Online-learning and Attention-based Obstacle Avoidance Using a Range Finder

We considered the problem of developing local reactive obstacle-avoidance behaviors by a mobile robot through online real-time learning. 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. We presented a learning-based approach to the problem. 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.