Optimize AI for Speed and Robustness in the Lab

Optimization of the AI

For better Ai operation, we need to have the best optimal hardware and software settings. These settings will be a determining factor to have the best possible data collection as input to train the AI:

Jetracer Optimization

Waveshare has a couple sets of examples using JupyterLab with Python libraries to Operate Ai operations with the Jetracer. To Experiment these examples, clone this repository into you jetracer: [1].

1. Gamepad(Teleoperation)[2]:

Observation

The challenge here was to control the Jetracer with a gamepad through Matlab. This method was not possible because;

  *The gamepad's adapter must be plug into the jetson nano for real time control. 
  *Gamepad uses python library before it functions with the jetracer properlly. 
  *Matlab cannot be installed on jetracer[3] since it is an arm processor (aarch64)[4]. 

Hence, making is difficult to control it directlly from matlab.

Alternative Approach

Instead of recreating the python library used on jupyterlab with matlab, we create a python library that will run the necessary libraries to operate the jetracer in the background

1. Camera:
2. Speed:
3. Steering:
4. Video format:
5. Image size: