How to Add Ultrasonic Sensors to an SDF File for Gazebo

In the cover image you can see an ultrasonic sensor that was added to a simulated robot in Gazebo. An ultrasonic sensor is useful because, unlike LIDAR, an ultrasonic sensor can detect glass. Detection of glass is important if you’re planning to build a robot for the real-world that will use the ROS 2 Navigation stack.

To add a simulated ultrasonic sensor to your SDF file, you will need to add code that looks like this:

  <!-- *********************** ULTRASONIC SENSOR ************************  -->
  <link name="ultrasonic_1_link">
    <pose>0.09 -0.139 0.595 0 0 0</pose>
    <sensor name="ultrasonic_1" type="ray">
      <plugin name="ultrasonic_sensor_1" filename="">

When you launch RViz along with Gazebo, you will need to add the Range sensor option so that you can visualize the ultrasonic sensor output. Be sure to select “Best Effort” for the reliability policy.

Sensor Fusion Using the Robot Localization Package – ROS 2

In this tutorial, I will show you how to set up the robot_localization ROS 2 package on a simulated mobile robot. We will use the robot_localization package to fuse odometry data from the /wheel/odometry topic with IMU data from the /imu/data topic to provide locally accurate, smooth odometry estimates. Wheels can slip, so using the robot_localization package can help correct for this.

This tutorial is the third tutorial in my Ultimate Guide to the ROS 2 Navigation Stack (also known as Nav2).

You can get the entire code for this project here.

If you are using ROS Galactic or newer, you can get the code here.

Let’s get started!


You have completed the first two tutorials of this series:

  1. How to Create a Simulated Mobile Robot in ROS 2 Using URDF
  2. Set Up the Odometry for a Simulated Mobile Robot in ROS 2

About the Robot Localization Package

We will configure the robot_localization package to use an Extended Kalman Filter (ekf_node) to fuse the data from sensor inputs. These sensor inputs come from the IMU Gazebo plugin and the differential drive Gazebo plugin that are defined in our SDF file.

In a real robotics project, instead of simulated IMU and odometry data, we would use data from an IMU sensor like the BNO055 and wheel encoders, respectively.

 The ekf_node will subscribe to the following topics (ROS message types are in parentheses):

  • /wheel/odometry :  Position and velocity estimate based on the information from the differential drive Gazebo plugin (in a real robotics project this would be information drawn from wheel encoder tick counts). The orientation is in quaternion format. (nav_msgs/Odometry)
  • /imu/data : Data from the Inertial Measurement Unit (IMU) sensor Gazebo plugin (sensor_msgs/Imu.msg)

This node will publish data to the following topics:

  • /odometry/filtered : The smoothed odometry information (nav_msgs/Odometry)
  • /tf : Coordinate transform from the odom frame (parent) to the base_footprint frame (child). To learn about coordinate frames in ROS, check out this post.

Install the Robot Localization Package

Let’s begin by installing the robot_localization package. Open a new terminal window, and type the following command:

sudo apt install ros-foxy-robot-localization

If you are using a newer version of ROS 2 like ROS 2 Galactic, type the following:

 sudo apt install ros-galactic-robot-localization 

The syntax for the above command is:

sudo apt install ros-<ros2-distro>-robot-localization

If you are using ROS 2 Galactic, you will need to download the robot_localization package to your workspace.

cd ~/dev_ws/src
git clone -b fix/galactic/load_parameters

The reason you need to download that package above is because the Navigation Stack will throw the following exception if you don’t:

[ekf_node-1] terminate called after throwing an instance of ‘rclcpp::ParameterTypeException’ [ekf_node-1] what(): expected [string] got [not set]

cd ..
colcon build

Set the Configuration Parameters

We now need to specify the configuration parameters of the ekf_node by creating a YAML file.

Open a new terminal window, and type:

colcon_cd basic_mobile_robot
cd config
gedit ekf.yaml

Copy and paste this code inside the YAML file.

