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[ROS 1] Applications


  • This instructions were tested on Ubuntu 16.04 and ROS Kinetic Kame.
  • This instructions are supposed to be running on the remote PC. Please run the instructions below on your Remote PC. However, the part marked [TurtleBot] is the content that runs on SBC of TurtleBot3.
  • Make sure to run the Bringup instructions before use of the instruction

TIP: The terminal application can be found with the Ubuntu search icon on the top left corner of the screen. The shortcut key for running the terminal is Ctrl-Alt-T.

This chapter shows some demos using TurtleBot3. In order to implement these demos, you have to install the turtlebot3_applications and turtlebot3_applications_msgs packages.

[Remote PC] Go to catkin workspace directory (/home/(user_name)/catkin_ws/src) and clone the turtlebot3_applications and turtlebot3_applications_msgs repository. Then run the catkin_make to build the new packages.

$ sudo apt-get install ros-kinetic-ar-track-alvar
$ sudo apt-get install ros-kinetic-ar-track-alvar-msgs
$ cd ~/catkin_ws/src
$ git clone https://github.com/ROBOTIS-GIT/turtlebot3_applications.git
$ git clone https://github.com/ROBOTIS-GIT/turtlebot3_applications_msgs.git
$ cd ~/catkin_ws && catkin_make

TurtleBot Follower Demo


  • The follower demo was implemented only using a 360 Laser Distance Sensor LDS-01. a classification algorithm is used based on previous fitting with samples of person and obstacles positions to take actions. It follows someone in front of the robot within a 50 centimeter range and 140 degrees.
  • Running the follower demo in an area with obstacles may not work well. Therefore, it is recommended to run the demo in an open area without obstacles.

[TurtleBot] In order to run this demo, parameter in LIDAR launch file has to be modified. In the below example, Pluma is used to edit the launch file. In the param tag with frame_id as a name, replace base_scan to odom and save the file as shown in the below images.

$ pluma ~/catkin_ws/src/turtlebot3/turtlebot3_bringup/launch/turtlebot3_lidar.launch

NOTE: Turtlebot Follower Demo requires scikit-learn, NumPy and ScyPy packages.

[Remote PC] Install scikit-learn, NumPy and ScyPy packages with below commands.

$ sudo apt-get install python-pip
$ sudo pip install -U scikit-learn numpy scipy
$ sudo pip install --upgrade pip

[Remote PC] When installation is completed, run roscore on the remote pc with below command.

$ roscore

[TurtleBot] Launch the bringup

$ roslaunch turtlebot3_bringup turtlebot3_robot.launch

[Remote PC] Launch turtlebot3_follow_filter with below command.

$ roslaunch turtlebot3_follow_filter turtlebot3_follow_filter.launch

[Remote PC] Launch turtlebot3_follower with below command.

$ roslaunch turtlebot3_follower turtlebot3_follower.launch

TurtleBot Panorama Demo


  • The turtlebot3_panorama demo uses pano_ros for taking snapshots and stitching them together to create panoramic image.
  • Panorama demo requires to install raspicam_node package. Instructions for installing this package can be found at Gihub Link
  • Panorama demo requires to install OpenCV and cvbridge packages. Instructions for installing OpenCV can be found at OpenCV Tutorial Link

[TurtleBot] Launch the turtlebot3_rpicamera file

$ roslaunch turtlebot3_bringup turtlebot3_rpicamera.launch

[Remote PC] Launch panorama with below command.

$ roslaunch turtlebot3_panorama panorama.launch

[Remote PC] To start the panorama demo, please enter below command.

$ rosservice call turtlebot3_panorama/take_pano 0 360.0 30.0 0.3

Parameters that can be sent to the rosservice to get a panoramic image are:

[Remote PC] To view the result image, please enter below command.

$ rqt_image_view image:=/turtlebot3_panorama/panorama

Automatic Parking


  • The turtlebot3_automatic_parking demo was using a 360 laser Distance Sensor LDS-01 and a reflective tape. The LaserScan topic has intensity and distance data from LDS. The TurtleBot3 uses this to locate the reflective tape.
  • The turtlebot3_automatic_parking demo requires NumPy package.

