Date Published: Dec 17, 2016
Author(s): Alon Greyber , Kevin Jaget , Adithya Balaji

How to get started with vision for FRC. This will include basic techniques, GRIP, using co-processors and OpenCV

• 2016 FRC Workshops Basic Vision.pdf

Date Published: Dec 17, 2016
Author(s): Marshall Massengill

Presented by Marshall Massengill and Amrita Malur at the FIRST NC workshops.

• Team Culture

Date Published: Dec 17, 2016
Author(s): Alon Greyber , Kevin Jaget , Adithya Balaji

How to set up and use a Jetson TX1 with advanced object recognition techniques. Covers hardware setup such as powering the board and connecting to the robot network. Also covers vision coding techniques used to find retroreflective tape targets commonly found in FRC games.

• 2016FRC Workshops Jetson TX1 Vision Processing.pdf

Date Published: Dec 13, 2016
Author(s): Marshall Massengill , Kevin Jaget

Team900 was invited to nVidia's FRC First day to present this overview of their 2016 vision code

• 2016 Season nVidia Show & Tell.pdf

Date Published: Aug 18, 2016
Author(s): Alexander Allen

Document describing the structure, implementation and utilization of neural networks for tracking game objects on the field in real time. We used the Caffe library from Berkeley Vision, the OpenCV library, and the DIGITS software from NVIDIA to create the neural network. This specific network was developed to track boulders for the FRC 2015-2016 season but can be applied to track virtually any object with proper data collection and training.

• Zebravision4.0NeuralNets.pdf

Date Published: Jun 6, 2016
Author(s): Ben Decker

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 methods of generating data for the neural networks. We took video of the ball using various lighting conditions and different, single-color backgrounds. We then identified the ball in these videos, took the clearest frames with the ball visible, and applied randomized shifts to the background in order to generate data.

• Zebravision4.0ImageCapturing.docx

Date Published: May 23, 2016
Author(s): Alon Greyber

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. 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.

• Zebravision4.0NeuralNets.pdf

Date Published: May 19, 2016
Author(s): Alon Greyber

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.

• Zebravision4.0.tracking.pdf

Date Published: Dec 14, 2015
Author(s):

This is a prototype for a serve drive train, drawn in Solidworks in the Preseason of 2015-2016.

• Solidworks Parts

Date Published: Nov 23, 2015
Author(s): Noah Haskell , Jackson Bostian

This is a port of our swerve code into the new Command and Control framework for LabVIEW.

• Code