this paper, the fundamental issues relating to intelligent assembly robotic system design are briefly discussed and an overall control architecture is introduced. The lower portions of the proposed architecture, viz. the Execution and the Supervisory Levels are described in detail.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Acar, L. and Özgüner, Ü.: Design of knowledge rich hierarchical controllers for large functional systems,IEEE Trans. Systems, Man, and Cybernetics 20(4) (July/Aug. 1990).

  2. Alami, R. and Chochon, H.: Programming of assembly cell: task modelling and system integration,Proc. IEEE Int. Conf. Robotics and Automation, St. Louis, MI, March 1985.

  3. Albus, J. S., Barbera, A. J. and Nagel, R. N.: Theory and practice of hierarchical control,Proc. 23rd IEEE Computer Society Int. Conf., (Sep. 1981), 18–39.

  4. Albus, J. S., Barbera, A. J. and Fitzgerald, M. L.: Hierarchical control for sensory interactive robots,Proc. 11th Int. Industrial Robots, SME & RIA, Tokyo, Japan (Oct. 1981).

    Google Scholar 

  5. Albus, J. S.: Outline for a theory of intelligence,IEEE Trans. Systems, Man, and Cybernetics 21(3) (May/June 1991).

  6. Amat, J., Casals, A. and LIario, V.: Location of work-pieces and guidance of industrial robots with a vision system,Proc. 4th Conf. Robot Vision and Sensory Controls, London (Oct. 1984), 223–230.

  7. Angermuller, G. and Hardeck, W.: CAD-integrated planning for flexible manufacturing system with assembly tasks, inProc. IEEE Int. Robotics and Automation, Raleigh, NC, 1987, pp. 1822–1826.

  8. Barkmeyer, E.: Some interactions of information and control in integrated automation systems, inAdvanced Information Technologies for Industrial Materials Flow, Springer, New York, 1989, pp. 39–58.

    Google Scholar 

  9. Bhanu, B., Thune, N. and Thune, M.: CAOS: a hierarchical robot control system,Proc. IEEE Int. Conf. Robotics and Automation, Raleigh, NC, 1987, pp. 1603–1608.

  10. Birk, J., et al.:General Methods to Enable Robots with Vision to Acquire, Orient, and Transport Workpieces, University of Rhode Island, Tech. Rep.5, Aug. 1979.

  11. Boettcher, K. L.:An Information Theoretic Model of Decision Maker, M.S. Thesis, LIDS-TH-1096, MIT, Cambridge, MA, June 1981.

    Google Scholar 

  12. Brady, M.: Problems in robotics, in O. Khatib et al., (eds),The Robotics Review 1, MIT Press, 1989.

  13. Brooks, R. A.: Symbolic error analysis and robot planning,Int. J. Robotics Res. 1(4), 29–68.

  14. Brooks, R. A.: A robust layered control system for a mobile robot,IEEE J. Robotics and Automation 2 (1986), 14–23.

    Google Scholar 

  15. Burtnyk, N. and Basran, J.: Supervisory control of telerobots in unstructured environments, inProc. 5th Int. Conf. Advanced Robotics: Robots in Unstructured Environments, Pisa, Italy, June 1991, pp. 1421–1424.

  16. Chandrasekaran, B.: Natural and social system metaphors for distributed problem solving: introduction to the issue,IEEE Trans. Systems, Man, and Cybernetics,SMC-11(1) (Jan. 1981).

  17. Chang, E.: Participant systems,Proc. 1985 Distributed AI Workshop, Dec. 1985.

  18. Chochon, H. and Alami, R.: NNS, a knowledge-based on-line system for assembly workcell, inProc. IEEE Int. Conf. Robotics and Automation, 1986, San Francisco, CA, pp. 603–609.

  19. Clermont, G., Harmant, M. and Gaspart, P.: FIACRE: a flexible and integrated assembly cell for research and evaluation,Proc. 16th Int. Symp. Industrial Robots, Brussels, Belgium, 1986.