Save and close the file.

You can get a complete description of all the parameters on this page. Also you can check out this link on GitHub for a sample ekf.yaml file.

Create a Launch File

Now go to your launch folder. Open a new terminal window, and type:

colcon_cd basic_mobile_robot
cd launch

Copy and paste this code into the file.

If you are using ROS 2 Galactic or newer, your code is here.

Save the file, and close it.

Move to the package.

colcon_cd basic_mobile_robot

Open the package.xml file.

gedit package.xml file.

Copy and paste this code into the file.

Save the file, and close it.

Open the CMakeLists.txt file.

gedit CMakeLists.txt

Copy and paste this code into the file.

Save the file, and close it.

Build the Package

Now build the package by opening a terminal window, and typing the following command:

cd ~/dev_ws
colcon build

Launch the Robot

Open a new terminal, and launch the robot.

cd ~/dev_ws/
ros2 launch basic_mobile_robot

It might take a while for Gazebo and RViz to load, so be patient.

To see the active topics, open a terminal window, and type:

ros2 topic list

To see more information about the topics, execute:

ros2 topic info /imu/data
ros2 topic info /wheel/odometry

You should see an output similar to below:


Both topics have 1 publisher and 1 subscriber.

To see the output of the robot localization package (i.e. the Extended Kalman Filter (EKF)), type:

ros2 topic echo /odometry/filtered

I will move my robot in the reverse direction using the rqt_robot_steering GUI. Open a new terminal window, and type:


If you are using ROS 2 Galactic or newer, type:

sudo apt-get install ros-galactic-rqt-robot-steering

Where the syntax is:

sudo apt-get install ros-<ros-distribution>-rqt-robot-steering

Then type:

ros2 run rqt_robot_steering rqt_robot_steering --force-discover

Move the sliders to move the robot.


We can see the output of the odom -> base_footprint transform by typing the following command:

ros2 run tf2_ros tf2_echo odom base_footprint

Let’s see the active nodes.

ros2 node list

Let’s check out the ekf_node (named ekf_filter_node).

ros2 node info /ekf_filter_node

Let’s check out the ROS node graph.


Click the blue circular arrow in the upper left to refresh the node graph. Also select “Nodes/Topics (all)”.


To see the coordinate frames, type the following command in a terminal window.

ros2 run tf2_tools

If you are using ROS 2 Galactic or newer, type:

ros2 run tf2_tools view_frames

In the current working directory, you will have a file called frames.pdf. Open that file.

evince frames.pdf

Here is what my coordinate transform (i.e. tf) tree looks like:


You can see that the parent frame is the odom frame. The odom frame is the initial position and orientation of the robot. Every other frame below that is a child of the odom frame.

Later, we will add a map frame. The map frame will be the parent frame of the odom frame.

Finally, in RViz, under Global Options, change Fixed Frame to odom.


Open the steering tool again.


If you move the robot around using the sliders, you will see the robot move in both RViz and Gazebo.


That’s it!

In the next tutorial, I will show you how to add LIDAR to your robot so that you can map the environment and detect obstacles.

Automatic Docking to a Battery Charging Station – ROS 2

In this tutorial, I will show you how to create an autonomous docking application for a two-wheeled mobile robot. When the battery gets low, we want the robot to automatically go to a charging station (also known as docking station) to recharge its battery. 

The two most common ways to implement autonomous docking are:

  1. ArUco Marker or ARTag (e.g. Neobotix)
  2. Infrared Receiver and Transmitter (e.g. iRobot Roomba)

In this tutorial, we will assume we know the location of the battery charging station. You can use what we develop here as a template for ARTag or Infrared-based automatic docking.

Here is the output you will be able to achieve after completing this tutorial:


You can find the files for this post here on my Google Drive.

Create a tf Listener

The first thing you need to do is create a tf listener node to publish the base_link -> map transform.

