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We have decided to discontinue the publication of preprints on our preprint server end of 2024. The publication culture within mathematics has changed so much due to the rise of repositories such as ArXiV (www.arxiv.org) that we are encouraging all institute members to make their preprints available there. An institute's repository in its previous form is, therefore, unnecessary. The preprints published to date will remain available here, but we will not add any new preprints here.
Evolving neural behaviour control for autonomous robots
Martin Hülse, Bruno Lara, Frank Pasemann and Ulrich SteinmetzAbstract
An evolutionary algorithm for the creation of recurrent network structures is presented. The aim is to develop neural networks controlling the behaviour of miniature robots. Two different tasks are solved with this approach. For the first, the agents are required to move within an environment without colliding with obstacles. In the second task, the agents are required to move towards a light source. The evolution process is carried out in a simulated environment and individuals with high performance are also tested on a physical environment with the use of Khepera robots.
- Received:
- 04.04.01
- Published:
- 04.04.01
Related publications
Evolving neural behaviour control for autonomous robots
In: Artificial neural networks / G. Dorffner... (eds.)Berlin : Springer, 2001. - pp. 957-962
(Lecture notes in computer science ; 2130)