Research Article Open Access

Evolution of Neural Controllers for Robot Navigation in Human Environments

Genci Capi and Hideki Toda

Abstract

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.

Journal of Computer Science
Volume 6 No. 8, 2010, 837-843

DOI: https://doi.org/10.3844/jcssp.2010.837.843

Submitted On: 5 May 2010 Published On: 31 August 2010

How to Cite: Capi, G. & Toda, H. (2010). Evolution of Neural Controllers for Robot Navigation in Human Environments. Journal of Computer Science, 6(8), 837-843. https://doi.org/10.3844/jcssp.2010.837.843

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Keywords

  • Robot navigation
  • vision
  • neural networks
  • evolutionary algorithm