Evolution of synaptic delay based neural controllers for implementing central pattern generators in hexapod robotic structures

Abstract

We used synaptic delay based neural networks for implementing Central Pattern Generators (CPGs) for locomotion behaviours in hexapod robotic structures. These networks incorporate synaptic delays in their connections which allow greater time reasoning capabilities in the neural controllers, and additionally we incorporated the concept of the center-crossing condition in such networks to facilitate obtaining oscillation patterns for the robotic control. We compared the results against continuous time recurrent neural networks, one of the neural models most used as CPG, when proprioceptive information is used to provide fault tolerance for the required behavior.