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Examples

WARNING: This version is a temporarily uploaded manual that was originally supported in Kinetic. It needs to be ported to Noetic examples and is planned to be supported with Humble examples soon.

Make sure to run the Bringup instruction before performing this examples, and be careful when testing the robot on the table as the robot might fall.

NOTE:

  • This instruction was tested on Ubuntu 16.04 and ROS Kinetic Kame.
  • This instruction is supposed to be running on the remote PC. Follow the instruction on your Remote PC.

The contents in e-Manual are subject to be updated without a prior notice. Therefore, some video may differ from the contents in e-Manual.

NOTE: This feature is available for ROS Kinetic and ROS2 Dashing.

NOTE: The examples will be added as quickly as possible based on Humble. We appreciate your interest and support.

Move using Interactive Markers

The TurtleBot3 can be moved by Interactive Markers on RViz. You can move the TurtleBot3 to rotate or linear using Interactive Markers.

[Remote PC] Open a new terminal and launch the remote file.

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.

$ export TURTLEBOT3_MODEL=${TB3_MODEL}
$ roslaunch turtlebot3_bringup turtlebot3_remote.launch

[Remote PC] launch the interactive markers file.

$ roslaunch turtlebot3_example interactive_markers.launch

[Remote PC] Visualize the model in 3D with RViz.

$ rosrun rviz rviz -d `rospack find turtlebot3_example`/rviz/turtlebot3_interactive.rviz

Obstacle Detection

The TurtleBot3 can be moved or stopped by LDS data. When the TurtleBot3 moves, it stops when it detects an obstacle ahead.

[Remote PC] Launch the obstacle file.

$ roslaunch turtlebot3_example turtlebot3_obstacle.launch

Position Control

NOTE: This feature is available for Dashing.

Point Operation

The TurtleBot3 can be moved by 2D point (x, y) and z-angular. For example, if you insert (0.5, 0.3, 60), TurtleBot3 moves to point (x = 0.5m, y = 0.3m) and then rotates 60 deg.

[Remote PC] launch the pointop file.

$ roslaunch turtlebot3_example turtlebot3_pointop_key.launch

Patrol

The TurtleBot3 can be moved by custom routes. There are three routes(rectangle, triangle and circle). This example uses action topic. Action client translates patrol data(mode, area, count) to action server. And then action server translates cmd_vel to TurtleBot3. Please refer to the above tutorial video for more detailed usage.

[Remote PC] Launch the patrol server file.

$ rosrun turtlebot3_example turtlebot3_server

[Remote PC] Launch the patrol client file.

$ roslaunch turtlebot3_example turtlebot3_client.launch

TurtleBot Follower Demo

In order to try examples in the below, 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

NOTE:

  • 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.
  1. [Remote PC] Install scikit-learn, NumPy and ScyPy packages.
    $ sudo apt-get install python-pip
    $ sudo pip install -U scikit-learn numpy scipy
    $ sudo pip install --upgrade pip
    
  2. [Remote PC] When installation is completed, run roscore on the remote pc.
    $ roscore
    
  3. [TurtleBot] Launch the Bringup package.
    $ roslaunch turtlebot3_bringup turtlebot3_robot.launch
    
  4. [Remote PC] Set the enviroment variable for the TurtleBot3 Burger and launch turtlebot3_follow_filter.
    $ export TURTLEBOT3_MODEL=burger
    $ roslaunch turtlebot3_follow_filter turtlebot3_follow_filter.launch
    
  5. [Remote PC] Launch turtlebot3_follower.
    $ roslaunch turtlebot3_follower turtlebot3_follower.launch
    

TurtleBot Panorama Demo

NOTE:

  • 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
  1. [TurtleBot] Launch the turtlebot3_rpicamera file
    $ roslaunch turtlebot3_bringup turtlebot3_rpicamera.launch
    
  2. [Remote PC] Launch panorama.
    $ roslaunch turtlebot3_panorama panorama.launch
    
  3. [Remote PC] To start the panorama demo, enter the following 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:

    • Mode for taking the pictures.
    • 0 : snap&rotate (i.e. rotate, stop, snapshot, rotate, stop, snapshot, …)
    • 1 : continuous (i.e. keep rotating while taking snapshots)
    • 2 : stop taking pictures and create panoramic image - Total angle of panoramic image, in degrees - Angle interval (in degrees) when creating the panoramic image in snap&rotate mode, time interval (in seconds) otherwise - Rotating velocity (in radians/s)
  4. [Remote PC] To view the result image, enter the following command.
    $ rqt_image_view image:=/turtlebot3_panorama/panorama
    

Automatic Parking

NOTE:

  • 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.
  1. [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
    
  2. [Remote PC] Run roscore.
    $ roscore
    
  3. [TurtleBot] Bring up basic packages to start TurtleBot3 applications.
    $ roslaunch turtlebot3_bringup turtlebot3_robot.launch
    
  4. [Remote PC] If you use TurtleBot3 Burger, set the model of TurtleBot3 like command below.
    $ export TURTLEBOT3_MODEL=burger
    

    NOTE: Specify ${TB3_MODEL}: burger, waffle, waffle_pi before excuting the command. Set the permanent export setting by following Export TURTLEBOT3_MODEL instruction.

  5. [Remote PC] Run RViz.
    $ roslaunch turtlebot3_bringup turtlebot3_remote.launch
    $ rosrun rviz rviz -d `rospack find turtlebot3_automatic_parking`/rviz/turtlebot3_automatic_parking.rviz
    
  6. [Remote PC] Launch the automatic parking file.
    $ roslaunch turtlebot3_automatic_parking turtlebot3_automatic_parking.launch
    
    • You can select LaserScan topic in RViz.