  20. Corkill, D. D.: Blackboard architectures and control applications, inProc. IEEE Int. Symp. Intelligent Control, Philadelphia, PA, 1990, pp. 36–38.

  21. Craig, I. D.:The Cassandra Architecture, Ellis Horwood, 1989.

  22. Craig, J.:Introduction to Robotics: Mechanics and Control, Addison-Wesley, Reading, MA, 1986.

    Google Scholar 

  23. Dai, F.: Modelling of robot workcells for a programming and simulation system,IMACS SMS '88, Cetraro, Italy, Sep. 1988.

    Google Scholar 

  24. Decker, K. S.: Distributed problem-solving techniques: a survey,IEEE Trans. Systems, Man, and Cybernetics,SMC-17(5) (Sep./Oct. 1987).

  25. DeMello, L. H. and Sanderson, A. C.: AND/OR representation of assembly plans, inProc. AAAI-86, Philadelphia, PA, 1986, pp. 1113–1119.

  26. De Schutter, J.: Compliant Robust Motion: Task Formulation and Control, PhD Dissertation, Katholieke Universiteit, Leuven, Belgium, Feb. 1986.

    Google Scholar 

  27. Durrant-Whyte, H. F.:Integration, Coordination and Control of Multi-Sensor Robot Systems, Kluwer, Dordrecht, 1988.

    Google Scholar 

  28. Durrant-Whyte, H. F.: Sensor fusion: when more means better,Sensors: Technology, Systems, and Applications — 5th Conf. Sensors and Their Applications (The Adam Hilger Series on Sensors), B. E. Jones (ed.), LOP Pub. Ltd., UK, 1991.

    Google Scholar 

  29. El-Shimy, H.:Dynamic Control of Assembly Robots Based on End-Point Vision and Force Sensory Feedback, PhD Thesis Proposal, Dept. Electrical and Computer Engineering, University of Waterloo, April 1991.

  30. Espiau, B.: Closed loop control of robots with local environment sensing: principles and applications,Proc. 2nd Int. Symp. Robotics Research, Kyoto, Japan, 1984.

  31. Espiau, B.: Use of optical reflectance sensors, in G. Beni and S. Hackwood (eds),Recent Advances in Robotics, Wiley, New York, 1985.

    Google Scholar 

  32. Fox, B. R. and Kempf, K. G.: A representation for opportunistic scheduling, inRobotics Research: The Third Int. Symp., O. D. Faugeras and G. Giralt (eds), MIT Press, 1985, pp. 104–115.

  33. Frommherz, B. and Hoermann, K.: A concept for a robot action planning system, in U. Rembold and K. Hörmann (eds),Languages for Sensor-Based Control in Robotics, NATO ASI Series,F29, Springer, Berlin, 1987.

    Google Scholar 

  34. Galbraith, J. R.:The Design of Complex Organizations, Addison-Wesley, 1973.

  35. Georgeff, M. P.: Planning,Annu. Rev. Comput. Sci. (1987), 359–400, also inReadings in Planning, Allen et al., (eds), Morgan Kaufmann, Palo Alto, CA, 1990.

    Google Scholar 

  36. Gini, M., Doshi, R. S., Gluch, M., Smith, R. and Zualkenian, I.: The role of knowledge in the architecture of a robot control system, inProc. IEEE Int. Conf. Robotics and Automation, 1985, pp. 561–567.

  37. Gini, M. and Gini, G.: Programming for intelligent robot systems, in S. G. Tzafestas (ed.),Intelligent Robotic Systems, Marcel Dekker, New York, 1991.

    Google Scholar 

  38. Giralt, G.: Research trends in decisional and multi-sensory aspects of third generational robots, in H. Hanafusa and H. Inoue (eds),Robotics Research: The Second Int. Symp., MIT Pres, 1985.

  39. Hardey, N. W., Barnes, D. P. and Lee, M. H.: Declarative sensor knowledge in a robot monitoring system, in U. Rembold and K. Hörmann (eds),Languages for Sensor-Based Control in Robotics, NATO ASI Series, Vol. F29, Springer, Berlin, 1987.

