Generalizable whole-body global manipulation of deformable linear objects by dual-arm robot in 3-D constrained environments

[Project website]

Repository for the paper Generalizable whole-body global manipulation of deformable linear objects by dual-arm robot in 3-D constrained environments, IJRR, 2024.

Here we provide:

• The code of the proposed manipulation algorithm, which is mainly written in C++.

• A simulator built in Unity for simulating the manipulation and demonstrating the performance.

Installation

1. Install Unity for Linux 2020.03 doc (First install Unity Hub, then install Unity Editor 2020.03).

2. Install ROS Noetic.

3. Install Eigen 3.4.0 doc. (Note: required verion >= 3.4.0)

4. Install ceres 2.1.0 doc.

5. Install Ipopt doc.

6. Install ifopt doc.

7. Install cnpy doc.

8. Install yaml-cpp 0.6.2 doc if it is not installed on your system. (The version 0.7.0 contains a bug which makes find_package(yaml-cpp) not work correctly.)

9. Make sure the default python of the system is python3 (and not in conda virtual env). You can set python3 as the default python using

   $ sudo apt install python-is-python3

10. Install the following dependences in the system's python3:

    pip install empy
    pip install numpy

11. Install the following dependences in your conda python3 env (<= python 3.8.10):

    pip install rospkg
    pip install scipy
    pip install matplotlib
    pip install scikit-learn

12. Clone the repo:

    $ git clone https://github.com/Mingrui-Yu/DLO_planning_2.git

13. Clone third-party ROS packages:

    $ cd <YOUR_PATH>/DLO_planning_2_private/ws_dlo/src
    
    $ git clone https://github.com/THU-DA-Robotics/universal_robot.git -b calibration_devel
    $ git clone https://github.com/THU-DA-Robotics/robotiq.git -b noetic-devel
    $ git clone https://github.com/THU-DA-Robotics/dual_ur.git -b noetic_devel
    
    $ git clone https://github.com/Unity-Technologies/ROS-TCP-Endpoint.git

14. Install custom MoveIt! in ws_dlo/src/moveit_all. doc

15. Install other dependences:

    cd <YOUR_PATH>/DLO_planning_private/ws_dlo
    rosdep install --from-paths src --ignore-src -y

16. Build the catkin workspaces:

    cd DLO_planning_private/ws_dlo
    catkin_make -j8

17. Modify the project_dir in ws_dlo/src/dlo_arm_planning_pkg/config/sim/configs.yaml to .

18. Install Unity Robotics Hub into this Unity project doc.

Usage in Simulation

In each following terminal:

cd <YOUR_PATH>/ws_dlo/
source devel/setup.bash

In a new terminal:

roslaunch ros_tcp_endpoint endpoint.launch tcp_ip:=127.0.0.1 tcp_port:=10000

Then, in a new terminal:

roslaunch dlo_arm_planning_pkg sim_prepare.launch

Then, in a new terminal:

rosrun dlo_arm_planning_pkg <EXECUTABLE_FILE>

Available executable files

First, follow the above usage instructions.

An example to visualize the stable DLO configuration projection

# save the results
rosrun dlo_arm_planning_pkg example_vis_derm_projection

# visualize the results
# in your conda python3 env
python src/dlo_arm_planning_pkg/scripts/results/plot_derm_projection.py

An example to test the planner without using Unity

Test the planner in a custom planning scene for tens of times and print the statistic results.

rosrun dlo_arm_planning_pkg example_planner_without_unity

Evaluating the planner in the simulated Tasks

1. Launch the Unity Editor.

2. Click the Run buttion in the Unity Editor.

3. Run the cpp file:

   rosrun dlo_arm_planning_pkg eval_planner

   You can change the scene id (task id) and whether visualizing the start goal configurations / planned paths in ws_dlo/src/dlo_arm_planning_pkg/app/eval_planner.cpp. Remeber to re-catkin_make the project after any cpp modification.

4. Compute and print the statistic results of all planned paths:

   # in your conda python env
   python src/dlo_arm_planning_pkg/scripts/results/eval_planner.py

   You can change the scene id (task id) in the python script.

Evaluating the controller in the simulated Tasks

1. Launch the Unity Editor.

2. Click the Run buttion in the Unity Editor.

3. Run the cpp file:

   rosrun dlo_arm_planning_pkg eval_controller

   You can change the scene id (task id) and whether planning new paths in ws_dlo/src/dlo_arm_planning_pkg/app/eval_controller.cpp. Remeber to re-catkin_make the project after any cpp modification.

4. Compute and print the statistic results of all planned paths:

   # in your conda python env
   python src/dlo_arm_planning_pkg/scripts/results/eval_controller.py

   You can change the scene id (task id) in the python script.

Struction of the evalution data of the simulated Tasks

• data/sim/scene_<id>/:

  • dlo_identification:
  • dlo_derm_params.yaml: the identificated DLO DER model parameters.
  • all_dlo_end_0_quat.npy & all_dlo_end_1_quat.npy & all_dlo_fps_pos.npy: the recorded DLO configurations during the identification trajectory.
  • task_0:
  • start/ & goal/: the pre-defined start and goal configurations.
  • path_initial_to_start/: (irrelevant to the proposed method); the path from the initial configuration in Unity (a straight line) to the start configuration, only used in actual executions.
  • results/:
    • <name>: the name of the specific planning setup.
    • path_<id>:
      • smoothed_path/: the planned path.
      • planning_details.yaml: the planning details.
      • <execution_name>: the name of the execution setup.
      • execution_<id>:
        • execution_details.yaml: the execution details.
        • reference_path: the interpolated planned path, which is used as the reference path of the MPC.
        • actual_path: the path of actual execution.