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Artificial Intelligence and Robotics

  • Michael Brady
Part of the NATO ASI Series book series (volume 11)

Abstract

Robotics is that field concerned with the connection of perception to action. Artificial Intelligence must have a central role in Robotics if the connection is to be intelligent. Artificial Intelligence addresses the crucial questions of: what knowledge is required in any aspect of thinking; how that knowledge should be represented; and how that knowledge should be used Robotics challenges AI by forcing it to deal with real objects in the real world. Techniques and representations developed for purely cognitive problems, often in toy domains, do not necessarily extend to meet the challenge.

Robots combine mechanical effectors, sensors, and computers. AI has made significant contributions to each component. We review AI contributions to perception and object oriented reasoning. Object-oriented reasoning includes reasoning about space, path-planning, uncertainty, fitting, and friction. We concluded with three examples that illustrate the kinds of reasoning or problem solving abilities we would like to endow robots with.

Keywords

Inverse Kinematic Robot Motion Robotic Research Dead Reckoning Robot Vision 
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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References

  1. Agin, G. J, [1980], “Computer vision systems for industrial inspection and assembly,” Computer, 13, 11–20.CrossRefGoogle Scholar
  2. Ambler, A.P., and R.J. Popplestone, [1975], “Inferring the positions of bodies from specified spatial relationships,” Artificial Intelligence, 6, 2, 157–174.MathSciNetzbMATHCrossRefGoogle Scholar
  3. Asada, H, [June, 1982], “A characteristics analysis of manipulator dynamics using principal transformations,” Proc. Amen Control Conf., Washington, D.C..Google Scholar
  4. Asada, H, [1983], Proc. International Symposium of Robotics Research.Google Scholar
  5. Asada, H. and T. Kanade, [Aug. 1981], “Design concept of direct-drive manipulators using rare-earth DC torque motors,” Proc. 7th Int. Joint. Conf. Artificial Intelligence, Vancouver, British Columbia, 775–778.Google Scholar
  6. Baker H. Harlyn, and Binford T. O, [1981], “Depth from edge and intensity based stereo,” Int. Jt. Conf. Artif. Intel., 6,.Google Scholar
  7. Binford T. O, [1981], “Inferring surfaces from images,” Artificial Intelligence, 17, 205–245.CrossRefGoogle Scholar
  8. Boissonat, J. -D, [1982], “Stable matching between a hand structure and an object silhouette,” IEEE Patt. Anal. and Mach. Intell., PAMI-4, 603–611.CrossRefGoogle Scholar
  9. Brady, Michael, [1982], Parts description and acquisition using vision, Robot vision Rosenfeld, A [ed]. Proc. SPIE, Washington D.C., 1–7.Google Scholar
  10. Brady, Michael, [1983a], “Parallelism in vision,” Artificial Intelligence, to appear.Google Scholar
  11. Brady, Michael, [1983b], Criteria for shape representations, Human and Machine vision, Beck J, and Rosenfeld A., eds., Academic Press.Google Scholar
  12. Brady, Michael, [1983c], Trajectory planning, Robot motion: planning and control, Brady, Michael, Hollerbach, J. M., Johnson, T. J., Lozano-Perez, T., and Mason, M. T., MIT Press,.Google Scholar
  13. Brady, Michael, [1983d], Representing shape, This volume.Google Scholar
  14. Brady, Michael, and Asada, Haruo, [1983], Smoothed local symmetries and their implementation, Proc. First Int. Symp. Robotics Research.Google Scholar
  15. Brady, Michael, and Yuille, Alan, [1983], An extremum principle for shape from contour, MIT, AI Lab., MIT-AIM 711.Google Scholar
  16. Brooks, R.A, [1981], “Symbolic Reasoning Among 3-D Models and 2D Images,” Artificial Intelligence, 17, 285–348.CrossRefGoogle Scholar
  17. Brooks, R. A, [1982], “Symbolic error analysis and robot planning,” Int. Journal of Robotics Research, 1 [4], 29–68.CrossRefGoogle Scholar
