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PRIEST

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Projection Guided Sampling-Based Optimization For Autonomous Navigation

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# PRIEST
Welcome to the **PRIEST** repository! This repository is associated with the paper **"PRIEST: Projection Guided Sampling-Based Optimization For Autonomous Navigation."**. Additionally, we have provided videos and demos, which can be accessed [here](https://sites.google.com/view/priest-optimization).

## Table of Contents
- [Contacts](#Contacts)
- [General Requirements](#General-Requirements)
- [Running the First Benchmark - Comparison on BARN Dataset](#Running-the-First-Benchmark-Comparison-on-BARN-Dataset)
- [Running the Planner on a Dynamic Environment](Running-the-Planner-on-a-Dynamic-Environment)
- [Point-to-point-navigation](#Point-to-point-navigation)

## Contacts
- Fatemeh Rastgar (fatemeh.rastgar2@gmail.com)
- Arun Kumar Singh (aks1812@gmail.com)

## Contributing 
We welcome contributions from anyone interested in helping us to implement this project in ROS2.

## General Requirements

To run the codes, you'll need the following dependencies:
- ROS
- Numpy
- Jax-Numpy (https://github.com/google/jax)
- Ubuntu
- Scipy
- mayavi(https://docs.enthought.com/mayavi/mayavi/installation.html)

We have also utilized the following libraries and repositories in our study:

| Use| Repo/library name | Links |
| --- | --- |---|
| General | Numpy |...|
| General | Scipy |...|
| General | Jax-Numpy|(https://github.com/google/jax)|
|Plot 3d trajectories|mayavi|(https://docs.enthought.com/mayavi/mayavi/installation.html)|
|gradient-based solver | FATROP |(git@gitlab.kuleuven.be:robotgenskill/fatrop/fatrop.git)| 
|gradient-based solver |ROCKIT |(https://gitlab.kuleuven.be/meco-software/rockit)|
|Sampling-based method and Jackal| log-MPPI |(https://github.com/IhabMohamed/log-MPPI_ros)|
|Holonomic robot |(robootont_description, robotont_gazebo)| (https://github.com/robotont)|
|sampling-based method|VPSTO|(https://github.com/AnishShr/Optimization_Robot_Motion-_Planning/tree/main/paper_vpsto)|

## Running the First Benchmark - Comparison on BARN Dataset

For the first benchmark, we conducted comparisons between several planners, including DWA, TEB, MPPI, log-MPPI, and our PRIEST method. Note that, for fair comparison, we have used 
the best possible implementations of all the baselines. 
To run these comparisons, follow these steps:

1- Launch PRIEST

roslaunch priest clearpath_launch.launch
      rosrun priest planner_holonomic.py

2- Launch DWA/TEB Planner
  
        roslaunch compare clearpath_launch.launch
        roslaunch compare map_less_navigation.launch
        rosrun compare send_goal.py

Note that in the comparison/launch/map_less_navigation.launch file, you can choose whether to use the TEB planner or the DWA planner. To use the planner, use:

<include file="$(find compare)/launch/move_base_teb.launch">
      
and to use the DWA planner:

<include file="$(find compare)/launch/move_base.launch">
      
3- Launch MPPI/ log-MPPI:

To execute this benchmark, we have adapted the mppi-control package to be compatible with the Robotont Clearpath platform. The parameters remain the same as in the mppi-control package, with adjustments made to specific topics for compatibility with Robotont Clearpath. Please navigate to the "additional folder" in this repository. Copy the files and place them in corresponding directories within the mppi-control package. Afterward, run the following commands

roslaunch priest clearpath_launch.launch
      roslaunch mppi-control control_stage_robotont.launch
      rosrun compare send_goal.py

Please change the value from "true" to "false" in the following command within the control_stage_robotont.launch file to select the log-MPPI or MPPI method:

## Running the Planner on a Dynamic Environment

For this benchmark, we conducted comparisons between several planners, including baseline CEM, DWA, TEB, MPPI, log-MPPI, and our PRIEST method.
To run these comparisons, follow these steps:

1- Launch PRIEST:

roslaunch priest nonhol_clearpath.launch
      rosrun priest planner_nonhol_dy.py

2- Launch CEM:

roslaunch priest nonhol_clearpath.launch
      rosrun priest planner_cem_dynamic.py

3- Launch TEB/DWA Planner

roslaunch compare clear_path_dynamic_2.launch 
       roslaunch compare map_less_navigation.launch
       rosrun compare send_obs_velocities.py
       rosrun compare send_goal.py

Note that to run this benchmark, some changes are needed:
 
 a. Modification in the Gazebo simulation configuration found in the `jackal.gazebo` file within the `jackal_description` package by commenting out the plugin named `robot_groundtruth_sim` and adding the `jackal_controller` plugin

<gazebo>
                <plugin name="jackal_controller" filename="libgazebo_ros_planar_move.so">
              <commandTopic>mppi/cmd_vel</commandTopic>
              <odometryTopic>ground_truth/odom</odometryTopic>
              <odometryFrame>odom</odometryFrame>
              <odometryRate>60.0</odometryRate>
              <robotBaseFrame>base_link</robotBaseFrame>
              <xyzOffsets>0 0 0</xyzOffsets>
              <rpyOffsets>0 0 0</rpyOffsets>
          </plugin>
            </gazebo>

b. Adding the following lines to `move_base_teb.launch` / ` move_base.launch`

c. Changing the `self.x_fin` and `self.y_fin` in `send_goal.py` to 0 and 15, respectively.

4- Launch MPPI/ log-MPPI

roslaunch compare clear_path_dynamic.launch 
      roslaunch mppi_control control_stage.launch
      rosrun compare send_obs_velocities.py
      rosrun compare send_goal.py

Please note that the following changes are required in send_goal.py

- Subscriber topic: "/odom" to "/ground_truth/odom"
  
- goal.target_pose.header.frame_id should be "/map"

Additionally, for a fair comparison, we set the time horizon of MPPI and log-MPPI as 10s in `mppi_params_sim.yaml`.

We use laser scans to observe obstacles, so make sure to update the following files in the Jackal package:
 The link for the the yaml file is [link](https://github.com/fatemeh-rastgar/PRIEST/blob/main/compare/config/mppi_params_sim.yaml)

- costmap_common_params.yaml

## Point-to-point-navigation
### 2D Comparison
To perform a 2D comparison, follow these steps:

1. Navigate to the `2d_comparison` folder:
2. Run the `main.py` script:

### 3D Comparison
To perform a 2D comparison, follow these steps:

1. Navigate to the `3d_comparison` folder:
2. Run the `main.py` script:

If you have any questions or need further assistance, please feel free to contact us.

PRIEST

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