Evolution of Neural Controllers for Robot Navigation in Human Environments
Genci Capi and Hideki Toda
DOI : 10.3844/jcssp.2010.837.843
Journal of Computer Science
Volume 6, Issue 8
Problem statement: In this study, we presented a novel vision-based learning approach for autonomous robot navigation. Approach: In our method, we converted the captured image in a binary one, which after the partition is used as the input of the neural controller. Results: The neural control system, which maps the visual information to motor commands, is evolved online using real robots. Conclusion/Recommendations: We showed that evolved neural networks performed well in indoor human environments. Furthermore, we compared the performance of neural controllers with an algorithmic vision based control method.
© 2010 Genci Capi and Hideki Toda. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.