  18. Brooks, R. A, [1983a], “Solving the findpath problem by good representation of free space,” IEEE Trans. Sys. Man and Cyb., SMC-13, 190–197.MathSciNetGoogle Scholar
  19. Brooks, R. A, [1983b], “Planning collision free motions for pick and place operations,” International Journal of Robotics Research, 2 [4],.Google Scholar
  20. Brooks, R. A., and Lozano-Pérez, Tomás, [1983], A subdivision algorithm in configuration space for findpath with rotation, Proc. Int. Jt. Conf. Artif. Intell. Karlsrühe.Google Scholar
  21. Bruss A., and Horn, B. K. P, [1981], Passive Navigation, MIT, AI Memo 662.Google Scholar
  22. Bundy, Alan, et. al, [1979], Solving mechanics problems using meta-level inference, Expert systems in the microelectronic age, Michie, D. [ed.], Edinburgh Univ. Press.Google Scholar
  23. Canny, J. F, [1983 Sept], Finding lines and edges in images, Proc. AAAI Conf., Washington, DC.Google Scholar
  24. Canny, J. F, [1983], Finding lines and edges in images, MIT.Google Scholar
  25. Clocksin, W. E., et al, [1982], Progress in visual feedback for arc-welding of thin sheet steel, Robot Vision, Pugh, Alan ed., IFS.Google Scholar
  26. Davis, Larry S. and Rosenfeld Azriel, [1981], “Cooperating processes for low-level vision: a survey,” Artificial Intelligence, 17, 245–265.CrossRefGoogle Scholar
  27. DeKleer, J, [1975], Qualitative and quantitative knowledge in classical mechanics, MIT Artificial Intelligence Laboratory, AI-TR-352.Google Scholar
  28. Faugeras, O. et. al, [1982], Towards a flexible vision system, Robot Vision, Pugh, Alan ed., IFS.Google Scholar
  29. Featherstone, R, [1983], “Position and velocity transformations between robot end effector coordinates and joint angles,” The International Journal of Robotics Research, 2[2],.Google Scholar
  30. Forbus, K. D, [1983], Qualitative process theory, MIT Artificial Intelligence Laboratory AIM-664A.Google Scholar
  31. Franklin, James W., and VanderBrug, G. J, [March, 1982], Programming vision and robotics system with RAIL, Robots VI Conf., Detroit, SME.Google Scholar
  32. Gaston, Peter C, and Lozano-Pérez, Tomás, [1983], Tactile recognition and localization using object models: the case of polyhedra on a plane, MIT Artificial Intelligence Lab. AIM-705.Google Scholar
  33. Goto, T., K. Takeyasu, and T. Inoyama, [1980], “Control algorithm for precision insert operation robots,” IEEE Trans. Systems, Man, Cybernetics, SMC-10, 1, 19–25.Google Scholar
  34. Grimson, W. E. L, [1981], From images to surfaces: a computational study of the human early visual system, MIT Press, Cambridge.Google Scholar
  35. Hackwood, S., and Beni, [1983], “Torque sensitive tactile array for robotics,” Int. Jour. Robotics Research, 2 [2],.Google Scholar
  36. Haralick, Robert M., Watson, Layne T., and Laffcy, Thomas J, [1983], “The topographic primal sketch,” The International Journal of Robotics Research, 2 [1], 50–72.CrossRefGoogle Scholar
  37. Harmon L, [1982], “Automated Tactile Sensing,” Int. Jour. Robotics Research, 1 [2], 3–33.CrossRefGoogle Scholar
  38. Hildreth, E, [1983], The measurement of visual motion, MIT. Artificial Intelligence Laboratory.Google Scholar
  39. Hillis, W. Daniel, [1982], “A high-resolution image touch sensor,” Int. Jour. Robotics Research, 1 [2], 33–44.CrossRefGoogle Scholar
  40. Hollerbach, J. M, [1983], Dynamics, Robot motion: planning and control, Brady, Michael, Hollerbach, J. M., Johnson, T. J., Lozano-Perez, T., and Mason, M. T., MIT Press,.Google Scholar
  41. Hollerbach, J. M., and Sahar, Gideon, [1983], Wrist partitioned inverse kinematic accelerations and manipulator dynamics, MIT Artificial Intelligence Laboratory, AIM-717.Google Scholar
  42. Horn B. K. P, [1982], Sequins and Quills — Representations for Surface Topography, Representation of 3-Dimensional Objects ed. Bajcsy R., Springer Verlag.Google Scholar