[Remote PC] Install NumPy package with below commands. If you already installed numpy, you can skip below commands.

$ sudo apt-get install python-pip
$ sudo pip install -U numpy
$ sudo pip install --upgrade pip

[Remote PC] Run roscore.

$ roscore

[TurtleBot] Bring up basic packages to start TurtleBot3 applications.

$ roslaunch turtlebot3_bringup turtlebot3_robot.launch

[Remote PC] If you use TurtleBot3 Burger, set the model of TurtleBot3 like command below.

TIP: Before executing this command, you have to specify the model name of TurtleBot3. The ${TB3_MODEL} is the name of the model you are using in burger, waffle, waffle_pi. If you want to permanently set the export settings, please refer to Export TURTLEBOT3_MODEL page.


[Remote PC] Run RViz.

$ roslaunch turtlebot3_bringup turtlebot3_remote.launch
$ rosrun rviz rviz -d `rospack find turtlebot3_automatic_parking`/rviz/turtlebot3_automatic_parking.rviz

[Remote PC] Launch the automatic parking file.

$ roslaunch turtlebot3_automatic_parking turtlebot3_automatic_parking.launch  

Automatic Parking Vision


  • The turtlebot3_automatic_parking_vision uses raspberry pi camera and so the robot which is a default platform used for this demo is TurtleBot3 Waffle Pi. Since it parks from finding out AR marker on some wall, printed AR marker should be prepared. Whole process uses the image get from the camera, so if the process is not well being done, configure the parameters, such as brightness, contrast, etc.
  • The turtlebot3_automatic_parking_vision uses rectified image based on image_proc nodes. To get rectified image, the robot should get optic calibration data for raspberry pi camera. (Every downloaded turtlebot3 packages already have the camera calibration data as raspberry pi camera v2 default.)
  • The turtlebot3_automatic_parking_vision package requires ar_track_alvar package.

[Remote PC] Run roscore.

$ roscore

[TurtleBot] Bring up basic packages to start TurtleBot3 applications.

$ roslaunch turtlebot3_bringup turtlebot3_robot.launch

[TurtleBot] Start the raspberry pi camera nodes.

$ roslaunch turtlebot3_bringup turtlebot3_rpicamera.launch

[Remote PC] Raspberry pi package will publish compressed type image for fast communication. However, what will be needed in image rectification in image_proc node is raw type image. Hence, compressed image should be transform to raw image.

$ rosrun image_transport republish compressed in:=raspicam_node/image raw out:=raspicam_node/image

[Remote PC] Then, the image rectification should be carried out.

$ ROS_NAMESPACE=raspicam_node rosrun image_proc image_proc image_raw:=image _approximate_s=true _queue_size:=20

[Remote PC] Now should start the AR marker detection. Before running related launch file, the model of what will be used by this example code should be exported. After running the launch file, RViz will be automatically run under preset environments.

$ export TURTLEBOT3_MODEL=waffle_pi
$ roslaunch turtlebot3_automatic_parking_vision turtlebot3_automatic_parking_vision.launch

The contents in e-Manual can be updated without a previous notice. Therefore, some video may differ from the contents in e-Manual.

Load Multiple TurtleBot3s

NOTE: This application must be set firmware version 1.2.1 or higher.

[Remote PC] Run roscore.

$ roscore

Bringup multiple turtlebot3s with different namespace. We recommend the namespace includes common words such as tb3_0, tb3_1 or my_robot_0, my_robot_1

[TurtleBot(tb3_0)] Bring up basic packages with ROS NAMESPACE for nodes, multi_robot_name for tf prefix and set_lidar_frame_id for lidar frame id. This parameters must be the same.

$ ROS_NAMESPACE=tb3_0 roslaunch turtlebot3_bringup turtlebot3_robot.launch multi_robot_name:="tb3_0" set_lidar_frame_id:="tb3_0/base_scan"

[TurtleBot(tb3_1)] Bring up basic packages with ROS NAMESPACE for nodes, multi_robot_name for tf prefix and set_lidar_frame_id for lidar frame id. This parameters must be the same but different other robots.

