ninjacar_mppi

The tools contained within this repository have been used to implement an MPPI controller based off of GeorgiaTech's AutoRally implementation.

A Notion website was developed to gain a deeper understanding on how the algorithm is structured in CUDA language.

Setup Instructions

Contents

1. Install Prerequisites
2. Clone repository
3. Run MPPI controller
4. Sidenotes

1. Install Prerequisites

1. Install Ubuntu 18.04 64-bit

2. Install required packages

   sudo apt install git doxygen openssh-server libusb-dev texinfo

3. Install ros-melodic-desktop-full

4. Install Dependencies The core idea behind MPPI is to sample thousands of trajectories really fast. This is accomplished by implementing the sampling step on a GPU, for which you will need CUDA. We also use an external library to load python's numpy zip archives (.npz files) into C++.

   • Install CUDA

   • Install CNPY

     mkdir -p ~/tmp ; cd ~/tmp
     git clone git@github.com:rogersce/cnpy.git
     cd cnpy && mkdir build ; cd build && cmake ..
     make
     sudo make install
     

   • Install gtsam

     mkdir -p ~/tmp ; cd ~/tmp
     git clone git@github.com:borglab/gtsam.git
     cd gtsam && mkdir build && cd build
     cmake -DGTSAM_INSTALL_GEOGRAPHICLIB=ON -DGTSAM_WITH_EIGEN_MKL=OFF .. 
     make
     sudo make install
     

2. Clone or Fork Repositories

Get the autorally repository in a catkin workspace. The suggested location is ~/catkin_ws/src/ , but any valid catkin worskspace source folder will work.

1. Create workspace if non-existent and switch into it

   mkdir ~/catkin_ws/src/ ; cd ~/catkin_ws/src/
   

2. Clone repository and its dependencies

   git clone git@github.com:arpg/ninja_car_driving_control.git --recurse-submodules
   git clone git@github.com:JaumeAlbardaner/ninjacar_mppi.git
   

3. Set variables in ~/.bashrc

   Your ~/.bashrc should include these lines (adapted for your PC):

   export CUDA_HOME=/usr/local/cuda
   export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64
   export PATH=$PATH:$CUDA_HOME/bin
   
   source /opt/ros/melodic/setup.bash
   source /home/yauma/catkin_ws/devel/setup.bash
   
   export AR_MPPI_PARAMS_PATH=`rospack find autorally_control`/src/path_integral/params/
   

4. Install AutoRally ROS Dependencies

   Within the catkin workspace folder, run this command to install the packages this project depends on.

   cd ~/catkin_ws/ && rosdep install --from-path src --ignore-src -y
   

5. Build ninjacar_mppi

   It is recommended that you use catkin build:

   sudo apt-get install python-catkin-tools
   

   cd ~/catkin_ws/ && catkin build autorrally_control
   

   If problems arise building drive_control, contact @mmiles19 . If the problem is related to a missing library, you may try to comment lines 105-144 of the CMakeLists.txt file within the package ros_polulu_servo, building again, descommenting them, and building again. The issue should subside.

6. Compilation & Running

   First, check your Eigen version with

   pkg-config --modversion eigen3
   

   If you don't have at least version 3.3.5, update it:

   mkdir -p ~/tmp ; cd ~/tmp
   git clone git@github.com:eigenteam/eigen-git-mirror.git
   cd eigen-git-mirror && mkdir build && cd build
   cmake .. 
   sudo make install
   

3. Run MPPI controller

1. Change into the workspace

   cd ~/catkin_ws/
   

2. Source into the workspace's environment

   source devel/setup.bash
   

3. Connect to the VRPN server (if in the lab, simulation still works otherwise)

   roslaunch vrpn_client_ros sample.launch server:=tracker
   

4. Run the MPPI controller

   If the neural network model wants to be launched:

   roslaunch autorally_control path_integral_nn.launch
   

   If the base functions model wants to be launched instead:

   roslaunch autorally_control path_integral_bf.launch
   

4. Sidenotes

1. All of the variables set in the launch files are described in the MPPI Wiki.

2. Issues with Eigen occurred during compilation, hence why in some CMakeFiles, a different c++ compiler is used.

3. The republisher called in both launchers: command_republisher.py, is a patch made to regulate that the output of MPPI actually is within a range. Otherwise it would still output values outside the min and max values set in the .launch file.

4. When the path_integral_bf.launch is called, the variable use_feedback_gains must be set to false. The DDP algorithm does not work properly when the pose messages are not given consistently, which does not hold true in the motion capture space.

5. The republisher command_rosbag.py is to be called every time one wants to capture a rosbag to train a neural network using mppi_trainer.

6. The costMap building tool is located in the ninjacar_map repository.