Evolving neural networks for hexapod leg controllers

The incremental evolution of neural networks to control hexapod robot locomotion can be separated into two main parts: the evolution of leg controllers the cycle action of single legs (leg cycles) and the evolution of the coordination of these individual leg controllers to produce a gait. In this paper, we use a genetic algorithm to do the first of these steps, to evolve the structure of an artificial neural network that produces leg cycles for a hexapod robot. The robot has 12 servo effectors; two per leg to produce horizontal and vertical movement. The servos are controlled by pulses that are provided by the leg´s controller. A cycle of these pulses produces a leg cycle. With minimal restrictions on the structure of the neural network, a genetic algorithm was used to evolve in simulation the parameters of neurons and their connections. Neural networks were implemented on a BASIC Stamp II SX microcomputer and found to generate smooth leg cycles on the hexapod robot.