    Google Scholar 

  40. Hayes-Roth, B.: A blackboard architecture for control,Artificial Intelligence,26 (1985), 251–321.

    Google Scholar 

  41. Heckman, G. P. L. R.:Design, Implementation and Evaluation of a Control Structure for a Robot Visual Servoing System, M.A.Sc. Thesis, Dept. Electrical and Computer Engineering, University of Waterloo, Waterloo, Canada, 1992.

    Google Scholar 

  42. Heikkilä, T. and Röning, J.: PEM-modelling as the basis for designing intelligent robot control, inProc. 5th Int. Advanced Robotics: Robots in Unstructured Environments, Pisa, Italy, June 1991, pp. 463–470.

  43. Hendler, J. and Agrawal, A.: Mission Critical Planning: AI on the MARUTI Real-Time Operating System, Tech. Report UMIACS-TR-90-81, CS-TR-2486, Institute for Advanced Computer Studies and Dept. of Computer Science, University of Maryland, 1990.

  44. Hewitt, C.:Viewing Control Structures as Patterns of Passing Messages, MIT AI Lab., Cambridge, MA, Dec. 1976.

    Google Scholar 

  45. Hewitt, C. and Lieberman, H.: Design issues in parallel architectures for artificial intelligence, inProc. 28th IEEE Comput. Soc. Int. Conf., San Francisco, CA, Feb. 1984, pp. 418–423.

  46. Hodges, B. and Hallam, P.:Industrial Robotics, Heinemann, UK, 1990.

    Google Scholar 

  47. Hörmann, A.: A petri net based control architecture for a multi-robot system,Proc. IEEE Int. Symp. Intelligent Control, 1989.

  48. Hörmann, A., Meier, W. and Schloen, J.: A control architecture for an advanced faulttolerant robot system,J. Robotics and Autonomous Systems 7(2–3) (1991), 211–225.

    Google Scholar 

  49. Janabi-Sharifi, F.:Issues and Design of an Intelligent Assembly Robotic System Based on Relative Pose Measurements, Tech. Report 95-04, Dept. of Electrical and Computer Engineering, University of Waterloo, Waterloo, Canada, Feb. 1995.

    Google Scholar 

  50. Jones, A., Barkmeyer, E. and Davis, W.: Issues in the design and implementation of a system architecture for computer integrated manufacturing,Int. J. Computer Integrated Manufacturing,2(2) (March/April 1989), 65–76.

    Google Scholar 

  51. Kaelbling, L. P.: An architecture for intelligent reactive systems, in M. Georgeff and A. Lansky (eds),Reasoning About Actions and Plans, Morgan Kaufmann, Los Altos, CA, 1987, pp. 395–410.

    Google Scholar 

  52. Kelley, R. B. and Moed, M. C.: Knowledge-based robotics assembly system, in G. N. Saridis (ed.),Advances in Automation and Robotics, Vol. 2: Knowledge-Based Systems for Intelligent Automation, JAI Press, 1990.

  53. Kelley, R. B.: Knowledge-based robot workstation: supervisor design, in C. S. George Lee (ed.),Sensor-Based Robots: Algorithms and Architectures, NATO ASI Series,F66, Springer, Berlin, 1991.

    Google Scholar 

  54. Kelley, R. B.: Hierarchy for intelligent on-line execution, in S. G. Tzafestas (ed.),Intelligent Robotic Systems, Marcel Dekker, New York, 1991.

    Google Scholar 

  55. Kuokka, D. R.: The role of meta-reasoning in dynamic environments, in J. Handler (ed.),Working Notes on 1990 AAAI Spring Symposium: Planning in Uncertain, Unpredictable, or Changing Environments, University of Maryland, 1990.

  56. Lam, S. K. F.:High Speed Vision System Design, M.A.Sc. Project, Dept. of Electrical and Computer Engineering, University of Waterloo, Waterloo, Canada, 1992.

