Zebracorn Labs
Here at the Zebracorns, we believe in learning and pushing boundaries. In pursuit of that, we have published papers, given talks, and have other tidbits of knowledge lying around. We hope you enjoy these as much as we do.
Here at the Zebracorns, we believe in learning and pushing boundaries. In pursuit of that, we have published papers, given talks, and have other tidbits of knowledge lying around. We hope you enjoy these as much as we do.
Zebravision 4.0 is Team 900's vision system for the 2016 season; FIRST Stronghold. Our work was focused around recognizing the vision goals using shape and color based matching, recognizing the boulders using a neural network, and integrating the detection systems into a tracking system using the StereoLabs ZED stereo camera. One of the main features of Team 900's Zebravision code this year was goal detection. This paper gives an overview of the hardware and code used. The system used a Stereolabs ZED RGB-depth camera and green LED rings to highlight the retroreflective tape around the goal. The image was filtered to look for the reflected LED color and thresholded to turn it into binary green / not green image. The code then extracted contours from the image and applied a number of simple filters to rule out blobs which were obviously not goals. The remaining contours were scored in a number of criteria and the best scoring few objects were assumed to be goals. If more than one valid goal is found, several tiebreakers were used to pick one goal to shoot at. If a valid goal was found, the angle and distance to the target was reported; if none were found, a packet with -1.0 distance and angle was returned to the roboRIO.
Zebravision 4.0 is Team 900s vision system for the 2016 season; FIRST Stronghold. Our work was focused around recognizing the vision goals using shape and color based matching, recognizing the boulders using a neural network, and integrating the detection systems into a tracking system using the StereoLabs ZED stereo camera. This paper describes our tracking system, or how we get useful information that is persistent across frames from our detections.
This is a paper written in conjunction with FRC team 5190 - Green Hope Falcons.
This is a prototype for a serve drive train, drawn in Solidworks in the Preseason of 2015-2016.
All about git and its application for LabView as given at the FRC NC Workshop at JCC
An introduction to strategy analysis as given at the FRC NC Workshop at JCC
Introduction to computer vision in relation to FRC as given at the FRC NC Workshop at JCC.
All about the new Command and Control structure - from a Beta team as given at the FRC NC Workshop at JCC
An introduction to CAD in general and a tutorial of Onshape as given at the FRC NC Workshop at JCC
This is a port of our swerve code into the new Command and Control framework for LabVIEW.