Obstacle avoidance through incremental learning with attention selection

Abstract

This work presents a learning-based approach to the task of generating local reactive obstacle avoidance. The learning is performed online in real-time by a mobile robot. The robot operated in an unknown bounded 2-D environment populated by static or moving obstacles (with slow speeds) of arbitrary shape. The sensory perception was based on a laser range finder. To greatly reduce the number of training samples needed, an attentional mechanism was used. An efficient, real-time implementation of the approach had been tested, demonstrating smooth obstacle-avoidance behaviors in a corridor with a crowd of moving students as well as static obstacles.