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Using analytic and genetic methods to learn plans for mobile robots

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.