ECE/MAE 148 FA19 Team 7 Project: adaptive cruise control on autonomous driving RC car.

Please checkout our wiki page for more information: https://guitar.ucsd.edu/maeece148/index.php/2019FallTeam7#References

We use ROS in this project to work with RPLidar. Therefore please install ROS on your Jetson/Rpi first.

Follow the tutorials below to install ROS and setup catkin workspace:

http://wiki.ros.org/melodic/Installation/Ubuntu
http://wiki.ros.org/ROS/Tutorials/InstallingandConfiguringROSEnvironment

In order to communicate with RPLidar, we use the offical RPLidar ROS package:

https://github.com/robopeak/rplidar_ros

Wiki page for this package is:

http://wiki.ros.org/rplidar

To install this package, ssh into jetson and clone the repo into catkin_ws/src:

cd ~/catkin_ws/src
git clone https://github.com/robopeak/rplidar_ros.git

Then build this package:

cd ~/catkin_ws
catkin_make

This project uses YOLO for pedestrain and traffic sign detection. Please install YOLO under projects/d3 following the instruction:

https://pjreddie.com/darknet/install/

Make sure to download pretrained YOLO weights. For more information please refer to:

https://pjreddie.com/darknet/yolo/

cd ~/projects
git clone https://github.com/InspireX96/ECE148AdaptiveCruiseControl.git

Install required python packages in addition to packages in donkeycar framework

cd ECE148AdaptiveCruiseControl
pip install -r requirements.txt   # NOTE: you may need to run this command in sudo

To setup this project on top of existing donkey framework, simply run command:

cd src
./setup.sh

Use the launch file in RPLidar ROS package to turn on the LIDAR.

roslaunch rplidar_ros rplidar.launch

If you get some error messages here, please refer to the Notes section

Now it's time to run the vehicle!

cd ~/projects/d3
python manage_modified.py drive

Autopilot feature is preserved in manage_modified.py. Usage is the same as the original manage.py. Please refer to the original donkey framework for more information.

There are three drive modes to select:

1. Manual steering and throttle
2. Manual steering and adaptive cruise control throttle
3. AI steering and adaptive cruise control throttle

To visualize different LIDAR filters and LIDAR distance calculaor, you can run lidar_filter_player.py.

Before running this script, please make sure ROS is publishing LIDAR scans, so you can visualize them in real time.

You can manually run unit tests of this package. Please install the setup.py in order to let pytest find this package on your computer. You can setup using pip:

pip install -e .

Then run the unit tests:

pytest -v

This problem may cause apt-get update failure or catkin_make failure.

https://askubuntu.com/questions/929805/timedatectl-ntp-sync-cannot-set-to-yes

sudo service ntp stop
sudo ntpd -gq
sudo service ntp start
timedatectl   # check if ntp is synchronized

https://answers.ros.org/question/190317/ros-cant-find-package/

cd ~/catkin_ws
source devel/setup.bash

When running command roslaunch rplidar_ros rplidar.launch, this error message appears:

Error, cannot bind to the specified serial port /dev/ttyUSB0.

To solve this, we need to change the permission of ttyUSB0 by:

sudo chmod 666 /dev/ttyUSB0

After running command roslaunch rplidar_ros rplidar.launch and trying to visualize in Rviz, Rviz does not show LIDAR scan and throws error message:

For frame [laser]: Fixed Frame [map] does not exist

This is because tranformation from world to lidar frame is not defined. You can publish it manually by:

rosrun tf static_transform_publisher 0 0 0 0 0 0 map laser 10

Alternatively, you can run this command instead:

roslaunch rplidar_ros view_rplidar.launch

sudo apt-get install python3-pip python3-yaml
sudo pip3 install rospkg catkin_pkg

This project is intended to be used within the donkey car framework https://github.com/autorope/donkeycar.git

Object detection utilizes YOLO:

@article{yolov3,
  title={YOLOv3: An Incremental Improvement},
  author={Redmon, Joseph and Farhadi, Ali},
  journal = {arXiv},
  year={2018}
}

Travis CI with ROS utilizes .travis.yml sections from from https://github.com/felixduvallet/ros-travis-integration.git

Copyright (c) 2016, Felix Duvallet
All rights reserved.