Create the Charging Dock

Let’s begin by creating the charging dock.

Open a terminal window, and go to the following folder.

cd ~/dev_ws/src/two_wheeled_robot/models

Add the following folder named charging_dock.

Create the World

Open a terminal window, and go to the following folder.

cd ~/dev_ws/src/two_wheeled_robot/worlds

Make sure you have the following code inside the file.

Save the file, and close it.

Build the Package

We will now build our package.

cd ~/dev_ws/
colcon build

Load the World

Now load the world in Gazebo using the launch file.

Open a new terminal, and type:

ros2 launch two_wheeled_robot

Here is the output:


Autonomous Docking Without ARTag Vision

Create the Script

Now, let’s create a script that will make the mobile robot navigate to the charging dock when the battery gets low. Credit to this GitHub repository for the inspiration for this method.

On a high level, the algorithm does the following:

  1. Navigate to the perpendicular line to the ARTag. 
  2. Adjust heading.
  3. Go to a waypoint in front of the charging dock.
  4. Adjust heading.
  5. Go straight to the ARTag.

Open a terminal window, and go to the following folder.

cd ~/dev_ws/src/two_wheeled_robot/scripts
#! /usr/bin/env python3

  Navigate to a charging dock once the battery gets low.
Subscription Topics:
  Current battery state
  /battery_status - sensor_msgs/BatteryState
  2D Pose of the base_link of the robot in the map frame
  /map_to_base_link_pose2d – std_msgs/Float64MultiArray
Publishing Topics:
  Velocity command to navigate to the charging dock.
  /cmd_vel - geometry_msgs/Twist
Author: Addison Sears-Collins
Date: November 26, 2021

import math # Math library
import time  # Time library

from rclpy.duration import Duration # Handles time for ROS 2
import rclpy # Python client library for ROS 2
from rclpy.node import Node # Handles the creation of nodes
from rclpy.executors import MultiThreadedExecutor
from robot_navigator import BasicNavigator, NavigationResult # Helper module
from geometry_msgs.msg import PoseStamped # Pose with ref frame and timestamp
from geometry_msgs.msg import Twist # Velocity command
from sensor_msgs.msg import BatteryState # Battery status
from std_msgs.msg import Float64MultiArray # Handle float64 arrays

# Holds the current pose of the robot
current_x = 0.0
current_y = 2.0
current_yaw_angle = 0.0

# Holds the current state of the battery
this_battery_state = BatteryState()
prev_battery_state = BatteryState()

# Flag for detecting the change in the battery state
low_battery = False
low_battery_min_threshold = 0.25

class ConnectToChargingDockNavigator(Node):
    Navigates and connects to the charging dock
    def __init__(self):
      # Initialize the class using the constructor
      # Create a publisher
      # This node publishes the desired linear and angular velocity of the robot
      self.publisher_cmd_vel = self.create_publisher(
      timer_period = 0.1
      self.timer = self.create_timer(timer_period, self.navigate_to_dock)

      # Holds the goal poses of the robot
      self.goal_x = [-1.0, -1.0, -1.0]
      self.goal_y = [2.0, 1.4, 0.83]
      self.goal_yaw_angle = [-1.5708, -1.5708, -1.5708]

      # Keep track of which goal we're headed towards
      self.goal_idx = 0

      # Declare linear and angular velocities
      self.linear_velocity = 0.08  # meters per second
      self.angular_velocity = 0.1 # radians per second

      # Declare distance metrics in meters
      self.distance_goal_tolerance = 0.05
      self.reached_distance_goal = False      

      # Declare angle metrics in radians
      self.heading_tolerance = 0.05
      self.yaw_goal_tolerance = 0.05
    def navigate_to_dock(self):
      global low_battery
      if low_battery == False:
        return None 
      self.get_logger().info('Navigating to the charging dock...')
      # Launch the ROS 2 Navigation Stack
      navigator = BasicNavigator()

