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The future of robot programming*

Published online by Cambridge University Press:  09 March 2009

Maria Gini
Maria Gini
Affiliation:
Computer Science Department, University of Minnesota, Minneapolis MN, (USA)
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Summary

This paper presents current trends in robot programming. The open problems with current robot programming systems are outlined and indications for solutions are given. Since computer controlled robots have been introduced, the methodology of robot programming has seen a great deal of development. Two completely different approaches to robot programming have been considered in the past. On the one hand within the Artificial Intelligence community a lot of research has been done to provide robots with autonomous reasoning capabilities. On the other hand, the need to control industrial robots has pushed the development of simple but effective methods for robot programming. To put it simply, Artificial Intelligence researchers have taken a top-down approach trying to solve the difficult problem of reasoning and have assumed that all the rest was easy. Others have taken a bottom-up approach first trying to control robots and only later trying to incorporate intelligence. The complexity of industrial automation tasks requires programming systems more sophisticated that those in use today. Artificial Intelligence is the best candidate to create the next generation of robot programming systems.

Keywords

ProgrammingRobotFutureArtificial Intelligence

Information

Type
Article
Information
Robotica , Volume 5 , Issue 3 , July 1987 , pp. 235 - 246
Copyright
Copyright © Cambridge University Press 1987

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References

1.Smith, D.H. and Wilson, R.C., Industrial Robots: a Delphi forecast of markets and technology (SME, Dearborn, MI, USA, 1982).Google Scholar
2.Popplestone, R.J., “An interpreter for a language for describing assemblies” Artificial Intelligence 14, 79–107 (1980).CrossRefGoogle Scholar
3.Lozano-Perez, T. and Wesley, M.A., “An algorithm for planning collision free paths among polyhedral obstacles” Comm. of the ACM 22, 560–570 (1979).CrossRefGoogle Scholar
4.Lozano-Perez, T., “Automatic planning of manipulator transfer movements” IEEE Trans. on Systems, Man, and Cybernetics SMC-11, 681–698 (1981).Google Scholar
5.Udupa, S., “Collision detection and avoidance in computer controlled manipulators” 5th International Joint Conf. on Artificial Intelligence (Boston, Mass., 1977) pp. 737–748.Google Scholar
6.Donald, B.R., “On motion planning with six degrees of freedom: solving the intersection problems in configuration space” IEEE International Conf. on Robotics and Automation (St. Louis, MO, 1985) pp. 536–541.Google Scholar
7.Chien, R.T., Zhang, L. and Zhang, B., “Planning collision-free paths for a robot arm among obstacles” IEEE Trans. on Pattern Analysis and Machine Intelligence PAMI-6, 91–96 (1984).CrossRefGoogle ScholarPubMed
8.Brooks, R.A., “Planning collision free motions for pick and place operations” Robotics Research 2, 19–44 (1983); id., “Solving the find-path problem by good representation of free space” IEEE Trans. Systems, Man, and Cybernetics SMC-13, 190–197 (1983).CrossRefGoogle Scholar
9.Khatib, O., “Real time obstacle avoidance for manipulators and mobile robots” IEEE Intern. Conf. on Robotics and Automation (St. Louis, MO, USA, 1985) pp. 500–505.Google Scholar
10.Hogan, N., “Mechanical impedance control in assistive devices and manipulators” JACC (San Francisco, CA, USA, 1980).Google Scholar
11.Laugier, C., “A program for automatic grasping of objects with a robot arm” 11th Intern. Symp. on Industrial Robots“ (Tokyo, Japan, 1981).Google Scholar
12.Wingham, M., “Planning to grasp objects in a cluttered environment” M.Phil. Thesis (University of Edinburgh, U.K., 1977).Google Scholar
13.Lozano-Perez, T., Mason, M.T. and Taylor, R.H., “Automatic synthesis of fine-motion strategies for robots” Robotics Research 3, 3–24 (1984).CrossRefGoogle Scholar
14.Salisbury, J.K., “Active stiffness control of a manipulator in Cartesian coordinates” 19th Conf. on Decision and Control (Albuquerque, NM, USA, 1980).CrossRefGoogle Scholar
15.van Brussels, H. and Simons, J., “The adaptable or compliance concept and its use for automatic assembly by active force feedback accommodations” 9th Intern. Symp.on Industrial Robots (Washington, DC, 1979) pp. 167–181.Google Scholar
16.Mason, M.T., “Compliance and force control for computer controlled manipulators” IEEE Trans. on Systems, Man, and Cybernetics SMC-11, 418–432 (1981).Google Scholar
17.Dufay, B. and Latombe, J.C., “An approach to automatic robot programming based on inductive learning” In: (Brady, M. and Paul, R., eds.) Robotics Research (MIT Press, Boston, Mass., USA, 1983).Google Scholar
18.Brooks, R.A., “Symbolic error analysis and robot planning” Robotics Research” 1, 29–68 (1982).CrossRefGoogle Scholar
19.Sacerdoti, E., A Structure for Plans and Behavior (Amer. Elsevier Publ. Co., 1977).Google Scholar
20.Sussman, G.J., A Computer Model of Skill Acquisition (Amer. Elsevier Publ. Co, 1975).Google Scholar
21.Fikes, T.E., “Monitored execution of robot plans produced by STRIPS” IFIP 71, 101–105 (1971).Google Scholar
22.Srinivas, S., “Error recovery in a robot system” PhD Thesis, CIT (1976).Google Scholar
23.Friedman, L., “Robot learning and error correction” 5th Intern. Joint Conf. on Artificial Intelligence (Chicago, Ill., USA, 1975) pp. 339–358.Google Scholar
24.Wilkins, D.E., “Domain-independent planning: representation and plan generation” Artificial Intelligence 22, 269–301 (1984).CrossRefGoogle Scholar
25.Gini, M. and Gini, G., “Towards automatic error recovery in robot programs” Intern. Joint Conf. on Artificial Intelligence 83 (Karlsruhe, West Germany, 1983) pp. 821–823.Google Scholar
26.Gini, M. and Gini, G., “Recovery from failures: a new challenge to industrial robotics” 26th IEEE Computer Society Intern. Conf. COMPCON 83 (Washington, D.C., 1983) pp. 220–227.Google Scholar
27.Gini, M. et al. , “The role of knowledge in the architecture of a robust robot control” IEEE Intern. Conf. on Robotics and Automation (St. Louis, MO, USA, 1985) pp. 561–567.Google Scholar