Coordinating Multiple Robot Arms to Increase Productivity

  • John Roach
  • Paul Montague


Robotics research has mainly concentrated on low-level kinematics, dynamics, and path control problems. Intelligent decision making will also be required for truly effective robots. Planning for tasks requiring multiple arm coordination, including human/robot cooperative efforts, is becoming increasingly important. This paper reports progress toward a theory and implementation of collaborative action. An execution environment for coordinated parallel action is also presented.


Plan Graph Parallel Action Virginia Tech Robot Operating System Goal Context 
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]
    R. G. Barker and H. F. Wright, The Midwest and Its Children, Row, Peterson and Co., Evanston, IL, 1954.CrossRefGoogle Scholar
  2. [2]
    J. deKleer, “Causal and ieleological reasoning in circuit recognition,” Artificial Intelligence Laboratory, Technical Report 529, M.I.T., Cambridge, MA, 1979.Google Scholar
  3. [3]
    E. Dijkstra, “Cooperating sequential processes,” Technical Report EWD-123, Technological University, Eindhoven, The Netherlands, 1965.Google Scholar
  4. [4]
    J. Dreussi, “The detection and correction of errors in problem-solving systems,” Ph.D. dissertation, Dept. of Computer Science, University of Texas, Austin, TX, 1982.Google Scholar
  5. [5]
    R. Fikes and N. Nilsson, “STRIPS: A new approach to the application of theorem proving to problem solving,” Artificial Intelligence Journal, vol. 2, no. 3–4, 189–208, Winter 1971.MATHGoogle Scholar
  6. [6]
    R. Filman and D. Friedman, Coordinated Computing, McGraw Hill, New York, 1984.Google Scholar
  7. [7]
    D. Gentner and A. Stevens, Mental Models, Lawrence Erlbaum Associates, Hillsdale, NJ, 1983.Google Scholar
  8. [8]
    P. Hayes, “The Naive Physics Manifesto,” in Expert Systems in the Micro Electronics Age, Donald Michie, ed., Edinburgh University Press, Edinburgh, Scotland, 1979.Google Scholar
  9. [9]
    J. Roach and T. Dean, “Intuitive physics for robots,” in preparation.Google Scholar
  10. [10]
    J. Roach and G. Fowler, “The HC Manual: Virginia Tech Prolog,” Technical Report, Dept. of Computer Science, Virginia Polytechnic Institute and State University, 1983.Google Scholar
  11. [11]
    E. Sacerdoti, A Structure for Plans and Behavior, Elsevier, New York, 1977.MATHGoogle Scholar
  12. [12]
    L. Siklossy and J. Dreussi, “An efficient robot planner which generates its own procedures,” Proc. 3IJCAI, Stanford, CA, 423–420, 1973. Reprinted in Tutorial on Robotics, C. Lee, R. Gonzalez, and K. S. Fu, eds., Silver Spring, MD: IEEE Computer Society Press, 1983.Google Scholar
  13. [13]
    G. Sussman, “A computational model of skill acquisition,” Ph.D. dissertation, M.I.T., Cambridge, MA, August 1973, AI-TR-297.Google Scholar
  14. [14]
    A. Tate, “INTERPLAN: A plan generation system that can deal with interactions between goals,” Memo MIP-R-109, Machine Intelligence Research Unit, University of Edinburgh, 1974.Google Scholar
  15. [15]
    S. Vere, “Planning in Time: Windows and durations for activities and goals,” IEEE PAMI, vol. PAMI-5, no. 3, 246–266, May 1983.Google Scholar
  16. [16]
    D. Warren, “WARPLAN: A system for generating plans,” Memo No. 76, Dept. of Computational Logic, School of Artificial Intelligence, University of Edinburgh, 1974.Google Scholar

Copyright information

© Plenum Press, New York 1985

Authors and Affiliations

  • John Roach
    • 1
  • Paul Montague
    • 1
  1. 1.Department of Computer ScienceVirginia Polytechnic Institute and State UniversityBlacksburgUSA

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