      # Wait for navigation to fully activate. Use this line if autostart is set to true.

      # If desired, you can change or load the map as well
      # navigator.changeMap('/path/to/map.yaml')

      # You may use the navigator to clear or obtain costmaps
      # navigator.clearAllCostmaps()  # also have clearLocalCostmap() and clearGlobalCostmap()
      # global_costmap = navigator.getGlobalCostmap()
      # local_costmap = navigator.getLocalCostmap()

      # Set the robot's goal pose
      goal_pose = PoseStamped()
      goal_pose.header.frame_id = 'map'
      goal_pose.header.stamp = navigator.get_clock().now().to_msg()
      goal_pose.pose.position.x = 0.0
      goal_pose.pose.position.y = 2.0
      goal_pose.pose.position.z = 0.25
      goal_pose.pose.orientation.x = 0.0
      goal_pose.pose.orientation.y = 0.0
      goal_pose.pose.orientation.z = 0.0
      goal_pose.pose.orientation.w = 1.0

      # Go to the goal pose

      i = 0

      # Keep doing stuff as long as the robot is moving towards the goal
      while not navigator.isNavComplete():
        # Do something with the feedback
        i = i + 1
        feedback = navigator.getFeedback()
        if feedback and i % 5 == 0:
          print('Distance remaining: ' + '{:.2f}'.format(
            feedback.distance_remaining) + ' meters.')
          # Some navigation timeout to demo cancellation
          #if Duration.from_msg(feedback.navigation_time) > Duration(seconds=1800.0):

      # Do something depending on the return code
      result = navigator.getResult()
      if result == NavigationResult.SUCCEEDED:
        print('Successfully reached charging dock staging area...')
        low_battery = False
      elif result == NavigationResult.CANCELED:
        print('Goal was canceled!')
      elif result == NavigationResult.FAILED:
        print('Goal failed!')
        print('Goal has an invalid return status!')  
    def connect_to_dock(self):  
      # While the battery is not charging
      while this_battery_state.power_supply_status != 1:
        # Publish the current battery state
        self.get_logger().info('NOT CHARGING...')
        if (self.goal_idx == 0):
          self.get_logger().info('Going to perpendicular line to ARTag...')
        elif (self.goal_idx == 1):
          self.get_logger().info('Going to perpendicular line to ARTag...')
        elif (self.goal_idx == 2):
          self.get_logger().info('Going straight to ARTag...')
          # Stop the robot
          cmd_vel_msg = Twist()
          cmd_vel_msg.linear.x = 0.0
          cmd_vel_msg.angular.z = 0.0
          self.get_logger().info('Robot is idle...')
      self.get_logger().info('Successfully connected to the charging dock!')
    def get_distance_to_goal(self):
      Get the distance between the current x,y coordinate and the desired x,y coordinate
      The unit is meters.
      distance_to_goal = math.sqrt(math.pow(self.goal_x[self.goal_idx] - current_x, 2) + math.pow(
        self.goal_y[self.goal_idx] - current_y, 2))
      return distance_to_goal
    def get_heading_error(self):
      Get the heading error in radians
      delta_x = self.goal_x[self.goal_idx] - current_x
      delta_y = self.goal_y[self.goal_idx] - current_y
      desired_heading = math.atan2(delta_y, delta_x) 
      heading_error = desired_heading - current_yaw_angle
      # Make sure the heading error falls within -PI to PI range
      if (heading_error > math.pi):
        heading_error = heading_error - (2 * math.pi)
      if (heading_error < -math.pi):
        heading_error = heading_error + (2 * math.pi)
      return heading_error
    def get_radians_to_goal(self):
      Get the yaw goal angle error in radians
      yaw_goal_angle_error = self.goal_yaw_angle[self.goal_idx] - current_yaw_angle
      return yaw_goal_angle_error
    def go_to_line(self):
      Go to the line that is perpendicular to the AR tag
      distance_to_goal = self.get_distance_to_goal()
      heading_error = self.get_heading_error()
      yaw_goal_error = self.get_radians_to_goal()
      cmd_vel_msg = Twist()
      # If we are not yet at the position goal
      if (math.fabs(distance_to_goal) > self.distance_goal_tolerance and self.reached_distance_goal == False):
        # If the robot's heading is off, fix it
        if (math.fabs(heading_error) > self.heading_tolerance):
          if heading_error > 0:
            cmd_vel_msg.angular.z = self.angular_velocity
            cmd_vel_msg.angular.z = -self.angular_velocity
          cmd_vel_msg.linear.x = self.linear_velocity
      # Orient towards the yaw goal angle
      elif (math.fabs(yaw_goal_error) > self.yaw_goal_tolerance):
        if yaw_goal_error > 0:
          cmd_vel_msg.angular.z = self.angular_velocity
          cmd_vel_msg.angular.z = -self.angular_velocity
        self.reached_distance_goal = True
      # Goal achieved, go to the next goal  
        # Go to the next goal
        self.goal_idx = self.goal_idx + 1    
        self.get_logger().info('Arrived at perpendicular line. Going straight to ARTag...')
        self.reached_distance_goal = False     

