Neuroscience in Motion: The Application of Schema Theory to Mobile Robotics

  • Ronald C. Arkin


Theories for path planning behavior in animals can be of great value in providing significant insights into the design of functioning mobile robot systems. A mobile robot path execution system has been developed that strongly correlates with a model for detour behavior in the frog. The robot’s motor schema based navigation system draws on potential field methodology to produce “intelligent” behavior based on environmental perception. Both simulation and actual experimental results are presented.


Mobile Robot Path Planning Motor Schema House Model Mobile Robot Navigation 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Arbib M (1981) Perceptual structures and distributed motor control. In: Brooks R (ed) Handbook of physiology. The nervous system IL American Physiological Society, Bethesda MD, pp 1449–1465Google Scholar
  2. Arbib M, House D (1985) Depth and detours: an essay on visually guided behavior. COINS Tech Rep 85–20Google Scholar
  3. Arkin R (1986) Path planning for a vision-based mobile robot. COINS Tech Rep 86–48Google Scholar
  4. Arkin R (1987a) Motor schema based navigation for a mobile robot: an approach to programming by behavior. Proc IEEE Int Conf on Robotics and Automation, pp 264–271Google Scholar
  5. Arkin R (1987b) Reactive/reflexive navigation for an autonomous vehicle. Proc of the American Institute for Aeronautics and Astronautics Computers in Aerospace VI Conference, Wakefield MA, pp 298–306Google Scholar
  6. Arkin R (1987c) Towards cosmopolitan robots: intelligent navigation in extended man-made environment and PhD Dissertation, Dept of Computer and Information Science, Univ of Mass, Amherst (see also COINS Tech Rep 87: 80 )Google Scholar
  7. Barto A, Anderson C, Sutton R (1982) Synthesis of nonlinear control surfaces by a layered associative search network. Biol Cybern 43: 175–185PubMedCrossRefGoogle Scholar
  8. Brooks R (1986a) Achieving artificial intelligence through building robots. MITA I Lab Memo 899Google Scholar
  9. Brooks R (1986b) A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation 2 (1): 14–23CrossRefGoogle Scholar
  10. Collett T (1983) Picking a route: do toads follow rules or make plans? In: Ewert J-P, Capranica RR, Ingle DJ (eds) Advances in vertebrate neuroethology. Plenum Press, New York, pp 321–330CrossRefGoogle Scholar
  11. Craik KJW (1943) The nature of explanation. Cambridge Univ Press, LondonGoogle Scholar
  12. Krogh B, Thorpe C (1986) Integrated path planning and dynamic steering control for autonomous vehicles. Proc IEEE Int Cont on Robotics and Automation, pp 1664–1669Google Scholar
  13. Lock A, Collett T (1979) A toad’s devious approach to its prey: a study of some complex uses of depth vision. J Comp Physiol 131: 179–189CrossRefGoogle Scholar
  14. Lyons D (1986) RS: a formal model of distributed computation for sensory-based robot control PhD Dissertation, Dept of Computer and Information Science, Univ of Mass, Amherst (see also COINS Tech Rep 86–43)Google Scholar
  15. Mittelstaedt H (1985) Analytical cybernetics of spider navigation. In: Barth FG (ed) Neurobiology of arachnids. Springer-Verlag, Berlin Heidelberg New York, pp 298–316CrossRefGoogle Scholar
  16. Mittelstaedt M, Mittelstaedt H, Mohren W (1979) Interaction of gravity and idiothetic course control in millipedes. J Comp Physiol 133: 267–281CrossRefGoogle Scholar
  17. Neisser U (1976) Cognition and reality: principles and implications of cognitive psychology. Freeman, San FranciscoGoogle Scholar
  18. Overton K (1984) The acquisition, processing, and use of tactile sensor data in robot control. PhD Dissertation, Dept of Computer and Information Science, Univ of Mass, Amherst (see also COINS Tech Rep 84–08)Google Scholar
  19. Piaget J (1971) Biology of knowledge. Edinburgh Univ Pess, EdinburghGoogle Scholar
  20. Webster M (1984) Webster’s ninth new collegiate dictionary. Merriam-Webster, Springfield, MAGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1989

Authors and Affiliations

  • Ronald C. Arkin
    • 1
  1. 1.Department of Information and Computer ScienceGeorgia Institute of TechnologyAtlantaUSA

Personalised recommendations