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Proceedings Paper

Optimal path planning for robot navigation by the Hopfield net
Author(s): G. Castellano; Ettore Stella; Giovanni Attolico; Arcangelo Distante
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Paper Abstract

Navigation in dynamic indoor environments requires a mobile vehicle to follow the planned path while avoiding unexpected obstacles eventually met along it. In this paper an attempt of designing a path planar using a computational model suitable for fast implementation on special purpose hardware is presented, in which the automatic modeling of the scene and its continuous updating are accomplished by means of a recursive ultrasonic-based obstacle avoidance system. From this model a graph representing all the possible paths for the robot in the free-space is built using well known methodologies (configuration space, generalized cones). The task of searching for the shortest path in this graph is solved by means of a neural network based on the Hopfield model, that represents an interesting alternative to classical techniques as A*. A major advantage of this neural approach is the parallel nature of the resulting network that allows a rapid convergence to a solution when implemented in hardware. Simulation results are shown to illustrate the performance of the Hopfield path planner.

Paper Details

Date Published: 3 October 1995
PDF: 10 pages
Proc. SPIE 2588, Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling, (3 October 1995); doi: 10.1117/12.222709
Show Author Affiliations
G. Castellano, Istituto Elaborazione Segnali ed Immagini (Italy)
Ettore Stella, Istituto Elaborazione Segnali ed Immagini (Italy)
Giovanni Attolico, Istituto Elaborazione Segnali ed Immagini (Italy)
Arcangelo Distante, Istituto Elaborazione Segnali ed Immagini (Italy)


Published in SPIE Proceedings Vol. 2588:
Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling
David P. Casasent, Editor(s)

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