ROS navigation tools are a set of software packages that enable autonomous robots to move safely and efficiently in complex environments. They use sensors, maps, localization, planning, and control algorithms to achieve this goal. In this article, you will learn the steps to use ROS navigation tools for your own robot projects.
The first step is to install ROS on your computer and your robot. ROS is an open-source framework that provides a set of libraries, tools, and conventions for developing robot applications. You can choose from different distributions and versions of ROS depending on your needs and preferences. To install ROS, you need to follow the official instructions for your operating system and hardware platform. You can also use Docker or VirtualBox to run ROS in a container or a virtual machine.
The most important step while installing the ROS is to identify which ROS distribution to install. My advice is to install ROS humble. As in ROS foxy some features might not be available. Also foxy provides basic security features, while ROS Humble includes more robust security mechanisms to protect your robotic systems against potential threats.
The next step is to set up your robot with the necessary hardware and software components for navigation. You need to have a robot base that can move and steer, a sensor that can measure the distance to obstacles, and a computer that can run ROS nodes. You also need to configure the communication between your computer and your robot, either via a wired or a wireless connection. You can use the roslaunch tool to start the ROS nodes on your robot and your computer.
Setting up the robot is one of most important steps to make sure you have a working testbed. To make the most out of ROS, the most commonly used setup is to publish topics on the robot and observe them on a separate local machine (i.e your laptop) by setting up same ROS DOMAIN ID and disabling Localhost only settings for ROS. For navigation, the robot needs at least one source of odometry ( IMU, wheel encoders, etc) and a sensor to get information about its environment ( camera, Lidar, stereo camera, etc).
The third step is to create a map of the environment where your robot will navigate. A map is a representation of the spatial layout and the features of the environment, such as walls, doors, furniture, etc. You can use the gmapping package to create a map from the sensor data collected by your robot. You can also use the map_server package to save and load the map as a file. You can visualize the map using the rviz tool.
When performing mapping, ensure that the robot moves at a minimal speed to capture accurate data. After moving the robot in a straight line for a short distance, rotate it to scan the surroundings fully, helping to create a more detailed and accurate map. Utilize visualization tools like RViz to monitor and visualize the generated maps in real-time, ensuring that the mapping process is proceeding correctly and adjusting as needed.
The fourth step is to localize your robot on the map. Localization is the process of estimating the pose (position and orientation) of your robot relative to the map. You can use the amcl package to perform localization using a probabilistic method called Monte Carlo Localization (MCL). You can also use the tf package to broadcast and transform the coordinate frames of your robot and the map. You can visualize the localization using the rviz tool.
Equip and calibrate your sensors. Create a map of the environment using tools such as GMapping. Configure the AMCL package with parameters in a YAML file. Launch the map server and AMCL node, then use RViz to visualize and verify the localization.
The fifth step is to plan a path for your robot to reach a desired goal. A path is a sequence of waypoints that guide your robot from its current pose to the goal pose. You can use the move_base package to plan a path using a global planner and a local planner. The global planner generates a coarse path that avoids static obstacles on the map. The local planner generates a fine path that avoids dynamic obstacles detected by the sensor. You can visualize the path using the rviz tool.
The final step is to control your robot to follow the path and reach the goal. Control is the process of sending commands to your robot base to adjust its speed and direction. You can use the move_base package to control your robot using a feedback loop that compares the actual pose and the desired pose. You can also use the rqt_graph tool to monitor the topics and messages exchanged between the ROS nodes involved in navigation. You can visualize the control using the rviz tool.