$ ROS_NAMESPACE=tb3_1 roslaunch turtlebot3_bringup turtlebot3_robot.launch multi_robot_name:="tb3_1" set_lidar_frame_id:="tb3_1/base_scan"

Then the terminal you launched tb3_0 will represents below messages. You can watch TF messages have prefix tb3_0


 * /rosdistro: kinetic
 * /rosversion: 1.12.13
 * /tb3_0/turtlebot3_core/baud: 115200
 * /tb3_0/turtlebot3_core/port: /dev/ttyACM0
 * /tb3_0/turtlebot3_core/tf_prefix: tb3_0
 * /tb3_0/turtlebot3_lds/frame_id: tb3_0/base_scan
 * /tb3_0/turtlebot3_lds/port: /dev/ttyUSB0

    turtlebot3_core (rosserial_python/serial_node.py)
    turtlebot3_diagnostics (turtlebot3_bringup/turtlebot3_diagnostics)
    turtlebot3_lds (hls_lfcd_lds_driver/hlds_laser_publisher)


process[tb3_0/turtlebot3_core-1]: started with pid [1903]
process[tb3_0/turtlebot3_lds-2]: started with pid [1904]
process[tb3_0/turtlebot3_diagnostics-3]: started with pid [1905]
[INFO] [1531356275.722408]: ROS Serial Python Node
[INFO] [1531356275.796070]: Connecting to /dev/ttyACM0 at 115200 baud
[INFO] [1531356278.300310]: Note: publish buffer size is 1024 bytes
[INFO] [1531356278.303516]: Setup publisher on sensor_state [turtlebot3_msgs/SensorState]
[INFO] [1531356278.323360]: Setup publisher on version_info [turtlebot3_msgs/VersionInfo]
[INFO] [1531356278.392212]: Setup publisher on imu [sensor_msgs/Imu]
[INFO] [1531356278.414980]: Setup publisher on cmd_vel_rc100 [geometry_msgs/Twist]
[INFO] [1531356278.449703]: Setup publisher on odom [nav_msgs/Odometry]
[INFO] [1531356278.466352]: Setup publisher on joint_states [sensor_msgs/JointState]
[INFO] [1531356278.485605]: Setup publisher on battery_state [sensor_msgs/BatteryState]
[INFO] [1531356278.500973]: Setup publisher on magnetic_field [sensor_msgs/MagneticField]
[INFO] [1531356280.545840]: Setup publisher on /tf [tf/tfMessage]
[INFO] [1531356280.582609]: Note: subscribe buffer size is 1024 bytes
[INFO] [1531356280.584645]: Setup subscriber on cmd_vel [geometry_msgs/Twist]
[INFO] [1531356280.620330]: Setup subscriber on sound [turtlebot3_msgs/Sound]
[INFO] [1531356280.649508]: Setup subscriber on motor_power [std_msgs/Bool]
[INFO] [1531356280.688276]: Setup subscriber on reset [std_msgs/Empty]
[INFO] [1531356282.022709]: Setup TF on Odometry [tb3_0/odom]
[INFO] [1531356282.026863]: Setup TF on IMU [tb3_0/imu_link]
[INFO] [1531356282.030138]: Setup TF on MagneticField [tb3_0/mag_link]
[INFO] [1531356282.033628]: Setup TF on JointState [tb3_0/base_link]
[INFO] [1531356282.041117]: --------------------------
[INFO] [1531356282.044421]: Connected to OpenCR board!
[INFO] [1531356282.047700]: This core(v1.2.1) is compatible with TB3 Burger
[INFO] [1531356282.051355]: --------------------------
[INFO] [1531356282.054785]: Start Calibration of Gyro
[INFO] [1531356284.585490]: Calibration End

[Remote PC] Launch robot state publisher with same namespace.

$ ROS_NAMESPACE=tb3_0 roslaunch turtlebot3_bringup turtlebot3_remote.launch multi_robot_name:=tb3_0
$ ROS_NAMESPACE=tb3_1 roslaunch turtlebot3_bringup turtlebot3_remote.launch multi_robot_name:=tb3_1

Before start another application, check topics and TF tree to open rqt

$ rqt

To use this setup, each turtlebot3 makes map using SLAM and these maps are merged simutaneously by multi_map_merge packages. You can get more information about this to visit Virtual SLAM by Multiple TurtleBot3s sections