PNGNav

This is the ROS implementation of NIRRT*-PNG (Neural Informed RRT* with Point-based Network Guidance) for TurtleBot navigation, which is the method in our ICRA 2024 paper

Neural Informed RRT*: Learning-based Path Planning with Point Cloud State Representations under Admissible Ellipsoidal Constraints

Zhe Huang, Hongyu Chen, John Pohovey, Katherine Driggs-Campbell

[Paper] [arXiv] [Main GitHub Repo] [Robot Demo GitHub Repo] [Project Google Sites] [Presentation on YouTube] [Robot Demo on YouTube]

All code was developed and tested on Ubuntu 20.04 with CUDA 12.0, ROS Noetic, conda 23.11.0, Python 3.9.0, and PyTorch 2.0.1. This repo provides the ROS package png_navigation which offers rospy implmentations on RRT*, Informed RRT*, Neural RRT*, and our NIRRT*-PNG for TurtleBot navigation. We offer instructions on how to use png_navigation in Gazebo simulation, and png_navigation can be readily applied in real world scenarios.

Citation

If you find this repo useful, please cite

@article{huang2023neural,
  title={Neural Informed RRT*: Learning-based Path Planning with Point Cloud State Representations under Admissible Ellipsoidal Constraints},
  author={Huang, Zhe and Chen, Hongyu and Pohovey, John and Driggs-Campbell, Katherine},
  journal={arXiv preprint arXiv:2309.14595},
  year={2023}
}

Setup

1. Run

cd ~/PNGNav
catkin_make

2. Run

conda env create -f environment.yml

3. Replace /home/zhe/miniconda3/ in the shebang line of scripts with your own system path. For example, if you are using ubuntu and miniconda3, and your account name is abc, replace /home/zhe/miniconda3/ with /home/abc/miniconda3.

4. Download the PointNet++ model weights for PNG, create the folder model_weights/ in PNGNav/src/png_navigation/src/png_navigation/wrapper/pointnet_pointnet2/, and move the downloaded pointnet2_sem_seg_msg_pathplan.pth to model_weights.

cd ~/PNGNav/src/png_navigation/src/png_navigation/wrapper/pointnet_pointnet2/
mkdir model_weights
cd model_weights
mv ~/Downloads/pointnet2_sem_seg_msg_pathplan.pth .

5. If you have map_realworld.pgm and map_realworld.yaml, move them to PNGNav/src/png_navigation/src/png_navigation/maps.

How to Run TurtleBot3 Gazebo Simulation

Simulation Setup

Follow tutorial on TurtleBot3 official website to install dependencies, and test TurtleBot3 gazebo simulation.

sudo apt-get install ros-noetic-joy ros-noetic-teleop-twist-joy \
  ros-noetic-teleop-twist-keyboard ros-noetic-laser-proc \
  ros-noetic-rgbd-launch ros-noetic-rosserial-arduino \
  ros-noetic-rosserial-python ros-noetic-rosserial-client \
  ros-noetic-rosserial-msgs ros-noetic-amcl ros-noetic-map-server \
  ros-noetic-move-base ros-noetic-urdf ros-noetic-xacro \
  ros-noetic-compressed-image-transport ros-noetic-rqt* ros-noetic-rviz \
  ros-noetic-gmapping ros-noetic-navigation ros-noetic-interactive-markers

sudo apt install ros-noetic-dynamixel-sdk
sudo apt install ros-noetic-turtlebot3-msgs
sudo apt install ros-noetic-turtlebot3

cd ~/PNGNav/src
git clone -b noetic-devel https://github.com/ROBOTIS-GIT/turtlebot3_simulations.git
cd ~/PNGNav && catkin_make

Instructions

0. Add the line below to ~/.bashrc.

export TURTLEBOT3_MODEL=waffle_pi

1. Launch Gazebo simulation.

cd ~/PNGNav
conda deactivate
source devel/setup.bash
roslaunch turtlebot3_gazebo turtlebot3_world.launch

2. Launch map_server and amcl for Turtlebot3. Note it is the launch file in our png_navigation package, which excludes launch of move_base and rviz.

cd ~/PNGNav
conda deactivate
source devel/setup.bash
roslaunch png_navigation turtlebot3_navigation.launch

3. Launch rviz.

cd ~/PNGNav
conda deactivate
source devel/setup.bash
roslaunch png_navigation rviz_navigation_static.launch

4. Estimate the pose of Turtlebot3 by teleoperation. After pose estimation, remember to kill turtlebot3_teleop_key.launch.

conda deactivate
roslaunch turtlebot3_teleop turtlebot3_teleop_key.launch

5. Launch the planning algorithm. After you see Global Planner is initialized., you can start planning on rviz by choosing navigation goal.

cd ~/PNGNav
conda deactivate
source devel/setup.bash
roslaunch png_navigation nirrt_star_c.launch

or any of these lines

roslaunch png_navigation nirrt_star.launch
roslaunch png_navigation nrrt_star.launch
roslaunch png_navigation irrt_star.launch
roslaunch png_navigation rrt_star.launch

How to Run Dynamic Obstacles Implementation

Here are the instructions to run the implementation with dynamic obstacles presented in the simulation. We exchange the terms dynamic obstacles and moving humans.

