In the field of evolutionary robotics, artificial neural networks (ANNs) are an intriguing control strategy attempting to replicate the functionality of natural brains. These networks, essentially directed graphs, with the possibility for cycles, are comprised of nodes containing a mathematical function, connected by weighted edges. Inputs are correlated with information that may be useful for a robot such as: orientation, speed, goal conditions, etc., which is then propagated through the edges and weights to arrive at a set of outputs to direct motor movements or sensor readings. Unfortunately, the size and complexity of these networks can grow rapidly when anything but the most simple tasks are attempted, making these graphs very challenging to interpret what processes and information are being used by the ANN for controlling the robot. I’ll save the long description of ANNs, but for an idea of what they can do, the following video features an ANN to control a swimming robot in a simulated flow.
Author Archives: Jared Moore
WebGL for Scientific Visualization
I plan to flesh this out into a full fledged blog post in the future. For now, this page contains links that complement my presentation at the Visualization Workshop during BEACON Congress 2013.
Coupled with supplementary code, WebGL can be used to create interactive demos or even present simulations directly in a web browser. Conway’s Game of Life has been implemented many times in WebGL, but I have found this one to be particularly interesting. Additional demos for games, simulations and scientific visualizations can be found at http://www.chromeexperiments.com/webgl/. Disclaimer: Try not to spend too much time on the site!