Sculpting in time

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DJI RoboMaster EP

At the beginning of the year I planned to spend six months researching autonomous driving, and computer graphics, so I started researching hardware for developing autonomous driving systems. The first thing I considered was the jetbot that was open when nvidia released the jetson nano platform in 2019, which is basically a toy car equipped with a 3D printed body, two small wheels, and a jetson nano developer kit, a camera, and official project address, considering the cost needed to build the entire car hardware platform and the playability, I decided not to consider a toy-like solution for the car robot, and finally I aimed at the DJI robomaster EP because It opened up the complete SDK, and the whole robot platform was designed for educational competitions, which is very rich in playability and does not require too much thinking about the underlying hardware assembly itself. This allowed me to take a page from jetbot’s autopilot design and port it to the robomaster EP to create the most original autopilot development platform.

Coral Edge TPU As Robot Brain

After a period of hands-on testing, I decided that the hardware solution I would use on the robomaster EP would be raspberry pi 4 + google edge tpu, with the following architecture:

The left side is some tools that will be used for autonomous driving, the vision acceleration chip I used is google edge tpu, compared to nvidia Jetson GPU, it has lower power consumption, so that when doing visual computing will not consume a lot of the robot’s limited battery power.

The right side is a machine learning operation and maintenance platform that I need to build myself, from data collection, model training and testing, and finally model deployment, are all automated.

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