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].
Gamepad(Teleoperation)
Here is the resource to use teleoperator on the jetracer [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 from scratch, we create a python library that will run all necessary libraries needed to operate the jetracer, then run it with matlab in the background. This topic will be covered in the next chapter.