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Triton-Racer-Sim

An autonomous robocar simulation client, designed to work with donkey gym.

The project is inspired by, and has taken reference from donkeycar.

Progress

Driving and training pipeline is up and running.

Config added. Use python manage.py generateconfig to create one.

Features

  • Speed-based models
  • Throttle and steering lock at launch
  • Adjustable image resolution in donkey gym
  • Ability to reset the car to startline
  • Ability to break
  • Inter-deployable models with donkeycar (throttle-based models only)
  • Compatability with donkeycar parts

Install

Installing on a laptop/desktop/server: follow host setup and repo download.

Installing on a Jetson series SBC: follow Jetson setup and repo download.

Set up Environment on Host Windows / Ubuntu / MacOS

  1. Install miniconda
  2. conda create -n tritonracer python=3.8
  3. conda activate tritonracer
  4. pip install docopt pyserial tensorflow==2.3.0 pillow keras==2.3.0 opencv-python pygame==2.0.0.dev10
  5. conda install scikit-learn
  6. If you are going to use donkey simulator: set up donkey gym in this environment (omit conda activate donkey in the original installation procedure)
    1. If you have a donkeycar installation with donkey gym setup, navigate to the donkey gym repository. If not, find a suitable place and git clone https://github.com/tawnkramer/gym-donkeycar
    2. Make sure you are still in tritonracer environment conda activate tritonracer
    3. pip install -e .[gym-donkeycar]

Set up Environment on Nvidia Jetson series

  1. Check python3 version to be 3.6-3.8 python3 --version. If not, install and update python3.
  2. Install virtualenv sudo apt-get install virtualenv
  3. Create a new environment python3 -m virtualenv -p python3 tritonracer
  4. Invoke tritonracer environment source ~/tritonracer/bin/activate
  5. Install tensorflow for Jetson
  6. Install dependencies pip install docopt pyserial pillow keras pygame
  7. More on the hardware setup later.

Download TritonRacer Repo (Both Host PCs and Jetson)

  1. git clone https://github.com/Triton-AI/Triton-Racer-Sim
  2. Copy Triton-Racer-Sim/TritonRacerSim/car_templates/ folder to somewhere outside the repository (and optionally rename it). This is your car folder, like "d3" of donkeycar.
  3. IMPORTANT: Go to your car folder, open manage.py and edit line 11 to be your path to the Triton-Racer-Sim repo in your system sys.path.append('/home/haoru/Projects/Triton-Racer-Sim/')

Manual

Creation of car instances will be supported later.

Config File

python manage.py generateconfig will create a myconfig.json under the car_template.

For detailed information about what each setting does: check out TritonRacerSim/core/config.py for detailed explanations.

Drive the Car

Create a config file, then enter

python manage.py drive

IMPORTANT: by default data collection is turned OFF

Use a PS4 joystick:

  • Left X axis: steering
  • Right Y axis: throttle
  • Right Trigger: breaking
  • Circle: toggle recording
  • Triangle: delete 100 records
  • Square: reset the car
  • Share: switch driving mode

Use an XBOX joystick:

  • Left X axis: steering
  • Right Y axis: throttle
  • Right Trigger: breaking
  • B: toggle recording
  • Y: delete 100 records
  • X: reset the car
  • Back (below XBOX icon on the left): switch driving mode

Use a G28 Driving Wheel (oof)

  • Steering, throttle and breaking are mapped to the corresponding equipment
  • The rest is the same as PS4

Data recorded can be found in data/records_x/

Before Training

If you have a GPU: install CUDA support for tensorflow

Note: If you have CUDA installed, and tensorflow says Could not load dynamic library 'libcublas.so.10' and falls back to CPU during training, add these lines to your ~/.bashrc:

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/extras/CUPTI/lib64
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-10.2/lib64

Train a Model

python manage.py train --tub data/records_1 --model ./models/pilot.h5

  • --tub: path to the data folder
  • --model: where you would like to put your model
  • --transfer: (optional) which old model you would like to train upon

IMPORTANT: --tub param is mandatory.

RAM usage (120 * 160 image, 7000 Records): 8GB

VRAM usage (120 * 160 image, 128 batch size): 3GB

IMPORTANT If you run into insufficient memory issue, follow this guide to pre-allocate space for Ubuntu swap file (Allocate more than the size of your physical RAM). Windows users, click here. If you do this, your system may stall momentarily duing training in the first two epoches, but that's still better than the training getting killed.

Model Types

  • cnn_2d: Take an image and predict throttle and steering;
  • cnn_2d_speed_as_feature: Take an image and current speed, and predict throttle and steering
  • cnn_2d_speed_control: Take an image and predict speed and steering;
  • cnn_2d_full_house: Take an image, segment of track, and current speed, and predict speed and steering. (Speed prediction is unrelated to current speed)

Test the Model

python manage.py drive --model ./models/pilot.h5

Switch between modes:

  • Full human control
  • Human throttle + AI steering
  • Full AI control

Migrate Component from Donkeycar, or Write Your Own

How to write your custom component for tritonracer:

  1. Subclass the Component class from TritonRacerSim.components.component import Component.
  2. Define the names of the inputs and outputs for the component in the constructor super().__init__(self, inputs=['cam/img'], outputs=['ai/steering', 'ai/throttle'], threaded=False)
  3. Implement step(self, *args). Called when the car's main loop iterate through its parts at 20Hz (equivalent to run() of donkeycar). Expect args to have the same number of elements as defined in the inputs, and remember to return the same number of outputs as defined in the outputs.
  4. Implement thread_step(self) if it is a component with its own thread of, for example, polling information from peripherals. thread_step is started before the car starts (equivalent to update() of donkeycar). Remember to put a loop, and some delay time inbetween, for the code to run periodically.
  5. (Optional) Implement other APIs onStart(self), onShutdown(self), getName(self)
  6. Add your component in manage.py's assemble_car(): car.addComponent(my_component)

Roadmap

Features to come:

  1. 3D covolution networks
  2. Reinforcement learning
  3. Migration to real car
  4. Image filtering
  5. Packaging the software
  6. Merging with donkeycar