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Self-organizing multi-resolution grid for motion planning and control

T Fomin1, T Rozgonyi, C Szepesvári

  • 1Department of Photophysics, Institute of Isotopes, Hungarian Academy of Sciences, Budapest, Hungary.

International Journal of Neural Systems
|December 1, 1996
PubMed
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This study presents a self-organizing neural network for simultaneous object control and path planning. The novel Multigrid Position-and-Direction-to-Action (MPDA) map enables efficient, integrated reactive path planning and motion control.

Area of Science:

  • Artificial Intelligence
  • Computational Neuroscience
  • Robotics

Background:

  • Traditional control systems often struggle with simultaneous path planning and object manipulation.
  • Existing methods may lack efficiency in dynamic or complex environments.

Purpose of the Study:

  • To develop a novel, fully self-organizing neural network for integrated low-dimensional control and path planning.
  • To enhance control efficiency by simulating diffusion processes on a multigrid structure.

Main Methods:

  • A self-organizing multigrid discretizing structure was introduced to represent the environment.
  • Diffusion processes were simulated within a recurrent neural network on this multigrid system.
  • The Multigrid Position-and-Direction-to-Action (MPDA) map was developed, integrating interneurons for control command generation.

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Main Results:

  • The proposed multigrid diffusion simulation demonstrated fast diffusion, adapting resolution to free space and bottlenecks.
  • The MPDA map effectively integrated reactive path planning with continuous motion control.
  • Population coding was observed for the command vector, indicating efficient information representation.

Conclusions:

  • The novel neural network architecture offers an efficient and integrated solution for combined control and path planning tasks.
  • The MPDA map architecture shows promise for real-time robotic control and autonomous systems.
  • The approach facilitates learning inverse dynamics and generating control commands through associative learning.