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Autonomous System made by Formula Student Driverless team UMotorsport

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TODO

  • yaml config file, remove globals
  • move all utils to my_utils
  • the agent should have all the conditionals and control the vehicle when in a mission. Should have the while True loop?
  • rename github project from Deteccion_conos to um_driverless
  • find todos and fix them
  • check delays between simulator and processed image, response time
  • knowing the pickling error, try to visualize to a thread

UM-Driverless Contents

We won't use Conda since it's not necessary, and the several python versions have caused problems. Also conda can't install all the packages we need, so there would be some packages installed with pip and others with conda. It also caused problems with docker.

  • First apt installs

    sudo apt update && sudo apt upgrade -y #; spd-say "I finished the update"
sudo apt install curl nano git pip python3 zstd #zstd is zed dependency
pip install --upgrade pip; #spd-say "Finished the installs"

  • Clone the GitHub directory:

    git clone https://github.com/UM-Driverless/Deteccion_conos.git

  • Install the requirements (for yolo network and for our scripts)

    cd ~/Deteccion_conos
pip install -r {requirements_file_name}.txt #yolo_requirements.txt requirements.txt

  • [OPTIONAL] If you want to modify the weights, include the weights folder in: "yolov5/weights/yolov5_models"

  • ZED Camera Installation.

    1. Download the SDK according to desired CUDA version and system (Ubuntu, Nvidia jetson xavier jetpack, ...). If it doesn't find the matching CUDA version of the SDK, it will install it. When detected, it will continue with the installation.
    2. Add permits: 
      sudo chmod 777 {FILENAME}
    3. Run it without sudo (You can copy the file and Ctrl+Shift+V into the terminal. Don't know why tab doesn't complete the filename): 
      sh {FILENAME}.run
    4. By default accept to install cuda, static version of SDK, AI module, samples and Python API. Diagnostic not required.
    5. Now it should be installed in the deault installation path: /usr/local/zed
    6. To get the Python API (Otherwise pyzed won't be installed and will throw an error): 
      python3 /usr/local/zed/get_python_api.py

  • To make sure you are using the GPU (Get IS CUDA AVAILABLE? : True)

    • Check what GPU driver you should install: https://www.nvidia.co.uk/Download/index.aspx?lang=en-uk
    • Check what GPU driver you have. X.Org -> nvidia-driver-515. In Software and Updates.
    • If errors, reinstall the driver from scratch: 
      sudo apt-get remove --purge nvidia-* -y
sudo apt autoremove
sudo ubuntu-drivers autoinstall
sudo service lightdm restart
sudo apt install nvidia-driver-525 nvidia-dkms-525
sudo reboot

  • To check all cuda versions installed dpkg -l | grep -i cuda

  • You can the cuda version compatible with the graphics driver using nvidia-smi or the built-in app in xavier o orin modules.

  • To check the cuda version of the installed compiler, use /usr/local/cuda/bin/nvcc --version

  • Now pytorch should use the same CUDA version as the ZED camera. Check this: https://www.stereolabs.com/docs/pytorch/

  • You should be able to run:

    python3 main.py
  • To explore if something fails:
    • sudo apt-get install python3-tk

INSTALL SIMULATOR

Go to https://github.com/FS-Driverless/Formula-Student-Driverless-Simulator/releases and download the latest version. This is an executable file that will run the simulator. It can be stored and run from anywhere. To connect to the Python code, clone the repo in the same folder as Deteccion_conos. Here you can see Python examples.

Test program 
# This code adds the fsds package to the pyhthon path.
# It assumes the fsds repo is cloned in the home directory.
# Replace fsds_lib_path with a path to wherever the python directory is located.
import sys, os
# fsds_lib_path = os.path.join(os.path.expanduser("~"), "Formula-Student-Driverless-Simulator", "python")
fsds_lib_path = os.path.join(os.getcwd(),"python")
print('CARPETA:',fsds_lib_path)
sys.path.insert(0, fsds_lib_path)

import time

import fsds

# connect to the AirSim simulator 
client = fsds.FSDSClient()

# Check network connection
client.confirmConnection()

# After enabling api controll only the api can controll the car. 
# Direct keyboard and joystick into the simulator are disabled.
# If you want to still be able to drive with the keyboard while also 
# controll the car using the api, call client.enableApiControl(False)
client.enableApiControl(True)

# Instruct the car to go full-speed forward
car_controls = fsds.CarControls()
car_controls.throttle = 1
client.setCarControls(car_controls)

time.sleep(5)

# Places the vehicle back at it's original position
client.reset()

To use, first run the fsds-... file, click "Run simulation", then run the python code

Notes

  • To use CAN comm with the Nvidia Jetson Orin, the can bus has to be working properly and connected when the Orin turns on. There has to be at least another device to acknowledge messages.