0. Add the line below to ~/.bashrc.

export TURTLEBOT3_MODEL=waffle_pi

1. Launch Gazebo simulation.

cd ~/PNGNav
conda deactivate
source devel/setup.bash
roslaunch turtlebot3_gazebo turtlebot3_world.launch

2. Launch map_server and amcl for Turtlebot3. Note it is the launch file in our png_navigation package, which excludes launch of move_base and rviz.

cd ~/PNGNav
conda deactivate
source devel/setup.bash
roslaunch png_navigation turtlebot3_navigation.launch

3. Launch rviz.

cd ~/PNGNav
conda deactivate
source devel/setup.bash
roslaunch png_navigation rviz_navigation_static.launch

4. Estimate the pose of Turtlebot3 by teleoperation. After pose estimation, remember to kill turtlebot3_teleop_key.launch.

conda deactivate
roslaunch turtlebot3_teleop turtlebot3_teleop_key.launch

5. Create moving humans.

cd ~/PNGNav
conda deactivate
source devel/setup.bash
rosrun png_navigation moving_humans_with_noisy_measurements.py

Add /dr_spaam_detections/PoseArray and /gt_human_positions/PoseArray to rviz.

6. Start human detector.

cd ~/PNGNav
conda deactivate
source devel/setup.bash
rosrun png_navigation human_checker_gazebo.py

7. Start running dynamic obstacle aware planning algorithm.

cd ~/PNGNav
conda deactivate
source devel/setup.bash
roslaunch png_navigation nrrt_star_dynamic_obstacles.launch

or

cd ~/PNGNav
conda deactivate
source devel/setup.bash
roslaunch png_navigation nirrt_star_c_dynamic_obstacles.launch

Notes:

• Change moving human speed by changing vx, vy in PNGNav/src/png_navigation/scripts_dynamic_obstacles/moving_humans_with_noisy_measurements.py.
• change the human detection radius of robot by changing human_detection_radius in PNGNav/src/png_navigation/scripts_dynamic_obstacles/human_checker_gazebo.py.

Real World Deployment on TurtleBot 2i

We use CrowdNav_Sim2Real_Turtlebot to deploy the planning algorithms in png_navigation on a TurtleBot 2i. In addition, remember to comment the line with # gazebo and uncomment the line with # real world shown below in local_planner_node.py and local_planner_node_check.py in this repo for real world deployment.

self.cmd_vel = rospy.Publisher('cmd_vel', Twist, queue_size=5) # gazebo
# * self.cmd_vel = rospy.Publisher('cmd_vel_mux/input/teleop', Twist, queue_size=5) # real world

How to Create Your Own Map Yaml File

1. After you finish SLAM and save the map as .pgm, you will also get a yaml file. Edit the file which look like this.

image: /home/png/map_gazebo.pgm
resolution: 0.010000
origin: [-10.000000, -10.000000, 0.000000]
negate: 0
occupied_thresh: 0.65
free_thresh: 0.196
setup: 'world'
free_range: [-2, -2, 2, 2]
circle_obstacles: [[1.1, 1.1, 0.15],
                   [1.1, 0, 0.15],
                   [1.1, -1.1, 0.15],
                   [0, 1.1, 0.15],
                   [0, 0, 0.15],
                   [0, -1.1, 0.15],
                   [-1.1, 1.1, 0.15],
                   [-1.1, 0, 0.15],
                   [-1.1, -1.1, 0.15]]
rectangle_obstacles: []

The format of fields are as follows.

free_range_pixel: [xmin, ymin, xmax, ymax]
circle_obstacles: [[x_center_1, y_center_1, r_1],
                   [x_center_2, y_center_2, r_2],
                   [x_center_3, y_center_3, r_3],
                   ...,
                   [x_center_n, y_center_n, r_n]]
rectangle_obstacles: [[xmin_1, ymin_1, xmax_1, ymax_1],
                      [xmin_2, ymin_2, xmax_2, ymax_2],
                      [xmin_3, ymin_3, xmax_3, ymax_3],
                      ...,
                      [xmin_n, ymin_n, xmax_n, ymax_n]]

2. Move .pgm and edited .yaml files to PNGNav/src/png_navigation/src/png_navigation/maps. Keep their names the same, for example abc.pgm and abc.yaml. When running the launch file, run

roslaunch png_navigation nirrt_star_c.launch map:=abc

3. You can keep the other fields the same and leave them there. Here is the reference to what these fields mean. We use ros_map_editor to locate the pixels of obstacle keypoints which define the geometry, and then transform from pixel coordinates to world positions. If you are also going to transform from pixel map to get the geometric configurations in the real world, you can use functions get_transform_pixel_to_world and pixel_to_world_coordinates from PNGNav/src/png_navigation/src/png_navigation/maps/map_utils.py.

Required fields:

    image : Path to the image file containing the occupancy data; can be absolute, or relative to the location of the YAML file

    resolution : Resolution of the map, meters / pixel

    origin : The 2-D pose of the lower-left pixel in the map, as (x, y, yaw), with yaw as counterclockwise rotation (yaw=0 means no rotation). Many parts of the system currently ignore yaw.

    occupied_thresh : Pixels with occupancy probability greater than this threshold are considered completely occupied.

    free_thresh : Pixels with occupancy probability less than this threshold are considered completely free.

    negate : Whether the white/black free/occupied semantics should be reversed (interpretation of thresholds is unaffected) 

How to Transfer to Your Mobile Robot

1. Modify robot_config in PNGNav/src/png_navigation/src/png_navigation/configs/rrt_star_config.py. For now, only robot_config.clearance_radius matters to global planning.
2. Modify src/png_navigation/scripts/local_planner_node.py. In particular, make sure self.cmd_vel, self.odom_frame, and self.base_frame of class LocalPlanner match your robot setup. Tune parameters as needed.