    Google Scholar 

  57. Latombe, J.-C.:Robot Motion Planning, Kluwer, Dordrecht, 1991.

    Google Scholar 

  58. Laugier, C. and Pertin, J.: Automatic grasping: a case study in accessibility analysis,Proc. Advanced Software in Robotics, Liège, Belgium, May 1983.

    Google Scholar 

  59. Laugier, C. and Troccaz, J.: SHARP, a system for automatic programming of manipulation robots, in O. Faugeras and G. Giralt (eds),Robotics Research: The Third Int. Symp., MIT Press, Cambridge, Oct. 1985.

    Google Scholar 

  60. Lozano-Perez, T.: Task planning, in M. Brady et al. (eds),Robot Motion: Planning and Control, MIT Press, 1982.

  61. Lozano-Perez, T., Mason, M. T. and Taylor, R. H.: Automatic synthesis of fine-motion strategies for robots,Int. J. Robotics Res. 3(1) (Spring 1984), 3–24.

    Google Scholar 

  62. Lozano-Perez, T., Jones, J. L., Mazer, E., O'Donnell, P. A., Grimson, W. E. L., Tournassoud, P. and Lanusse, A.: Handey: a robot system that recognizes, plans and manipulates, inProc. IEEE Int. Conf. on Robotics and Automation, 1987, pp. 843–849.

  63. Lozano-Perez, T.: Robot programming and artificial intelligence, in W. Grimson et al. (eds),AI in the 1980s and Beyond, MIT Press, 1987.

  64. Lumelsky, V. and Sun, K.: Gross motion planning for a simple 3D articulated robot arm moving amidst unknown arbitrarily shaped obstacles,Proc. IEEE Int. Conf. Robotics and Automation, Raleigh, NC, April 1987.

  65. Lyons, D. M. and Arbib, M. A.: A formal model of computation for sensory-based robotics,IEEE Trans. Robotics and Automation 5(3), June 1989.

  66. Malcolm, C. and Fothergill, A. P.: Some architectural implications of the use of sensors, in U. Rembold and K. Hörmann (eds),Languages for Sensor-Based Control in Robotics, NATO ASI-Series, Vol. F29, Springer, Berlin, 1987.

    Google Scholar 

  67. Mason, M. T.: Compliant motion, in M. Brady et al. (eds),Robot Motion: Planning and Control, MIT Press, 1982.

  68. McCain, H. G.: A hierarchically controlled sensory interactive robot in the automated manufacturing research facility, inProc. IEEE Int. Conf. Robotics and Automation, St. Louis, MI, March 1985, pp. 931–939.

  69. Mesarovic, M. D., Macko, K. and Takahara, Y.:Theory of Multi-Level Systems, Academic Press, New York, 1970.

    Google Scholar 

  70. Meystel, A.: Intelligent control: a sketch of the theory,J. Intelligent and Robotic Systems 2 (1989), 97–107.

    Google Scholar 

  71. Meystel, A.: Multiresolutional recursive design operator for intelligent machines,Proc. IEEE Int. Symp. Intelligent Control, Virginia, Aug. 1991.

  72. Nimrod, N., Margalith, A. and Mergler, H. W.: A laser-based scanning range finder for robotic applications,Proc. 2nd Int. Conf. Robot Vision and Sensory Controls, 1982.

  73. O'Grady, P. and Lee, K. H.: A hybrid actor and blackboard approach to manufacturing cell control,J. Intelligent and Robotic Systems 3 (1990), 67–72.

    Google Scholar 

  74. Ohara, S., Yangihara, Y. and Takahashi, T.: Robot operation based on a model updated by sensory data, inProc. 5th Int. Conf. on Advanced Robotics: Robots in Unstructured Environments, Pisa, Italy, June 1991, pp. 1658–1661.

  75. Patzelt, W.: A robot position control algorithm for the grip onto an accelerated conveyor belt, inProc. 12th Int. Symp. on Industrial Robots, Paris, France, June 1982, pp. 391–400.

