Evolved Neurodynamics for Robot Control

Small recurrent neural network with two and three neurons are able to control autonomous robots showing obstacle avoidance and photo-tropic behaviors. They have been generated by evolutionary processes, and they demonstrate, how dynamical properties can be used for an effective behavior control. Presented examples also show how sensor fusion can be obtained by evolution. Additional techniques are used to excavate the relevant neural processing mechanisms underlying specific behavior features.

• Type: Conference
• Year: 2003
• Where: ESANN
• Authors: Frank Pasemann, Martin Hülse, Keyan Zahedi
• Keywords: ESANN 2003, ESANN 2007, Effective Behavior Control, Recurrent Neural Network, Robots Showing Obstacle