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Proceedings Paper

Using analytic and genetic methods to learn plans for mobile robots
Author(s): Dianne J. Cook
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Paper Abstract

A small mobile robot can be of great use in exploring environments, maneuvering through dangerous areas, identifying and tracking objects, and carrying cargo. Current methods of planning for robots focus on heavy on-board processing making use of multiple goals, learning, and failure recovery, or they focus on using very little on-board power running small reactive plans. We describe a method that makes use of both types of planning. While an on- board processor can generate small reactive plans for one particular goal, an off-site computer can perform goal management and learn from the robot's failures and successes to modify the rule base for the robot's future plans. This paper describes these ideas and illustrates their use on a T1 mobile robot.

Paper Details

Date Published: 11 March 1993
PDF: 10 pages
Proc. SPIE 1964, Applications of Artificial Intelligence 1993: Machine Vision and Robotics, (11 March 1993); doi: 10.1117/12.141781
Show Author Affiliations
Dianne J. Cook, Univ. of Texas/Arlington (United States)


Published in SPIE Proceedings Vol. 1964:
Applications of Artificial Intelligence 1993: Machine Vision and Robotics
Kim L. Boyer; Louise Stark, Editor(s)

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