Ardel Alegre - CSE, Senior
Jesus Fausto - ECE, Senior
Jesi Miranda - ECE, Senior
Jacob Plata - MAE, Senior
In previous years, the cars used by groups in this class controlled the motor by sending throttle information only in the form of voltage. This is fine if the battery levels stay the same. However, an issue arises as battery levels increase or decrease. The power being sent by the battery varies and subsequently the speed of the motor varies as well. Our goal is to implement the TritonAI framework as well as their low level hardware and software to train based on velocity rather than throttle.
Jetson Nano: to do high level controls
Teensy 4.0: to do low level controls
Motor: EZRUN 3660SL G2, Brushless sensored DC motor
Base Plate: Designed to hold the Jetson Nano, camera, and other components and was mounted to the chassis of the car.
Camera Mount: The camera is mounted directly to this with the lens sticking through the hole. The curved end was designed to allow the most adjustability in terms of camera angle.
Camera Base: The camera mount attached to one of the three sets of holes at the top of this piece allowing for multiple camera heights if necessary.
Our project is the first time the TritonAI framework has been implemented on actual hardware. Their work can be found at https://github.com/Triton-AI/Triton-Racer-Sim
This framework is designed to work with the existing donkey gym framework (https://github.com/tawnkramer/gym-donkeycar).
Instructions to get Triton-Racer-Sim to work with UCSD GPU cluster
1. Install miniconda --> reference: https://dev.to/waylonwalker/installing-miniconda-on-linux-from-the-command-line-4ad7
conda create -n tritonracer python=3.8
conda activate tritonracer
pip install docopt pyserial tensorflow==2.3.0 pillow keras==2.3.0 opencv-python opencv-contrib-python pygame==2.0.0.dev10 Shapely simple-pid
conda install scikit-learn
6. Copy car program (FROM the jetson nano terminal for now):
cd ~/projects scp -r car_templates <ucsd username>.dsmlp-login.ucsd.edu:~/projects
7. Copy helper files:
cd ~/tritonracer scp -r Triton-Racer-Sim <ucsd username>.dsmlp-login.ucsd.edu:~
When in the Virtual environment:
8. Install helper packages:
conda install -c anaconda cudatoolkit=10.1 conda install cudnn=7.6.0 conda install tensorflow-gpu
9. Get the proper path for ~/projects/manage.py :
pwd (in the terminal of virtual machine) Ex: /datasets/home/28/528/<user_name>/
10. Edit manage.py :
Edit the path in manage.py with the path from Step 9
11. Launch GPU cluster:
12. Have fun!
Activate environment: conda activate tritonracer Start Training: (In the ~/projects/car_templates folder) python manage.py train --tub data/<data_tub_name> --model ./models/<model_name>.h5