Cartesian Planner ROS Package

C++/ROS Source Codes for "Autonomous Driving on Curvy Roads without Reliance on Frenet Frame: A Cartesian-based Trajectory Planning Method" published in IEEE Trans. Intelligent Transportation Systems by Bai Li, Yakun Ouyang, Li Li, and Youmin Zhang.

Installation

Requirements

• ROS Melodic or later
• Python3

Install packages required by Ipopt

sudo apt-get install gcc g++ gfortran git patch wget pkg-config liblapack-dev libmetis-dev

Clone repository to any catkin workspace and compile workspace

cd ~/catkin_ws/src
git clone https://github.com/libai1943/CartesianPlanner.git cartesian_planner
cd .. && catkin_make
source devel/setup.bash

OPTIONAL: build and install Harwell Subroutine Library (HSL) (recommended for better performance)

git clone https://github.com/coin-or-tools/ThirdParty-HSL.git

# Obtain a tarball with HSL source code from http://www.hsl.rl.ac.uk/ipopt/ and unpack this tarball
tar -zxvf coinhsl-x.y.z.tar

# Rename the directory `coinhsl-x.y.z` to `coinhsl`, or set a symbolic link:
ln -s coinhsl-x.y.z coinhsl

./configure
make
sudo make install

# create symlink for Ipopt
sudo ln -s /usr/local/lib/libcoinhsl.so /usr/local/lib/libhsl.so
# Re-build workspace
cd ~/catkin_ws && catkin_make -DWITH_HSL=ON

Example

tits_pedestrian_static_dynamic_3.mp4

Example test case with 6 pedestrians, 3 moving vehicles and 2 static vehicles.

roslaunch cartesian_planner pedestrian_test.launch

Click anywhere in Rviz window with the 2D Nav Goal Tool to start planning.

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Generate and run new random case:

roslaunch cartesian_planner random_pedestrian_test.launch

Acknowledgement

CASADi

Special thanks to Baidu Apollo for common math libraries

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Copyright (C) 2022 Bai Li and Yakun Ouyang

Users must cite the following article if they use the source codes to conduct simulations in their new publications. Bai Li, Yakun Ouyang, Li Li, and Youmin Zhang, “Autonomous driving on curvy roads without reliance on Frenet frame: A Cartesian-based trajectory planning method,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 9, pp. 15729 - 15741, 2022. available at https://doi.org/10.1109/TITS.2022.3145389