  76. Paul, R. P., Durrant-Whyte, H. F. and Mintz, M.: A robust, distributed sensor and actuation robot control system, in O. D. Faugeras and G. Giralt (eds),Robotics Research: The Third Int. Symp., MIT Press, Cambridge, MA 1986, pp. 93–100.

    Google Scholar 

  77. Peshkin, M. A. and Sanderson, A. C.: Reachable grasps on a polygon: the convex rope algorithm,IEEE J. Robotics and Automation,RA-2(1), pp. 53–58.

  78. Petrin-Troccaz, J.: Grasping: a state of the art, in O. Khatib et al. (eds),The Robotics Review 1, MIT Press, 1989.

  79. Reif, J. H.: Complexity of the movers' problem and generalizations, inProc. 20th IEEE Symp. Found. Comp. Sci., 1979, pp. 421–427.

  80. Reiter, R.: A theory of diagnosis from first principles,Artificial Intelligence 32 (1987), 57–95.

    Google Scholar 

  81. Report of Working group, in U. Rembold and K. Hörmann (eds),Languages for Sensor-Based Control in Robotics, NATO ASI-Series,F29, Springer, Berlin, 1987.

    Google Scholar 

  82. Sanborn, J. C. and Hendler, J. A.: A model of reaction for planning in dynamic environments,Int. J. AI and Engineering,3(2) (1988), 95–102.

    Google Scholar 

  83. Saridis, G. N.:Self-Organizing Control of Stochastic Systems, Marcel Dekker, New York, 1977.

    Google Scholar 

  84. Saridis, G. N.: Intelligent robotic control,IEEE Trans. Automatic Control,AC-28(5) (May 1983), 546–557.

    Google Scholar 

  85. Saridis, G. N.: Intelligent machines: distributed vs. hierarchical intelligence, in D. Mladenov (ed.),Distributed Intelligence Systems: Methods and Applications, Selected Papers from the IFAC/IMACS Symp., Varna, Bulgaria, June–July 1988.

  86. Saridis, G. N.: Theory of intelligent machines,Proc. IEEE Int. Workshop on Intelligent Motion Control, Istanbul, Turkey, Aug. 1990.

  87. Schilling, R. J.: Fundamentals of Robotics, Analysis and Control, Prentice-Hall, 1990.

  88. Schwartz, J. T. and Sharir, M.: On the piano movers' problem II: General techniques for computing topological properties of real algebraic manifolds,Adv. Appl. Math., Issue 4 (1983), pp. 298–351.

    Google Scholar 

  89. Segre, A. M.: Explanation-Based Learning of Generalized Robot Assembly Plans, PhD Thesis, Dept. Elect. and Comp. Engineering, University of Illinois at Urbana Champaign, Urbana, IL, Jan. 1987.

    Google Scholar 

  90. Shafritz, J. M. and Ott, J. S. (eds): Classics of Organization Theory, Dorsey Press, 1987.

  91. Shoureshi, R., Momot, M., Mitchell, O. R., Feddema, J.: Vision-based intelligent control for automated assembly,J. Intelligent and Robotic Systems,2 (1989), 153–170.

    Google Scholar 

  92. Simunovic, S. N.:An Information Approach to Part Mating, PhD Thesis, Dept. of Mechanical Engineering, MIT, Apr. 1979.

  93. Smith, R. A.:Relative Position Sensing Using Kalman Filtering of Vision Data, M.A.Sc. Thesis, Dept. Electrical Engineering, University of Waterloo, 1989.

  94. Steiger-Garcão, A. and Camarinha-Matos, L. M.: A conceptual structure for a robot station programming system,J. Robotics,3(2) (1987), 195–204.

    Google Scholar 

  95. Steiger-Garcão, A and Camarinha-Matos, L. M.: An integrated architecture for robot cell programming and monitoring, in M. Jamshidi et al. (eds),Robotics and Manufacturing — Proc. 2nd Int. Symp. Robotics and Manufacturing: Research, Education and Application, Albuqerque, New Mexico, Nov. 1988.

