C++ and Python Programming with Go1

DroneBlocks now includes its comprehensive curriculum and courses on quadruped robots for FREE for one-year with every purchase of a Unitree Go2 EDU robot, opening the door to advanced robotics education for educators, students, and hobbyists alike. This initiative provides immediate access to high-quality learning materials, ensuring that learners can dive into the world of robotics with the best tools and knowledge at their fingertips. Learn more about the Quadruped Robotics Program from DroneBlocks Here.

This course is designed to provide a practical introduction to programming the Unitree Go1 quadruped robot using both C++ and Python languages on the Windows Subsystem for Linux (WSL). Students will learn how to use WSL to set up a Linux environment on a Windows machine, and how to use C++ and Python to control the robot's movements and interact with its sensors.

The course will begin with an overview of WSL and how it is used in the context of the Unitree Go1 quadruped robot. Students will then learn how to set up the environment and install the required packages to enable programming the robot with C++ and Python. Next, students will learn the basics of programming the robot with C++ and Python, including how to control the robot's movements.

Coming soon...

Students will learn how to use open-source computer vision libraries and tools to process and analyze data from the robot's camera systems. These new lessons will cover a range of computer vision topics, including color-based object detection, advanced object detection with YOLO, and developing computer vision apps for the robot.

The lessons will begin with an overview of the Unitree Go1 robot and its camera systems. Students will learn how to interface with the robot's cameras and capture images for processing. Next, the lessons will cover fundamental computer vision concepts such as image processing, filtering, and segmentation. Students will learn how to use the OpenCV library to implement these concepts in Python.

After the foundational topics are covered, the course will delve into more advanced computer vision topics. Students will learn how to implement color-based object detection using OpenCV and Python. The course will also cover deep learning-based object detection using YOLO, one of the most popular and widely used object detection frameworks.

Finally, the lessons will conclude with a project-based module where students will learn to develop their first computer vision app for the Unitree Go1 quadruped robot. Students will learn how to integrate their computer vision models with the robot's movement and control systems to create an application that can detect and interact with objects in its environment.