Skip to main content
SpringerLink
Log in

Neural networks in robotics: A survey

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

Abstract

The purpose of this paper is to provide an overview of the research being done in neural network approaches to robotics, outline the strengths and weaknesses of current approaches, and predict future trends in this area.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Albus, J., A new approach to manipulator control: the Cerebellar Model Articulation Controller (CMAC), J. Dynamic Systems, Measurement, and Control, 220–227 (Sept. 197).

  2. Albus, J., Data storage in the Cerebellar Model Articulation Controller (CMAC), J. Dynamic Systems, Measurement, and Control, 228–233 (Sept. 1975).

  3. AlbusJ., Mechanisms of planning and problem solving in the Brain, Mathematical Biosciences 45, 247–293 (1979).

    Google Scholar 

  4. AmariS. and ArbibM., Competition and cooperation in neural nets, In Systems Neuroscience, J.Metzler (ed.), Academic Press, New York, pp. 119–165 (1977).

    Google Scholar 

  5. Anderson, C., Learning to control an inverted pendulum with connectionist networks, IEEE Conf. on Decision and Control, pp. 2294–2298 (1987).

  6. BartoA. et al., Neuronlike adaptive elements that can solve difficult learning control problems, IEEE Trans. Systems, Man, and Cybernetics, Vol. 13, pp. 834–846 (1983).

    Google Scholar 

  7. BullockD. and GrossbergS., Neural dynamics of planned arm movements: emergent invariants and speed-accuracy properties during trajectory formation, Psychological Review, Vol. 95, No. 1, pp. 49–90 (1988).

    Google Scholar 

  8. EckmillerR., Neural network mechanisms for generation and learning of motor programs, IEEE Conf. on Neural Networks, Vol. 4, pp. 545–550 (1987).

    Google Scholar 

  9. ElsleyR., A learning architecture for control based on back-propagation neural networks, IEEE Conf. on Neural Networks, Vol. II, pp. 584–587 (1988).

    Google Scholar 

  10. GuezA. and AhmadZ., Solution to the inverse kinematics problem in robotics by neural networks, IEEE Conf. on Neural Networks, Vol. II, pp. 617–624 (1988).

    Google Scholar 

  11. GuezA. and SelinskyJ., A trainable neuromorphic controller, J. Robotic Systems 5(4), 363–388 (1988).

    Google Scholar 

  12. GuezA. and SelinskyJ., A neuromorphic controller with a human teacher, IEEE Conf. on Neural Networks, Vol. II, pp. 595–602 (1988).

    Google Scholar 

  13. GuezA. et al., Neuromorphic architecture for adaptive robot control: a preliminary analysis, IEEE Conf. on Neural Networks, Vol. 4, pp. 567–572 (1987).

    Google Scholar 

  14. Guez, A. et al., Neuromorphic architectures for fast adaptive robot control, IEEE Conf. on Robotics and Automation, pp. 145–149 (1988).

  15. GuezA. et al., Neural network architecture for control, IEEE Control Systems Magazine, Vol. 8, No. 2, pp. 22–25 (April 1988).

    Google Scholar 

  16. HoganN., Impedance control: an approach to manipulation, J. Dynamic Systems, Measurement, and Control, 107, 1–24 (March 1985).

    Google Scholar 

  17. Horne, W.-In an unpublished result we have demonstrated that it is possible to implement a limited class of multi-modal and non-convex decision regions using a two-layered network, thus countering Lippmann's arguments. For more information contact the author.

  18. IberallT., A ballpark approach to modelling human prehension, IEEE Conf. on Neural Networks, Vol. 4, pp. 535–544 (1987).

    Google Scholar 

  19. Iberall, T., A neural network for planning hand shapes in human prehension, IEEE Conf. on Decision and Control, pp. 2288–2293 (1987).

  20. JorgensonC.C., Neural network representation of sensor graphs in autonomous robot path planning, IEEE Conf. on Neural Networks, Vol. 4, pp. 507–516 (1987).

    Google Scholar 

  21. KawatoM. et al., A hierarchical model for voluntary movement and its application to robotics, IEEE Conf. on Neural Networks, Vol. 4, pp. 573–582 (1987).

    Google Scholar 

  22. KawatoM. et al., Hierarchical neural network model for voluntary movement with application to robotics, IEEE Control Systems Magazine, Vol. 8, No. 2, pp. 8–16 (April 1988).

    Google Scholar 

  23. Kuperstein, M., Generalized neural model for adaptive sensory-motor control of single postures, IEEE Conf. on Robotics and Automation, pp. 140–144 (1988).

  24. Lippmann, R., An introduction to computing with neural nets, IEEE ASSP Magazine, pp. 4–22 (April 1987).

  25. LiuH. et al., Building a generic architecture for robot hand control, IEEE Conf. on Neural Networks, Vol. II, pp. 567–574 (1988).

    Google Scholar 

  26. MarkiewiczB., Analysis of the computed torque drive method and comparison with conventional position servo for a computer-controlled manipulator, Technical Memo 33-601, Jet Propulsion Laboratory, Pasadena, CA (1973).

    Google Scholar 

  27. MarrianC. and PeckerarM., Electronic neural net algorithm for maximum entropy deconvolution, IEEE Conf. on Neural Networks, Vol. III, pp. 749–758 (1987).

