A Human-Robot Mentor-Protégé Relationship to Learn Off-Road Navigation Behavior

Abstract

In this paper, we present an approach to transfer human expertise for learning off-road navigation behavior to an autonomous mobile robot. The methodology uses the concept of humanized intelligence to combine principal component analysis and neural network learning to embed human driving expertise onto mobile robots. The algorithms are tested in the field using a commercial Pioneer 2AT robot to demonstrate autonomous traversal over rough natural terrain.