  96. Swartout, W. (ed.): Workshop report: DARPA Santa Cruz workshop of planning,AI Magazine (Summer 1988).

  97. Tani, K., et al.: High precision manipulators with visual sense,Proc. 7th Int. Symp. Industrial Robots, SME and RIA, Oct. 1977, pp. 561–568.

  98. Taylor, P. M., Selke, K. K. W. and Taylor, G. E.: Closed loop control of an industrial robot using visual feedback from a sensory gripper,Proc. 12th Int. Symp. on Industrial Robots, Paris, France, June 1982, pp. 79–86.

  99. Tzafestas, S. G.: Integrated sensor-based intelligent robot system,IEEE Control Systems Maganize (May 1988), 61–72.

  100. Tzafestas, S. G.: Introduction to intelligent robotic systems, in S. G. Tzafestas (ed.),Intelligent Robotic Systems, Marcel, Dekker, New York, 1991.

    Google Scholar 

  101. Vaaler, E. and Seering, W.: Automated assembly in the presence of significant system errors, inProc. IEEE Int. Symp. Intelligent Control, Virginia, 1988, pp. 344–349.

  102. Valavanis, K. P. and Saridis, G. N.: Information theoretic modelling of intelligent robotic systems, Part I: The organizational level,Proc. 26th Conf. Decision and Control, CA, Dec. 1987.

  103. Valavanis, K. P. and Saridis, G. N.: A review of intelligent control based methodologies for modeling and analysis of hierarchically intelligent systems, inProc. IEEE Int. Symp. Intelligent Control, Philadelphia, PA, Sep. 1990, pp. 15–20.

  104. Van Brussel, H. and Simons, J.: Automatic assembly by active force feedback accommodation, in A. Pugh (ed.),Robot Sensors, Vol. 2, IFS/Springer, Berlin, 1986.

    Google Scholar 

  105. Vijaykumar, R. and Arbib, M. A.: Problem decomposition for assembly planning, inProc. IEEE Int. Conf. Robotics and Automation, Raleigh, NC, 1987, pp. 1361–1366.

  106. Wang, J. and Wilson, W. J.: 3D relative position and orientation estimation using Kalman filter for robot control,Proc. IEEE Int. Conf. Robotics and Automation, Nice, France, May 1992.

  107. Weiss, L. E., Sanderson, A. and Neuman, C. P.: Dynamic sensor-based control of robots with visual feedback,IEEE J. Robotics and Automation RA-3(5) (Oct. 1987), 404–417.

    Google Scholar 

  108. Westmore, D. B. and Wilson, W. J.: Direct dynamic control of a robot using an endpoint mounted camera and Kalman filter position estimation, inProc. IEEE Int. Conf. Robotics and Automation, Sacramento, CA, Apr. 1991, pp. 2376–2384.

  109. Williams, C. C.:Design of a Computer Architecture for a Relative Position Sensing Robot, M.A.Sc. Thesis, Dept. of Electrical and Computer Engineering, University of Waterloo, Waterloo, Canada, 1991.

    Google Scholar 

  110. Wilson, W. J.: Vision sensor integration for direct manipulator end-point control,Robots 12/Vision '88 Conf., Detroit, MI, June 1988.

  111. Zheng, Y. F.: Integration of multiple sensors into a robotic system and its performance evaluation,IEEE Trans. Robotics and Automation,5(5) (Oct. 1989).

  112. Zheng, J. Y., Chen, Q. and Tsuji, S.: Active camera guided manipulation, inProc. IEEE Int. Conf. Robotics and Automation, Sacramento, CA, April 1991, pp. 632–638.

  113. Zuech, N. (ed.):Handbook of Intelligent for Industrial Automation, Addison-Wesley, 1992.

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Janabi-Sharifi, F., Wilson, W.J. An intelligent assembly robotic system based on Relative Pose measurements. J Intell Robot Syst 12, 49–86 (1995). https://doi.org/10.1007/BF01258307

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01258307

Key words

Navigation