Exploration and Model Building in Mobile Robot Domains

Abstract

The first results on COLUMBUS, an autonomous mobile robot, are presented. COLUMBUS operates in initially unknown structured environments. Its task is to explore and model the environment efficiently while avoiding collisions with obstacles. COLUMBUS uses an instance-based learning technique for modeling its environment. Real-world experiences are generalized via two artificial neural networks that encode the characteristics of the robot's sensors, as well as the characteristics of typical environments which the robot is assumed to face. Once trained, these networks allow for the transfer of knowledge across different environments the robot will face over its lifetime. Exploration is achieved by navigating to low confidence regions. A dynamic programming method is employed in background to find minimal-cost paths that, when executed by the robot, maximize exploration.