NVIDIA JETSON XAVIER NX SETUP

TODO Testing with Jetpack 5.1

KVASER Setup in Ubuntu

tar -xvzf linuxcan.tar.gz
sudo apt-get install build-essential
sudo apt-get install linux-headers-`uname -r`

In linuxcan, and linuxcan/canlib, run:

make
sudo make install

In linuxcan/common, run:

make
sudo ./installscript.sh

To have the python API:

pip3 install canlib

To DEBUG:

make KV_Debug_ON=1

conda

Cliente para realizar la detección de conos en el simulador

Este cliente funciona en conjunto con el simulador desarrollado en https://github.com/AlbaranezJavier/UnityTrainerPy. Para hacerlo funcionar solo será necesario seguir las instrucciones del repositorio indicado para arrancar el simulador y posteriormente ejecutar el cliente que podemos encontrar en el archivo /PyUMotorsport/main_cone_detection.py

Los pesos de la red neuronal para el main.py se encuentran en el siguiente enlace: https://drive.google.com/file/d/1H-KOYKMu6KM3g8ENCnYPSPTvb6zVnnFX/view?usp=sharing Se debe descomprimir el archivo dentro de la carpeta: /PyUMotorsport/cone_detection/saved_models/

Los pesos de la red neuronal para el main_2.py se encuentran en el siguiente enlace: https://drive.google.com/file/d/1NFDBKxpRcfPs8PV3oftLya_M9GxW8O5h/view?usp=sharing Se debe descomprimir el archivo dentro de la carpeta: /PyUMotorsport_v2/ObjectDetectionSegmentation/DetectionData/

To test

Go to canlib/examples

./listChannels
./canmonitor 0

To install any driver (canlib and kvcommon must be installed first):

make
sudo ./installscript.sh

Old stuff

Crea tu entorno virtual en python 3.8 y activalo

conda create -n formula python=3.8
conda activate formula
#conda install tensorflow-gpu

A continuación vamos a installar el Model Zoo de detección de Tensorflow

Si no tienes todavía la carpeta models/research/

git clone --depth 1 https://github.com/tensorflow/models

Una vez dispones de la carpeta models/research/

cd models/research/
protoc object_detection/protos/*.proto --python_out=.
cp object_detection/packages/tf2/setup.py .
python -m pip install .

Actualizar Xavier para ejecutar YOLOv5 (06/2022)

git clone https://github.com/UM-Driverless/Deteccion_conos.git
cd Deteccion_conos
pip3 install -r yolov5/yolo_requeriments.txt
sh can_scripts/enable_CAN.sh
python3 car_actuator_testing_zed_conect_yolo.py
  • Try to use a preconfigured JetPack 5.0.2 PyTorch Docker container, with all the dependencies and versiones solved: https://blog.roboflow.com/deploy-yolov5-to-jetson-nx/
    • Register in docker website
    • Login. If it doesn't work, reboot and try again. 
      docker login
    • Take the tag of a container from here: https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-pytorch . For example, for JetPack 5.0.2 (L4T R35.1.0) it's l4t-pytorch:r35.1.0-pth1.13-py3
    • Pull container 
      # l4t-pytorch:r35.1.0-pth1.13-py3 ->
sudo docker pull nvcr.io/nvidia/l4t-pytorch:r35.1.0-pth1.13-py3
    • Run container 
      # Will download about 10GB of stuff
sudo docker run -it --rm --runtime nvidia --network host nvcr.io/nvidia/l4t-pytorch:r35.1.0-pth1.13-py3
    • TODO FINISH

(Install visual studio, pycharm, telegram, ...)

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Autonomous System made by Formula Student Driverless team UMotorsport

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