      # Publish the velocity message  
    def go_to_artag(self):
      Go straight to the AR tag
      distance_to_goal = self.get_distance_to_goal()
      heading_error = self.get_heading_error()
      yaw_goal_error = self.get_radians_to_goal()
      cmd_vel_msg = Twist()
      # If we are not yet at the position goal
      if (math.fabs(distance_to_goal) > self.distance_goal_tolerance and self.reached_distance_goal == False):
        # If the robot's heading is off, fix it
        if (math.fabs(heading_error) > self.heading_tolerance):
          if heading_error > 0:
            cmd_vel_msg.angular.z = self.angular_velocity
            cmd_vel_msg.angular.z = -self.angular_velocity
          cmd_vel_msg.linear.x = self.linear_velocity
      # Orient towards the yaw goal angle
      elif (math.fabs(yaw_goal_error) > self.yaw_goal_tolerance):
        if yaw_goal_error > 0:
          cmd_vel_msg.angular.z = self.angular_velocity
          cmd_vel_msg.angular.z = -self.angular_velocity
        self.reached_distance_goal = True
      # Goal achieved, go to the next goal  
        # Go to the next goal
        self.goal_idx = self.goal_idx + 1 
        self.get_logger().info('Arrived at the charging dock...')   
        self.reached_distance_goal = True

      # Publish the velocity message  

class BatteryStateSubscriber(Node):
    Subscriber node to the current battery state
    def __init__(self):
      # Initialize the class using the constructor
      # Create a subscriber 
      # This node subscribes to messages of type
      # sensor_msgs/BatteryState
      self.subscription_battery_state = self.create_subscription(
    def get_battery_state(self, msg):
      Update the current battery state.
      global this_battery_state
      global prev_battery_state
      global low_battery
      prev_battery_state = this_battery_state
      this_battery_state = msg
      # Check for low battery
      if prev_battery_state.percentage >= low_battery_min_threshold and this_battery_state.percentage < low_battery_min_threshold:
        low_battery = True
class PoseSubscriber(Node):
    Subscriber node to the current 2D pose of the robot
    def __init__(self):
      # Initialize the class using the constructor
      # Create a subscriber 
      # This node subscribes to messages of type
      # std_msgs/Float64MultiArray
      self.subscription_pose = self.create_subscription(
    def get_pose(self, msg):
      Update the current 2D pose.
      global current_x
      global current_y
      global current_yaw_angle
      current_2d_pose =
      current_x = current_2d_pose[0]
      current_y = current_2d_pose[1]
      current_yaw_angle = current_2d_pose[2]      
def main(args=None):
  Entry point for the program.
  # Initialize the rclpy library
    # Create the nodes
    connect_to_charging_dock_navigator = ConnectToChargingDockNavigator()
    battery_state_subscriber = BatteryStateSubscriber()
    pose_subscriber = PoseSubscriber()
    # Set up mulithreading
    executor = MultiThreadedExecutor(num_threads=4)
      # Spin the nodes to execute the callbacks
      # Shutdown the nodes

    # Shutdown

if __name__ == '__main__':

Save the file, and close it.