  43. Hopcroft, J. E., Schwartz, J. T., and Sharir M, [1983], “Efficient detection of intersections among spheres,” The International Journal of Robotics Research, 2 [4].Google Scholar
  44. Horn and Schunck, [1982], “Determining Optical Flow,” Artificial Intelligence, 17, 185–203.CrossRefGoogle Scholar
  45. Ikeuchi K, [1981], “Determination of surface orientations of specular surfaces by using the photometric stereo method,” IEEE [accepted for publication],,.Google Scholar
  46. Ikeuchi K. and Horn B. K. P, [1981], “Numerical shape from shading and occluding boundaries,” Artificial Intelligence, 17, 141–185.CrossRefGoogle Scholar
  47. Ikeuchi, K., and Horn, B. K. P, [1983],, Proc. First Int. Symp. Robotics Research.Google Scholar
  48. Lieberman, L.I., and M.A. Wesley, [1977], “AUTOPASS: an automatic programming system for computer controlled mechanical assembly,” IBM J. Research Development, 21, 4, 321–333.CrossRefGoogle Scholar
  49. Lowe, D. G., and Binford, T. O, [1982], Segmentation and aggregation: an approach to figure-ground phenomena, Proc. Image Understanding Workshop, Baumann Lee S. [ed.], Sci. App. Inc. Tysons Corner Va., 168–178.Google Scholar
  50. Lozano-Perez, T, [1976], The design of a mechanical assembly system, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, AI TR 397.Google Scholar
  51. Lozano-Pérez, Tomás I, [1981], “Automatic planning of manipulator transfer movements,” IEEE Trans. Sys., Man and Cyb., SMC-11, 681–698.CrossRefGoogle Scholar
  52. Lozano-Pérez, Tomás I, [1983a], “Spatial planning: a configuration space approach,” IEEE Trans. Comp., C-32, 108–120.CrossRefGoogle Scholar
  53. Lozano-Pérez, Tomás, [1983b], Robot programming, MIT Artificial Intelligence Laboratory, AIM-698.Google Scholar
  54. Lozano-Pérez, Tomás, Mason, Matthew T., and Taylor, R. H, [1983c], Automatic Synthesis of fine-motion strategies for robots, Proc. International Symposium of Robotics Research.Google Scholar
  55. Lozano-Pérez, Tomás, [1983d], Spatial Reasoning, Robot motion: planning and control, Brady, Michael, Hollerbach, J. M., Johnson, T. J., Lozano-Perez, T., and Mason, M. T., MIT Press,Google Scholar
  56. Lozano-Perez, T. L., and Crimson, W. E. L, [1983], Local constraints in tactile recognition, MIT Artificial Intelligence Laboratory.Google Scholar
  57. Lozano-Perez, T. L., Mason, T. M., and Taylor, R. H, [1983],, Proc. First Int. Symp. Robotics Research.Google Scholar
  58. Marr, D, [1982], Vision, Freeman, San Francisco.Google Scholar
  59. Marr, D. and Hildreth, E.C, [1980], “Theory of Edge Detection,” Proc. R. Soc. Lond. B, 270, 187–217.CrossRefGoogle Scholar
  60. Marr D. and Poggio T, [1979], “A theory of human stereo vision,” Proc. R. Soc. Lond. B, 204, 301–328.CrossRefGoogle Scholar
  61. Mason, T. M, [reprinted in Robot motion: planning and control Brady, Michael, Hollerbach, J. M., Johnson, T. J., Lozano-Perez, T., and Mason, M. T., MIT Press] [1981], “Compliance and force control for computer controlled manipulators,” IEEE Trans. Sys. Man and Cyb., SMC-11, 418–432.CrossRefGoogle Scholar
  62. Mason, T. M, [1983], Compliance, Robot motion: planning and control, Brady, Michael, Hollerbach, J. M., Johnson, T. J., Lozano-Perez, T., and Mason, M. T., MIT Press,.Google Scholar
  63. Paul, R.P, [1981], Robot Manipulators: Mathematics, Programming, and Control, MIT Press, Cambridge, Mass..Google Scholar
  64. Pieper, D.L, [1968], The Kinematics of Manipulators under Computer Control, Ph.D. thesis, department of Computer Science, Stanford University.Google Scholar
  65. Pieper, D.L., and B. Roth, [September 1969], “The kinematics of manipulators under computer control,” Proc. 2nd Int. Conf. Theory of Machines and Mechanisms, Warsaw.Google Scholar
  66. Popplestone, R. J., Ambler, A. P., and Bellos, I. M, [1980], “An interpreter for a language for describing assemblies,” Artificial Intelligence, 14, 79–107.CrossRefGoogle Scholar
