Using analytic and genetic methods to learn plans for mobile robots

Dianne J. Cook

doi:10.1117/12.141781

11 March 1993 Using analytic and genetic methods to learn plans for mobile robots

Dianne J. Cook

Proceedings Volume 1964, Applications of Artificial Intelligence 1993: Machine Vision and Robotics; (1993) https://doi.org/10.1117/12.141781
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

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.

Citation Download Citation

Dianne J. Cook "Using analytic and genetic methods to learn plans for mobile robots", Proc. SPIE 1964, Applications of Artificial Intelligence 1993: Machine Vision and Robotics, (11 March 1993); https://doi.org/10.1117/12.141781

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