Edit CMakeLists.txt

Now let’s update our CMakeLists.txt file.

cd ~/dev_ws/src/two_wheeled_robot
gedit CMakeLists.txt

Add the script to your CMakeLists.txt file.

Save the file, and close it.

Create the Launch File

Open a new terminal window, and type:

cd ~/dev_ws/src/two_wheeled_robot/launch/hospital_world

Add the following code.

# Author: Addison Sears-Collins
# Date: November 26, 2021
# Description: Launch a two-wheeled robot using the ROS 2 Navigation Stack. 
#              The spawning of the robot is performed by the Gazebo-ROS spawn_entity node.
#              The robot must be in both SDF and URDF format.
#              If you want to spawn the robot in a pose other than the default, be sure to set that inside
#              the nav2_params_path yaml file: amcl ---> initial_pose: [x, y, z, yaw]
#              The robot will return to the charging dock when the /battery_status percentage is low.

import os
from launch import LaunchDescription
from launch.actions import DeclareLaunchArgument, IncludeLaunchDescription
from launch.conditions import IfCondition, UnlessCondition
from launch.launch_description_sources import PythonLaunchDescriptionSource
from launch.substitutions import Command, LaunchConfiguration, PythonExpression
from launch_ros.actions import Node
from launch_ros.substitutions import FindPackageShare

def generate_launch_description():
  package_name = 'two_wheeled_robot'
  robot_name_in_model = 'two_wheeled_robot'
  default_launch_dir = 'launch'
  gazebo_models_path = 'models'
  map_file_path = 'maps/hospital_world/hospital_world.yaml'
  nav2_params_path = 'params/hospital_world/nav2_connect_to_charging_dock_params.yaml'
  robot_localization_file_path = 'config/ekf.yaml'
  rviz_config_file_path = 'rviz/hospital_world/nav2_config.rviz'
  sdf_model_path = 'models/two_wheeled_robot_description/model.sdf'
  urdf_file_path = 'urdf/two_wheeled_robot.urdf'
  world_file_path = 'worlds/'
  nav_to_charging_dock_script = ''
  # Pose where we want to spawn the robot
  spawn_x_val = '0.0'
  spawn_y_val = '2.0'
  spawn_z_val = '0.25'
  spawn_yaw_val = '0.0'