    • /scan

    • /scan_spot

Automatic Parking Vision

NOTE:

  • 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.
  1. [Remote PC] Run roscore.
    $ roscore
    
  2. [TurtleBot] Bring up basic packages to start TurtleBot3 applications.
    $ roslaunch turtlebot3_bringup turtlebot3_robot.launch
    
  3. [TurtleBot] Start the raspberry pi camera nodes.
    $ roslaunch turtlebot3_bringup turtlebot3_rpicamera.launch
    
  4. [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
    
  5. [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
    
  6. [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
    

    TurtleBot3 Automatic Parking Vision

Load Multiple TurtleBot3s

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

  1. [Remote PC] Run roscore.
    $ roscore
    
  2. 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"
      
  3. Then the terminal you launched tb3_0 will represents below messages. You can watch TF messages have prefix tb3_0
    SUMMARY
    ========
    
    PARAMETERS
     * /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
    
    NODES
      /tb3_0/
        turtlebot3_core (rosserial_python/serial_node.py)
        turtlebot3_diagnostics (turtlebot3_bringup/turtlebot3_diagnostics)
        turtlebot3_lds (hls_lfcd_lds_driver/hlds_laser_publisher)
    
    ROS_MASTER_URI=http://192.168.1.2:11311
    
    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
    
  4. [Remote PC] Launch robot state publisher with same namespace.
    • [TurtleBot(tb3_0)]
      $ ROS_NAMESPACE=tb3_0 roslaunch turtlebot3_bringup turtlebot3_remote.launch multi_robot_name:=tb3_0
      
    • [TurtleBot(tb3_1)]
      $ ROS_NAMESPACE=tb3_1 roslaunch turtlebot3_bringup turtlebot3_remote.launch multi_robot_name:=tb3_1
      
  5. 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

NOTE: This feature is available for ROS2 Humble.

Description

  • What is Load Multiple TurtleBot3s example?
    • This example shows how to operate multiple TurtleBot3s in one Remote PC.
    • If you operate multiple TurtleBots as if you were operating just one, you won’t be able to distinguish which topic belongs to which robot.
    • You can assign namespace to each TurtleBot3’s node, topic, frame as so you can identify each of TurtleBot3.
  • Function of launch files
    1. multi_robot.launch.py
      • Launch sub launch files(gzsever, gzclient, robot_state_publisher, multi_spawn_turtlebot3) with parameter.
      • Modifying model SDF temporarily for changing odom frame_id and base scan’s target frame_id.
    2. robot_state_publisher.launch.py → robot_state_publisher node
      • Read model urdf and make /tf of hardware of robot.
    3. multi_spawn_turtlebot3.launch.py → spawn_entity.py
      • Read model.sdf and spawn TurtleBot3 model in gazebo world.
      • Sensor data is made by plugin that is written in model.sdf.

Multi Robot launch in Gazebo

In this chapter, we show how to launch multi robot in gazebo and result of that

  • Launch the multi_robot.launch in gazebo package.
    $ ros2 launch turtlebot3_gazebo multi_robot.launch.py  
    


  • You can see three TurtleBot3s as below picture.


  • Nodes and topics are completely saparated by namespace.

NOTE

  • Namespace is not necessary for /tf and /tf_static.
  • tf2_ros package manages /tf & /tf_static, allowing multiple nodes to publish /tf & /tf_static.
  • Instead, frame_ids in /tf & /tf_static must be unique.


  • Frames are completely saparated by namespace.

Modifing Multi Robot launch in Gazebo

In this chapter, we will learn how to modify the launch files to fit Gazebo simulation for your own project

  • Change robot number.
    • multi_robot.launch → Change value of number_of_robot variable.
  • Change world model.
    • multi_robot.launch → Change value of world varible to your world file path.
  • Change robot spawn location.
    • multi_robot.launch → Change value of pose list. Row means robot number, column[0] is x_pose, column[1] is y_pose
  • Change robot namespace.
    • multi_robot.launch → Change value of namespace variable.



TurtleBot3 World Example

  • You can see three TurtleBot3s as below picture.

number_of_robot = 4
pose = [[-2,-0.5], [0.5,-2], [2,0.5], [-0.5,2]]
world = turtlebot3_world.world

Multi Robot launch in reality

In this chapter, we show how to launch multi robot in reality and result of that

  • Modify the frame_id of the topic header. This allows sensor data to be viewed separately in RViz2.

This task should be performed on the files located on the TurtleBot SBC where bringup is operated.

$ nano ~/turtlebot3_ws/src/turtlebot3/turtlebot3_node/include/turtlebot3_node/sensors/imu.hpp  

$ nano ~/turtlebot3_ws/src/ld08_driver/src/lipkg.cpp  



  • Launch bringup with argument namespace.
    $ ros2 launch turtlebot3_bringup robot.launch.py namespace:=tb3_1 # Insert what you want to use as namespace
    


  • Nodes and topics are completely saparated by namespace.

Multi Robot Teleop

In this chapter, we will control multi robot with teleop in Gazebo simulation.

$ ros2 run turtlebot3_teleop teleop_keyboard --ros-args -r __ns:=/tb3_1 # Change the number to the robot you want to control  
$ ros2 run turtlebot3_teleop teleop_keyboard Change --ros-args -r __ns:=/tb3_1

Control Your TurtleBot3!
---------------------------
Moving around:
        w
   a    s    d
        x

w/x : increase/decrease linear velocity (Burger : ~ 0.22, Waffle and Waffle Pi : ~ 0.26)
a/d : increase/decrease angular velocity (Burger : ~ 2.84, Waffle and Waffle Pi : ~ 1.82)

space key, s : force stop

CTRL-C to quit