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Collision identification between convex polyhedra using neural networks.

J Yuan1

  • 1Dept. of Mech. Eng., Windsor Univ., Ont.

IEEE Transactions on Neural Networks
|January 1, 1995
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel collision identification neural network (CINN) for detecting potential collisions between convex polyhedra. The CINN offers an efficient solution for robot path planning and computer-aided manufacturing applications.

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computational Geometry

Background:

  • Collision identification is crucial for robot path planning and computer-aided manufacturing.
  • Existing methods can be computationally intensive for real-time applications.

Purpose of the Study:

  • To present a novel collision-identification neural network (CINN) for detecting collisions between convex polyhedra.
  • To demonstrate the CINN's suitability for online robot path planning.

Main Methods:

  • The proposed CINN integrates a modified Hamming net for point-to-polyhedron collision detection.
  • A constraint subnet assists in moving points within polyhedra to identify potential collisions.
  • The network features a simple canonical structure, implementable with basic electronic components.

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Main Results:

  • The CINN effectively identifies collisions between convex polyhedra.
  • Its design is analogous to the Hopfield net model, leveraging collective computing power.
  • The network is programmable and can be realized using nonlinear amplifiers and analog integrators.

Conclusions:

  • The CINN provides an efficient and simple method for convex polyhedron collision identification.
  • It is well-suited for real-time applications, particularly in online robot path planning.
  • The presented example demonstrates successful application in collision-free motion planning.