  ########## You do not need to change anything below this line ###############
  # Set the path to different files and folders.  
  pkg_gazebo_ros = FindPackageShare(package='gazebo_ros').find('gazebo_ros')   
  pkg_share = FindPackageShare(package=package_name).find(package_name)
  default_launch_dir = os.path.join(pkg_share, default_launch_dir)
  default_urdf_model_path = os.path.join(pkg_share, urdf_file_path)
  robot_localization_file_path = os.path.join(pkg_share, robot_localization_file_path) 
  default_rviz_config_path = os.path.join(pkg_share, rviz_config_file_path)
  world_path = os.path.join(pkg_share, world_file_path)
  gazebo_models_path = os.path.join(pkg_share, gazebo_models_path)
  os.environ["GAZEBO_MODEL_PATH"] = gazebo_models_path
  nav2_dir = FindPackageShare(package='nav2_bringup').find('nav2_bringup') 
  nav2_launch_dir = os.path.join(nav2_dir, 'launch') 
  sdf_model_path = os.path.join(pkg_share, sdf_model_path)
  static_map_path = os.path.join(pkg_share, map_file_path)
  nav2_params_path = os.path.join(pkg_share, nav2_params_path)
  nav2_bt_path = FindPackageShare(package='nav2_bt_navigator').find('nav2_bt_navigator')
  # Launch configuration variables specific to simulation
  autostart = LaunchConfiguration('autostart')
  headless = LaunchConfiguration('headless')
  map_yaml_file = LaunchConfiguration('map')
  namespace = LaunchConfiguration('namespace')
  params_file = LaunchConfiguration('params_file')
  rviz_config_file = LaunchConfiguration('rviz_config_file')
  sdf_model = LaunchConfiguration('sdf_model')
  slam = LaunchConfiguration('slam')
  urdf_model = LaunchConfiguration('urdf_model')
  use_namespace = LaunchConfiguration('use_namespace')
  use_robot_state_pub = LaunchConfiguration('use_robot_state_pub')
  use_rviz = LaunchConfiguration('use_rviz')
  use_sim_time = LaunchConfiguration('use_sim_time')
  use_simulator = LaunchConfiguration('use_simulator')
  world = LaunchConfiguration('world')
  # Map fully qualified names to relative ones so the node's namespace can be prepended.
  # In case of the transforms (tf), currently, there doesn't seem to be a better alternative
  # TODO(orduno) Substitute with `PushNodeRemapping`
  remappings = [('/tf', 'tf'),
                ('/tf_static', 'tf_static')]
  # Declare the launch arguments  
  declare_namespace_cmd = DeclareLaunchArgument(
    description='Top-level namespace')

  declare_use_namespace_cmd = DeclareLaunchArgument(
    description='Whether to apply a namespace to the navigation stack')
  declare_autostart_cmd = DeclareLaunchArgument(
    description='Automatically startup the nav2 stack')

  declare_map_yaml_cmd = DeclareLaunchArgument(
    description='Full path to map file to load')
  declare_params_file_cmd = DeclareLaunchArgument(
    description='Full path to the ROS2 parameters file to use for all launched nodes')
  declare_rviz_config_file_cmd = DeclareLaunchArgument(
    description='Full path to the RVIZ config file to use')

  declare_sdf_model_path_cmd = DeclareLaunchArgument(
    description='Absolute path to robot sdf file')

  declare_simulator_cmd = DeclareLaunchArgument(
    description='Whether to execute gzclient')

  declare_slam_cmd = DeclareLaunchArgument(
    description='Whether to run SLAM')

  declare_urdf_model_path_cmd = DeclareLaunchArgument(
    description='Absolute path to robot urdf file')
  declare_use_robot_state_pub_cmd = DeclareLaunchArgument(
    description='Whether to start the robot state publisher')

  declare_use_rviz_cmd = DeclareLaunchArgument(
    description='Whether to start RVIZ')
  declare_use_sim_time_cmd = DeclareLaunchArgument(
    description='Use simulation (Gazebo) clock if true')

  declare_use_simulator_cmd = DeclareLaunchArgument(
    description='Whether to start the simulator')

  declare_world_cmd = DeclareLaunchArgument(
    description='Full path to the world model file to load')
  # Specify the actions
  # Start Gazebo server
  start_gazebo_server_cmd = IncludeLaunchDescription(
    PythonLaunchDescriptionSource(os.path.join(pkg_gazebo_ros, 'launch', '')),
    launch_arguments={'world': world}.items())

  # Start Gazebo client    
  start_gazebo_client_cmd = IncludeLaunchDescription(
    PythonLaunchDescriptionSource(os.path.join(pkg_gazebo_ros, 'launch', '')),
    condition=IfCondition(PythonExpression([use_simulator, ' and not ', headless])))

  # Launch the robot
  spawn_entity_cmd = Node(
    arguments=['-entity', robot_name_in_model,
               '-file', sdf_model,
                  '-x', spawn_x_val,
                  '-y', spawn_y_val,
                  '-z', spawn_z_val,
                  '-Y', spawn_yaw_val],

