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[[Tutorial using ROS for ver.3]] * Grasping with TurtleBot3 automatically [#e93a5390] This sample is a simple sample that automatically grips an object. This example is a simple example that automatically grasps an object. TurtleBot3 has OpenManipulator. Please refer to [[here>http://emanual.robotis.com/docs/en/platform/turtlebot3/manipulation/#manipulation]] for the specifications of Open Manipulator. ~ Please refer to [[here>http://emanual.robotis.com/docs/en/platform/turtlebot3/appendix_realsense/]] for camera specifications. Object recognition by darknet_ros (YOLO) and Point Cloud data are used. ~ In addition, CUDA must be installed in the Ubuntu environment in order to perform YOLO object recognition. ~ Although it is possible to operate using the CPU without installing CUDA, the object recognition speed is very slow. So CUDA must be installed in the Ubuntu side in order to recognize objects using YOLO. ~ Although it is possible to check this example scene using CPU without installing CUDA, the object recognition speed is very slow. The outline of the operation is as follows. + Move the TurtleBot3 to the front of the object by key operation. + Move the TurtleBot3 so that the gripping target appears as close to the center of the color image as possible. + Specify the object to be gripped by key operation. + Move the TurtleBot3 to the front of the target object by keyboard operation. + Move the TurtleBot3 so that the target object comes to the center of the color image. + Select the target object by keyboard operation. When the above operation is performed, an attempt is made to grip the object in the following flow. When the above operation is performed, TurtleBot3 tries to grasp the object as follows: + darknet_ros (YOLO) performs object recognition using the color image output by TurtleBot3. + Estimate the 3D coordinates of an object using the position of the object in the color image and Point Cloud information. + Grasp the calculated 3D coordinate position. + The darknet_ros (YOLO) performs object recognition using a color image. + Estimate the 3D position of the target object using color images and Point Cloud information. + Grasp the calculated 3D position. Please refer to [[here>http://emanual.robotis.com/docs/en/platform/turtlebot3/manipulation/#manipulation]] for the specifications of Open Manipulator. ~ Please refer to [[here>http://emanual.robotis.com/docs/en/platform/turtlebot3/appendix_realsense/]] for camera specifications. ~ Also, since it was more convenient for the camera to have the shortest distance from the depth sensor, use RealSense SR300 instead of RealSense R200. Please refer to [[here>http://emanual.robotis.com/docs/en/platform/turtlebot3/appendix_realsense/]] for camera specifications.~ Because it is convenient to shorten the minimum distance of the depth sensor, RealSense SR300 is used instead of RealSense R200. ** Build Ubuntu environment [#c709f1fe] In this sample, darknet_ros (YOLO) needs to be installed in Ubuntu environment. ~ It is recommended to check the operation of [[normal darknet> https://github.com/pjreddie/darknet]] without using ros. ~ ''The following darknet_ros has been confirmed to work with version 1.1.4. '' In this example, darknet_ros (YOLO) needs to be installed in Ubuntu environment. ~ It is recommended to check the operation of [[normal darknet>https://github.com/pjreddie/darknet]] without using ros. ~ ''We confirmed that it works with darknet_ros version 1.1.4.'' The procedure is as follows + Download and install CUDA from [[here] https://developer.nvidia.com/cuda-downloads]]. (As mentioned above, it is possible to run using the CPU without installing CUDA) ~ The procedure is as follows: + Download and install CUDA from [[here>https://developer.nvidia.com/cuda-downloads]]. (As mentioned above, it is also possible to run using the CPU without installing CUDA) ~ (For details, please adjust according to your PC and NVIDIA Driver environment.) + Git clone darknet_ros. ([[Reference> https://github.com/leggedrobotics/darknet_ros]]) ~ However, please check out the committed commit and download additional data. + Git clone darknet_ros. ([[Reference>https://github.com/leggedrobotics/darknet_ros]]) ~ However, please check out the specific commit and download additional data. $ cd ~/catkin_ws/src $ git clone --recursive https://github.com/leggedrobotics/darknet_ros.git $ git checkout ac666ab8e8e3dd23a8a95d891fb90874e63c8cb5 $ cd ~/catkin_ws/src/darknet_ros/darknet_ros/yolo_network_config/weights/ $ wget http://pjreddie.com/media/files/yolo.weights +darknet_rosをインストールする。（[[参考>https://github.com/leggedrobotics/darknet_ros]]） + Install darknet_ros. ([[Reference>https://github.com/leggedrobotics/darknet_ros]]) $ cd ~/catkin_ws $ catkin_make -DCMAKE_BUILD_TYPE=Release ** Startup Procedure [#bc27b1e9] First, start Ubuntu. Then start Windows. Start the Ubuntu side and then the Windows side. *** Ubuntu side startup procedure [#l39593fd] Open a new terminal and run the following command: $ roslaunch sigverse_turtlebot3_open_manipulator grasping_auto.launch $ roslaunch sigverse_turtlebot3_open_manipulator grasping_auto.launch *** Windows startup procedure [#df821e14] *** Windows side startup procedure [#df821e14] Start the [Assets/SIGVerse/SampleScenes/Turtlebot3/OpenManipulatorSR300(.unity)] scene with reference to [[here>Tutorial using ROS for ver.3#open_scene]]. ** Run [#sd88b953] You can operate TurtleBot3 by key operation on the terminal named grasping_auto on Ubuntu side. You can operate TurtleBot3 by operating the keyboard on the terminal named grasping_auto on Ubuntu side. ~* Check the terminal for details of the operation. If you want to finish, stop the Unity side and then the ROS side. Ubuntu side #ref(TurtleBot3GraspAutoUbuntu.png) Windows side #ref(TurtleBot3GraspAutoWindows.png) Windows side (grasped "clock") #ref(TurtleBot3GraspAutoWindowsGrasping.png)
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