A Neural Approach for Robot Navigation based on Cognitive Map Learning

Wenjie Yan , Cornelius Weber , Stefan Wermter
Proceedings of the International Joint Conference on Neural Networks (IJCNN 2012), pages 1146--1153, doi: 10.1109/IJCNN.2012.6252522 - Jun 2012
Associated documents :  
This paper presents a neural network architecture for a robot learning new navigation behavior by observing a human’s movement in a room. While indoor robot navigation is challenging due to the high complexity of real environments and the possible dynamic changes in a room, a human can explore a room easily without any collisions. We therefore propose a neural network that builds up a memory for spatial representations and path planning using a person’s movements as observed from a ceiling-mounted camera. Based on the human’s motion, the robot learns a map that is used for path planning and motor-action codings. We evaluate our model with a detailed case study and show that the robot navigates effectively.

 

@InProceedings{YWW12,
 	 author =  {Yan, Wenjie and Weber, Cornelius and Wermter, Stefan},
 	 title = {A Neural Approach for Robot Navigation based on Cognitive Map Learning},
 	 booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN 2012)},
 	 journal = {None},
 	 editors = {}
 	 number = {}
 	 volume = {}
 	 pages = {1146--1153},
 	 year = {2012},
 	 month = {Jun},
 	 publisher = {IEEE},
 	 doi = {10.1109/IJCNN.2012.6252522},
 }