  # Start robot localization using an Extended Kalman filter
  start_robot_localization_cmd = Node(
    {'use_sim_time': use_sim_time}])

  # Subscribe to the joint states of the robot, and publish the 3D pose of each link.
  start_robot_state_publisher_cmd = Node(
    parameters=[{'use_sim_time': use_sim_time, 
    'robot_description': Command(['xacro ', urdf_model])}],

  # Launch RViz
  start_rviz_cmd = Node(
    arguments=['-d', rviz_config_file])   
  # Launch navigation to the charging dock
  start_navigate_to_charging_dock_cmd = Node(

  # Launch navigation to the charging dock
  start_map_to_base_link_transform_cmd = Node(

  # Launch the ROS 2 Navigation Stack
  start_ros2_navigation_cmd = IncludeLaunchDescription(
    PythonLaunchDescriptionSource(os.path.join(nav2_launch_dir, '')),
    launch_arguments = {'namespace': namespace,
                        'use_namespace': use_namespace,
                        'slam': slam,
                        'map': map_yaml_file,
                        'use_sim_time': use_sim_time,
                        'params_file': params_file,
                        'autostart': autostart}.items())

  # Create the launch description and populate
  ld = LaunchDescription()

  # Declare the launch options

  # Add any actions

  return ld

Build the file.

cd ~/dev_ws/
colcon build

Update the Parameters

Open a new terminal window, and type:

cd ~/dev_ws/src/two_wheeled_robot/params/hospital_world
gedit nav2_connect_to_charging_dock_params.yaml

Add these parameters.

Save the file, and close it.

Launch the Robot

Open a new terminal window, and run the following message to indicate the battery is at full charge. All of this is a single command.

ros2 topic pub /battery_status sensor_msgs/BatteryState '{voltage: 9.0, percentage: 1.0, power_supply_status: 3}' 

Open a new terminal and launch the robot.

ros2 launch two_wheeled_robot

Select the Nav2 Goal button at the top of RViz and click somewhere on the map to command the robot to navigate to any reachable goal location.

The robot will move to the goal location.


While the robot is moving, stop the /battery_status publisher.


Now run this command to indicate low battery:

ros2 topic pub /battery_status sensor_msgs/BatteryState '{voltage: 2.16, percentage: 0.24, power_supply_status: 3}' 

The robot will plan a path to the staging area and then move along that path.

Once the robot reaches the staging area, the robot will navigate to the charging dock (i.e. ARTag) using the algorithm we developed earlier in this post. 


Once the robot has reached the charging dock, press CTRL + C to stop the /battery_status publisher, and type:

ros2 topic pub /battery_status sensor_msgs/BatteryState '{voltage: 2.16, percentage: 0.24, power_supply_status: 1}' 

That 1 for the power_supply_status indicates the battery is CHARGING.


That’s it!


ArUco Marker or AR Tag Automatic Docking References

If you are interested in taking this application a step further, you can use ArUco Marker or AR Tag-based navigation to return to the docking station. You will need to have a camera on your robot

Here are some helpful links:




Note that some of the old ROS packages for AR Tag pose estimation are out of date. This package enables you to detect the tag and then calculate its pose.

The best tutorial for ArUco marker detection and pose estimation using OpenCV is here. You can also check out this tutorial this tutorial, and this book.

To learn how to convert the ArUco marker OpenCV output to a ROS-compatible format, check this out.

This YouTube video is also useful.

The key is to use OpenCV’s aruco.estimatePoseSingleMarkers(…) method, which returns the pose of an ArUco marker relative to the camera reference frame. Once you know that, you can use tf to calculate the pose of the ArUco marker relative to the base_link frame. You then modify the algorithm we wrote above to center the base_link frame with the ArUco tag.

Infrared-based Automatic Docking References

I did not use infrared receivers and transmitters in this tutorial, but if you’re interested in using this technique, below are some helpful links to get you started.

Keep building!