Evolving Locomotion for a Humanoid Robot

The purpose of this bachelor work was the evolution of artificial neural networks

to develop locomotion for the DARwIn-OP robot. The DARwIn-OP, henceforth

referredtoasDarwin, isa45cmtallhumanoidrobotwhichisused, amongstothers,

in the RoboCup for robot soccer.

The main problem in robot soccer is creating a robust and fast locomotion.

Since a humanoid robot is a very complex system, it is difficult to handcraft a

robust walking algorithm. Furthermore, it needs to be adjusted by hand if the

floor or the weight distribution of the robot itself is changed.

One approach to automatically developing a walking algorithm is based on

biological evolution, by which a gradual improvement of individual solutions can

be achieved over many generations. Its parallel nature and pragmatic approach to

solve problems makes artificial evolution a well suited solution for this task. But

evolution too has certain difficulties which must be overcome. For example, tens

of thousands of experiments need to be performed in order to find a good solution

in a complex search space.

In this work, a system was developed, which exploits the concurrency offered

by evolution and performs the experiments in the Webots simulator on several

computers in parallel, thereby finding solutions in a reasonable amount of time.

It used an accurate replica model of the Darwin to evaluate the solutions, which

makes the transfer of a suitable solution to the real Darwin robot realistic.

This work focuses on the oscillating pattern generation within the artificial

neural network (ANN) and by external sources, as well as the impact of neurons in

the hidden layer of the ANN. The experiments have shown that an ANN is able to

generate a pattern without the use of a central pattern generator. Furthermore the

results indicate that at least four neurons in the hidden layer have to be present

for a locomotion to evolve.