- FTC ANDROID STUDIO TUTORIAL HOW TO
- FTC ANDROID STUDIO TUTORIAL DRIVER
- FTC ANDROID STUDIO TUTORIAL SERIES
They connect to each other through the cell phones internal wifi system so each team is on their own wireless system.
FTC ANDROID STUDIO TUTORIAL DRIVER
The robot has a Boost ZTE Speed Android cell phone (64-bit, 8GB RAM, 1.2 GHz ~ 300x faster than the NXT) and the Driver Station has a Boost ZTE Speed Android cell phone. The new 2015-16 platform is based around Android cell phones. In theory this was a good configuration, however, because of power connection issues and some Samantha wifi issues the overall difficulty of setting up, troubleshooting, and correcting the problems forced a national redesign of the wireless connection between driver and robot. Joysticks were connected to a local computer and commands transmitted through wifi to the master controller. The previous hardware platform was the LEGO NXT brick (32-bit, 64KB RAM, 4Mhz) was used as the computer, an add-on Samantha wifi module was used to connect the robot to a dedicated router to a master network controller and programming was performed mostly with RobotC and some with LabView. In 2015 for the 2015-16 season the computer hardware, wifi, and programming changed.
Here is the official FIRST FTC announcement of this new technology platform.
FTC ANDROID STUDIO TUTORIAL SERIES
f) Watch THIS from FTC and follow this training series from Intelitek. Find a Java programmer to help you get started. e) If you have programmed then programming option #2 and #3 are probably for you. d) If you have never programmed learn about programming option #1 and maybe option #2.
FTC ANDROID STUDIO TUTORIAL HOW TO
c) Watch this on how to setup your phones. b) Buy your two Boost ZTE Speed phones while they are still available at a reasonable price. Here are my suggestions … a) Scan and read this page to get a general knowledge of what is going on. This includes collecting visual data, annotating the data, and training the model.īelow is my tutorial video with the Google Colab Notebook linked.Already confused and frustrated? Don’t know where to start. I have been able to successfully make TFLite models in a matter of hours. Using Google Colab allows us to use Google run servers and GPUs to make the machine learning model training pretty quick. It is a web-based interface for Python programming. It is essentially a Google Doc for Jupyter Notebooks. I had never used this technology before and I have become very impressed with it. The tool itself it embedded into a Google Colab Notebook. I have created a tutorial video for the tool below. After about two weeks, lots of reading, lots of coding, lots of mistakes, lots of issues, etc., I finally have a working tool that all teams of FTC can use to create their models. So, I set out to learn how to make my own TensorFlowLite (TFLite) machine learning models for Object Detection. (This might have to do with the management of private student information)
And 2) my school district does not allow students to have a FIRST Inspires account. This is limiting for two reasons: 1) some students across the world may not be on an official team and want to learn about machine learning. To access this, teams need to have a login with the FIRST Inspires website. Most of my students prefer the OnBot Java setup for its ease of use while still allowing them to use all of their programming skills from AP CSA.įTC uses TensorFlowLite Object Detection and has created FIRST Machine Learning Toolchain (FTC-ML) so that teams can train their own machine learning models. But this had a very steep learning curve that required students to use Android Studio when making their code. We have also used OpenCV for object detection in the past. In the past we have used Vuforia and their VuMarks technology. This type of task has been apart of FTC for about 4 or 5 years now.
This time I spent my time learning about and creating a tool for FIRST Tech Challenge (FTC) teams to train their robots to recognize objects using a camera. I spent my 2022-2023 Winter Break coding.
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