  67. Porter, G., and Mundy, J, [1982], “A non-contact profile sensor system for visual inspections,” IEEE Workshop on Ind. Appl. of Mach. Vis.,,.Google Scholar
  68. Raibert, M. H., and Craig, J. J, [1983], A hybrid force and position controller, Robot motion: planning and control, Brady, Michael, Hollerbach, J. M., Johnson, T. J., Lozano-Perez, T., and Mason, M. T., MIT Press,.Google Scholar
  69. Raibert, Marc H., and Tanner, John E, [1982], “Design and implementation of a VLSI tactile sensing computer,” Int. Jour. Robotics Research, 1 [3], 3–18.CrossRefGoogle Scholar
  70. Requicha, A. A. G, [December, 1980], “Representation of Rigid Solids: Theory, Methods, and Systems,” Computing Surveys, 12, 4, 437–464.CrossRefGoogle Scholar
  71. Rich, C. R., and Waters, R, [1981], Abstraction, inspection, and debugging in programming, MIT Artificial Intelligence Laboratory, AIM-634.Google Scholar
  72. Sacerdoti, E, [1975], A structure for plans and behavior, SRI Artificial Intelligence Center TR-109.Google Scholar
  73. Salisbury, J.K, [1982], Kinematic and Force Analysis of Articulated Hands, Ph.D. thesis, department of Mechanical Engineering, Stanford University.Google Scholar
  74. Salisbury, J.K., and J.J. Craig, [1982], “Articulated hands: force control and kinematic issues,” Int. J. Robotics Research, 1, 1, 4–17.CrossRefGoogle Scholar
  75. Schunck, B. G, [1983], Motion segmentation and estimation, MIT Artificial Intelligence Laboratory.Google Scholar
  76. Schwartz, Jacob T., and Sharir, Micha, [1983], “The piano movers problem III,” The International Journal of Robotics Research, 2 [3].Google Scholar
  77. Taylor, R.H, [July, 1976], The synthesis of manipulator control programs from task-level specifications, Artificial Intelligence Laboratory, Stanford University, AIM-282.Google Scholar
  78. Taylor, R. H., Summers, P. D., and Meyer J. M, [1982], “AML: a manufacturing language,” The International Journal of Robotics Research, 1[3], 19–41.CrossRefGoogle Scholar
  79. Terzopoulos, D, [1983], “Multi-level reconstruction of visual surfaces,” Computer Graphics and Image Processing.Google Scholar
  80. VAL, [1980], User’s guide: a robot programming and control system, CONDEC Unimation Robotics.Google Scholar
  81. Villers, Philippe, [1982], Present industrial use of vision sensors for robot guidance, Robot Vision, Pugh, Alan ed., IFS.Google Scholar
  82. Vilnrotter F., Nevatia R., and Price K. E, [1981], Structural analysis of natural textures, Proc. Image Understanding Workshop ed. Lee Baumann S., 61–68.Google Scholar
  83. Wesley, M. A. et al, [January, 1980], “A Geometric Modeling System for Automated Mechanical Assembly,” IBM J. Research and Development, 24, 1, 64–74.MathSciNetCrossRefGoogle Scholar
  84. Whitney, D. E, [1983], The mathematics of compliance, Robot motion: planning and control, Brady, Michael, Hollerbach, J. M., Johnson, T. J., Lozano-Perez, T., and Mason, M. T., MIT Press,Google Scholar
  85. Winston, Patrick H, [1983], Artificial Intelligence, Second Edition, Addison Wesley, Reading: Mass..Google Scholar
  86. Winston, Patrick H., Binford, Thomas O., Katz, Boris, and Lowry, Michael, [1983], Learning physical descriptions from functional descriptions, examples, and precedents, MIT Artificial Intelligence Laboratory, AIM-679.Google Scholar
  87. Witkin, Andrew P, [1981], “Recovering surface shape and orientation from texture,” Artificial Intelligence, 17, 17–47.CrossRefGoogle Scholar
  88. Zucker S. W., Hummel R. A., and Rosenfeld Azriel, [1977], “An application of relaxation labelling to line and curve enhancement,” IEEE Trans. Computers, C-26, 394–403, 922–929.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1984

Authors and Affiliations

  • Michael Brady
    • 1
  1. 1.MIT Artificial Intelligence LaboratoryCambridgeUSA

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