The eblearn library is a C++ implementation of the machine learning algorithms used in the LAGR project, and in particular the supervised training of convolutional neural networks. It includes demos and tutorials.
Rovio Soccer: students programmed (in lush) Rovio robots to find a yellow tennis ball and push it between red goal posts. To robustly recognize colors, images are converted from RGB to YUV space to reduce sensitivity to different lighting conditions, then thresholding the squared Euclidean distance of the U and V components. To robustly find colored objects, noise is removed by eroding and dilating the binary image obtained after color detection and objects are identified by a connected components analysis. Finally, the distance to each object is estimated by transforming the distances from pixel space to real world space.
3PI Line following: students competed to follow a black line as fast as possible (see video) with the 3PI robots. Those robots have 5 reflectivity sensors on the front side, covering an angle of approximately 45 degrees. The fastest robots used a PID controller.
3PI Dead Reckoning: for this project, we aim for accuracy rather than speed. Robots have to follow a black line until its end, then return exactly to the starting point.
video gives an overview of the robot's intelligence developped by New
York University for the DARPA LAGR program. The robot is able to learn
new obstacles representations thanks to neural networks. It can see and
navigate from more than 200 meters away.
For this contest we have built a robot from scratch that can play rugby against another one. It grabs and shoots balls to score points. Our approach is a robust and reactive robot that does not assume his position on the playground, using vision and infrared sensors.