asar_hybrid_tmp

Overview

This repository contains action servers to control two 7DoF manipulators and simulation environments for suturing tasks. The work is a component of a research paper titled "A Hybrid Framework Combining Task-Motion Planning and Dynamic Behavior Trees for Minimally Invasive Surgery".

Examples of Suturing Tasks

Dependencies

This project depends on several packages for controlling the 7DoF manipulator, the forceps, and the simulation environment:

• Python3 for ROS Noetic (Ubuntu 20.04)

• asar_robot_control_packages - https://github.com/husikl/asar_robot_control_packages.git

• dbt_ros - https://github.com/husikl/dbt_ros.git

• CoppeliaSim v4.3.0 or later

• OMPL with Python bindings

• PddlStream library - https://github.com/caelan/pddlstream.git

• Pinocchio library built with FCL. Execute the following command to build Pinocchio with FCL:

    ```bash
    cmake .. -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=/usr/local -DPYTHON_EXECUTABLE=/usr/bin/python3 -DBUILD_PYTHON_INTERFACE=ON -DBUILD_WITH_URDF_SUPPORT=ON -DBUILD_WITH_CASADI_SUPPORT=ON -DBUILD_WITH_COLLISION_SUPPORT=ON
    ```
  

Installation

(TO-DO section)

Usage

1. Launch a CoppeliaSim environment from the sim folder (e.g., stitch_mts_bm2.ttt). This includes two PSMS attached to RCMs, a tissue phantom, and a surgical needle. Feel free to adjust the RCM locations.
2. To initialize a single PSM (robotic arm with surgical tool attached), run roslaunch asar_hybrid_tmp asar_test_ompl.launch.
3. For the two PSM case, run roslaunch asar_hybrid_tmp asar_test_ompl_arm2.launch. The above commands initialize the arms, assuming the simulation is already running.
4. To set the RCM, adjust the values for: <arg name="trocar_x" value="0.4205"/> <arg name="trocar_y" value="0.250"/> <arg name="trocar_z" value="0.201"/>. Note that the frame of reference for both arms is with respect to each arm's base link.
5. After initializing the arms, you can send a goal target to each arm by sending a goal to the action server: rostopic pub /unit0/arm_grasp_server/goal. Press tab after this and adjust the command field in the resulting full message type (e.g., command : 'grasp'). This will trigger the grasp sampling and motion planning to grasp the needle.

To run TMP with pddlstream (assumes that pddlstream is built), follow these steps:

1. Navigate to the pddlstream directory and copy the folder tmp_suturing from this repo to "/pddlstream_directory/examples/".
2. From the root directory for pddlstream, you can initialize the ROS action service for the TMP planner by running python -m examples.tmp_suturing.run in a new terminal.

To run the DBT planner and executor, use the following commands in two separate terminals (assuming that dbt_ros is built):

1. To run the executor node (which waits for BT and executes it once received), enter rosrun dbt_ros bt_executor.
2. To run the DBT tree generator, enter rosrun dbt_ros pddl_to_bt_service.py.

To run some of the suturing benchmarks, select the function in suture_benchmark.py. For example, to use def benchmark_loop(self,) on line 864, comment or uncomment the function to benchmark the motion and run rosrun asar_hybrid_tmp suture_benchmark.py.

To start the benchmark in a new terminal, use the command rosservice call /start_benchmark "{}".

TODO: Combine nodes to run the benchmark and create JSON-based config selection to run different tasks.