PR2 Robot 3D Perception with MoveIt! Motion planning framework

This project gives the PR2 robot the ability to locate an object in a cluttered environment, pick it up and then move it to some other location. This is an interesting problem to solve and is a challenge at the forefront of the robotics industry today. The project uses a perception pipeline to identify target objects from a so-called “Pick-List” in that particular order, pick up those objects and place them in corresponding drop-boxes.

Note : The perception pipeline for this project is explained in PART1 and PART2 of another repo here. The Moveit!Motion planning framework source files can be found in the pr2_moveit directory.

1. Setup

2. Project Heads Up

3. Pick and Place Simulate

Setup

1. Move to the /src directory of your active ROS workspace and clone the project files.

$ cd ~/catkin_ws/src
$ git clone https://github.com/ashutoshtiwari13/PR2Robot-3D-Perception.git

2. Install missing dependencies using rosdep install.

$ cd ~/catkin_ws
$ rosdep install --from-paths src --ignore-src --rosdistro=kinetic -y

3. Build the project.

$ cd ~/catkin_ws
$ catkin_make

4. Add following to your .bashrc file

$ export GAZEBO_MODEL_PATH=~/catkin_ws/src/PR2Robot-3D-Perception/pr2_robot/models
$ source ~/catkin_ws/devel/setup.bash

Note : source the ~/.bashrc file too to avoid errors.

5. See the project scenario using

$ roslaunch pr2_robot pick_place_project.launch

6. To run the demo

$ cd ~/catkin_ws/src/PR2Robot-3D-Perception/pr2_robot/scripts
$ chmod u+x pr2_safe_spawner.sh
$ ./pr2_safe_spawner.sh

Project Heads Up

• PART1 of the repo here explains the methods used for Segmentation of the different items at the table top using point cloud library.
• PART2 of the above repo uses SVM algorithm to train the classfier to detect objects and both together form what we call a Perception pipeline.
• pcl_callback() is the function that will be called back every time a message is published to /pr2/world/points topic. This function has the 3D point cloud perception pipeline, object detection, and a call to the PR2 mover function.
• Last part of pcl_callback() function is to call the PR2 mover to pick and place detected objects.

  if len(detected_objects)>0:
        try:
            pr2_mover(detected_objects)
        except rospy.ROSInterruptException:
            pass
    else:
        rospy.logwarn('No detected objects !!!')

• pr2_mover is the main fucntion controlling the PR2 Arm It performs the following major fucntions
  • Initializes variables including ROS messages.
  • Get Parameters for ROS parameters server by reading objects list and drop box data from ROS parameters server.
  • Rotate PR2 in place to capture side tables for the collision map. This is to rotate PR2 to capture side tables data to avoid collision.
  • Calculate objects Centroid (x,y,z) of each detected object based on its points array.
  • Loop through the pick list. In this loop each object is picked from the pick-list we received through ROS parameter server and match it to one of the detected objects to decide on pick pose, place pose, and arm name and write all data to yaml file.
  • Writing the yaml files to disk

Pick and Place Simulate

For this project, we have a variety of different objects to identify. There are 3 different worlds

• test1.world
• test2.world
• test3.world

Each of the three worlds has different items on the table in front of the robot. These worlds are located in the /pr2_robot/ worlds/. Object recognition can be performed base on the train model.sav generated from PART2 of the mini-projetc described here. The program read each pick list which are pick_list1.yaml pick_list_2.yaml and pick_list_3.yaml and display the name of each objects.

Running the 3 world tests

The required test world is selected by editing the following lines in pick_place_project.launch

<arg name="world_name" value="$(find pr2_robot)/worlds/test1.world"/>

and

<rosparam command="load" file="$(find pr2_robot)/config/pick_list_1.yaml"/>

First World Output

For the first world, PR2 was able to identify all 3 items which are the soap sop2 and biscuits. See output_1.yaml located in the /pr2_robot/output/

Second World Output

For the second world, PR2 was able to identify 5 out of 5 items which are soap2 book soap and biscuits glue.See output_2.yaml located in the /pr2_robot/output/

Third World Output

For the third world, PR2 was able to identify all 8 objects. See output_3.yaml located in the /pr2_robot/output/