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Proceedings Paper

Path planning for mobile robot using sonar map and neural network
Author(s): Jin Cao; Wen-chuan Chiang; Terrell Nathan Mundhenk; Ernest L. Hall
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Paper Abstract

The purpose of this paper is to present a new approach for path planing of a mobile robot in static outdoor environments. A simple sensor model is developed for fast acquisition of environment information. The obstacle avoidance system is based on a micro-controller interfaced with multiple ultrasonic transducers with a rotating motor. Using sonar readings and environment knowledge, a local map based on weight evaluation function is built for the robot path planing. The path planner finds the local optimal path using the A* search algorithm. The robot is trained to learn a goal-directed task under adequate supervision. The simulation experiments show that a robot, utilizing our neural network scheme, can learn tasks of obstacle avoidance in the work space of a certain geometrical complexity. The result shows that the proposed algorithm can be efficiently implemented in an outdoor environment.

Paper Details

Date Published: 6 October 1998
PDF: 9 pages
Proc. SPIE 3522, Intelligent Robots and Computer Vision XVII: Algorithms, Techniques, and Active Vision, (6 October 1998); doi: 10.1117/12.325771
Show Author Affiliations
Jin Cao, Univ. of Cincinnati (United States)
Wen-chuan Chiang, Univ. of Cincinnati (United States)
Terrell Nathan Mundhenk, Univ. of Cincinnati (United States)
Ernest L. Hall, Univ. of Cincinnati (United States)


Published in SPIE Proceedings Vol. 3522:
Intelligent Robots and Computer Vision XVII: Algorithms, Techniques, and Active Vision
David P. Casasent, Editor(s)

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