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Contents

  1. Authors
  2. Required packages - Kinetic Version
  3. Easy install
  4. Run GGCNN in Gazebo and RVIZ
  5. Connecting with real UR5

1.0 - Authors

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*LaR - Laboratório de Robótica, Departamento de Engenharia Elétrica e de Computação, Universidade Federal da Bahia, Salvador, Brasil

**PPGM - Programa de Pós-Graduação em Mecatrônica, Universidade Federal da Bahia, Salvador, Brasil.


2.0 - Required packages - Kinetic Version

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This code was developed with Python 2.7 on Ubuntu 16.04 with ROS Kinetic.

It is recommended to follow the EASY INSTALL step below

NOTE: This package should be placed into your src folder. Please open an issue if you find any problem related to this package.

In order to run the experiment you will need a trained .params (Contact the authors to download the .params) Create a folder called params inside the selective_grasping folder and put the .params there. Remember to rename the .params files and related variables in detect.py


3.0 - Easy install

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In order to install all the required packages easily, create a new catkin workspace

mkdir -p ~/catkin_ws_new/src

Clone this repository into the src folder

cd ~/catkin_ws_new/src
git clone https://github.com/caiobarrosv/selective_grasping

Run the install.sh file

cd ~/catkin_ws_new/src/selective_grasping/install
./install.sh

4.0 - Run the experiments in Gazebo

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THE GAZEBO SIMULATION IS NOT WORKING PROPERLY. THE GRASPS ARE UNSTABLE AND NEED TO BE FIXED. THE SIMULATION IS USED ONLY TO RUN EXPERIMENTS PRIOR TO THE REAL WORLD APPLICATION TO AVOID ACCIDENTS

Please follow each following steps:

4.1 - Launch Gazebo:

roslaunch selective_grasping gazebo_ur5.launch

4.2 - Run the UR5 control node Press enter after the following message appears and jump to the step 4.3:

"==== Press enter to move the robot to the 'depth cam shot' position!"

rosrun selective_grasping ur5_open_loop.py --gazebo

4.3 - Run the SSD node

rosrun selective_grasping detect.py --gazebo

4.4 - Run the GG-CNN node Press enter after the following message appears and jump to the step 4.5: "Press enter to start the GGCNN"

rosrun selective_grasping run_ggcnn.py --gazebo

4.5 - Spawn the objects in the workspace

rosrun selective_grasping spawn_objects.py

4.6 - Detect the april tags in the environment This node will generate a frame for the identified tag in the environment

rosrun selective_grasping april_tag_tf_generator.py

4.7 - UR5 control node After running the GG-CNN node you are able to move the robot and perform the grasp using the terminal provided by step 4.2

4.8 - Change the Gazebo properties (OPTIONAL) It will speed up your Gazebo simulation a little bit :)

rosrun selective_grasping change_gazebo_properties.py

4.9 - Visualize the images published by the GG-CNN You might want to see the grasp or any other image. In order to do that, you can use the rqt_image_view.

rosrun rqt_image_view

4.9 - Visualize depth cloud in RVIZ If you want to visualize the data being published by the Intel Realsense D435 please run the following node:

rosrun selective_grasping rviz_ur5.launch

5.0 Connecting with real UR5

5.1 - Use the following command in order to connect with real UR5. If you are using velocity control, do not use bring_up. Use ur5_ros_control instead.

roslaunch selective_grasping ur5_ros_control.launch robot_ip:=192.168.131.13

5.2 - Launch the real Intel Realsense D435

roslaunch selective_grasping rs_d435_camera.launch

5.3 - Launch the gripper control node

rosrun robotiq_2f_gripper_control Robotiq2FGripperRtuNode.py /dev/ttyUSB0

5.4 - Launch the ggcnn node

rosrun selective_grasping run_ggcnn_ur5.py --real

5.5 - Launch the main node of the Intel Realsense D435

rosrun selective_grasping command_GGCNN_ur5.py

5.6 - If you want to visualize the depth or point cloud, you can launch RVIZ

roslaunch selective_grasping rviz_ur5.launch

5.7 - Firstly check the machine IP. The IP configured on the robot must have the last digit different.

ifconfig

Disable firewall

sudo ufw disable

Set up a static IP on UR5 according to the following figure

config

Set up a connection on Ubuntu according to the following figure

config_ethernet2

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A selective grasping approach developed in ROS

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