Goals

Our software team this year is planning to use vision in tandem with numerous sensors to craft a high-scoring autonomous as well as automating systems to reduce driver stress. Learn more about these changes below and more will be added as we implement them into our code.

Vuforia

For the Relic Recovery challenge, Vuforia is the star of the software show. Allowing our robot to recognize patterns and interpret them is critical to being able to achieve a high score in autonomous. In the first meet, our robot scored an 85-point autonomous by successfully using Vuforia to recognize and interpret ciphers.

OpenCV

OpenCV allows our robot to recognize the jewels at the beginning of autonomous and hit the correct jewel off the pedestal.

History

In the 2015-2016 season, our team advanced from the South Super Regional off the first place Control Award. We used both vision and a 9-axis IMU to run an incredible, yet unreliable, autonomous and built in many automated features like automatic flip-protection while we were climbing up the mountain. The following year, autonomous was less complicated so we left vision to use solely the IMU as means to increase reliablity. This year we are coming back to using vision in our autonomous and are intending to make numerous improvements concerning reliability.