    Google Scholar 

  28. Mel, B., MURPHY: a robot that learns by doing, Proc. AIP Neural Networks Conf., Denver, pp. 544–553 (Nov. 1987).

  29. MillerW.T. et al., Application of a general learning algorithm to the control of robotic manipulators, Int. J. Robotics Research, 6, No. 2, 84–98 (1987).

    Google Scholar 

  30. MiyamotoH. et al., Feedback-error-learning neural network for trajectory control of a robotic manipulator, Neural Networks, 1, No. 3, 251–265 (1988).

    Google Scholar 

  31. PaoY. and SobajicD., Artificial neural-net based intelligent robotics control, Proc. SPIE-Intelligent Robots and Computer Vision, Vol. 848, pp. 542–549 (1987).

    Google Scholar 

  32. Pati, Y. et al., Neural networks for tactile perception, IEEE Conf. on Robotics and Automation, pp. 134–139 (1988).

  33. PsaltisD. et al., Neural controllers, IEEE Conf. on Neural Networks, Vol. 4, pp. 551–558 (1987).

    Google Scholar 

  34. PsaltisD. et al., A multilayered neural network controller, IEEE Control Systems Magazine, Vol. 8, No. 2, pp. 17–21 (1988).

    Google Scholar 

  35. Raibert, M., Analytical equations vs table look-up for manipulation: a unifying concept, IEEE Conf. on Decision and Control, pp. 576–579 (1977).

  36. RaibertM. and CraigJ., Hybrid position/force control of manipulators, J. Dynamic Systems, Measurement, and Control, 102, 126–133 (June 1981).

    Google Scholar 

  37. RosenblattF., Principles of Neurodynamics, Spartan, New York (1962).

    Google Scholar 

  38. RumelhartD. et al., Parallel Distributed Processing: Explorations in the Microstructure of Cognition, MIT Press, Cambridge (1986).

    Google Scholar 

  39. Seshadri, V., A neural network architecture for robot path planning, Proc. Second International Symp. on Robotics and Manufacturing: Research, Foundation, and Applications, ASME Press, pp. 249–256 (1988).

  40. ShepanskiJ. and MacyS., Teaching artificial neural systems to drive: manual training techniques for autonomous systems, Proc. SPIE-Intelligent Robots and Computer Vision, Vol. 848, pp. 286–293 (1987).

    Google Scholar 

  41. Shepanski, J. and Macy, S., Teaching artificial neural systems to drive: manual training techniques for autonomous systems, Proc. AIP Neural Networks Conf., Denver, pp. 693–700 (Nov. 1987).

  42. SmithF., A trainable nonlinear function generator, IEEE Trans. Automatic Control AC-11, No. 2, 212–218 (1966).

    Google Scholar 

  43. SobajicD. et al., Intelligent control of the Intelledex 605% robot manipulator, IEEE Int. Conf. on Neural Networks, Vol. II, pp. 633–640 (1988).

    Google Scholar 

  44. TankD. and HopfieldJ., ‘Neural’ computation of decisions in optimization problems, Biological Cybernetics 52, 141–152 (1985).

    Google Scholar 

  45. TolatV. and WidrowB., An adaptive ‘broom balancer’ with visual inputs, IEEE Int. Conf. on Neural Networks, Vol. II, pp. 641–647 (1988).

    Google Scholar 

  46. TsutsumiK. and MatsumotoH., Neural computation and learning strategy for manipulator position control, IEEE Conf. on Neural Networks, Vol. 4, pp. 525–534 (1987).

    Google Scholar 

  47. TsutsumiK. et al., Neural computation for controlling the configuration of 2-dimensional truss structure, IEEE Conf. on Neural Networks, Vol. II, pp. 575–586 (1988).

    Google Scholar 

  48. WhitneyD., Resolved motion rate control of manipulators and human prostheses, IEEE Trans. Man-Machine Systems MMS-10, No. 2, 47–53 (1969).

    Google Scholar 

  49. Whitney, D., Force feedback control of manipulator fine motions, J. Dynamic Systems, Measurement, and Control, pp. 91–97 (June 1977).

  50. Widrow, B. and Smith, F., Pattern recognizing control systems, Computer and Information Sciences (COINS) Symposium Proc, Spartan Books, Wash. DC (1963).

  51. Widrow, B., The original adaptive neural net broom-balancer, IEEE Conf. on Circuits and Systems, pp. 351–357 (1987).

  52. WilsonG. and PawleyG., On the stability of the travelling salesman problem of Hopfield and Tank, Biological Cybernetics 58, 63–70 (1988).

    Google Scholar 

  53. Yueng, D. and Bekey, G., Adaptive load balancing between mobile robots through learning in an artificial neural system, IEEE Conf. on Decision and Control, pp. 2299–2304 (1987).

Download references

Author information

Authors and Affiliations

  1. CAD Laboratory for Systems/Robotics, Department of Electrical and Computer Engineering, University of New Mexico, 87131, Albuquerque, NM, USA

    Bill Horne, M. Jamshidi & Nader Vadiee

Authors
  1. Bill Horne
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Jamshidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nader Vadiee
    View author publications

    You can also search for this author in PubMed Google Scholar

Additional information

This work was supported, in part, by Sandia National Laboratories under contract No. 06-1977, Albuquerque, New Mexico.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Horne, B., Jamshidi, M. & Vadiee, N. Neural networks in robotics: A survey. Journal of Intelligent and Robotic Systems 3, 51–66 (1990). https://doi.org/10.1007/BF00368972

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF00368972

Key words

Search

Navigation