Evolving Snake Robot Controllers Using Artificial Neural Networks as an Alternative to a Physics-Based Simulator

Traditional simulators can be complex, time-consuming and require specialized knowledge to develop while still being unable to adequately model reality. Artificial Neural Networks (ANNs) can be trained to simulate real-world robots and therefore serve as an alternative to traditional approaches of robot simulation during the Evolutionary Robotics (ER) process. ANN-based simulators require little specialized knowledge and can automatically incorporate many real-world peculiarities. This paper reports a simulator that consisted of ANNs which were trained to predict changes in the position of a real-world snakelike robot. Navigational behaviours were evolved in simulation and subsequently verified on the real-world robot. This paper demonstrated that ANNs are a viable alternative to traditional simulators for evolving controllers for snake-like robots.