What's the easiest way to use ROS navigation stack for goal-oriented robot behavior?

The ROS navigation stack epitomizes robot ingenuity, mirroring human-like navigation: 1. Map Server: The robot's cerebral cortex, crafting a detailed map of obstacles and pathways 2. Localization System: Much like self-awareness, sensors pinpoint the robot's place in the map 3. Global Planner: A strategic navigator like our GPS, calculating routes amid changing environments akin our ability to choose efficient paths amidst uncertainty 4. Local Planner: Reflects the robot's nimbleness, generating trajectories accounting for dynamics and real-time data akin to skilled drivers The ROS navigation stack merges perception, strategic planning, and precise execution, enabling robots to navigate complex terrains, echoing human navigation prowess.

The new generation navigation stack used in ROS 2 also known as Nav2 is a bit different from the traditional navigation stack used in ROS. Nav2 employs a behaviour tree-based approach to enable complex, customizable robot actions and decision-making processes, allowing robots to react to changing environments and tasks. Now the navigation stack has 4 main components which are the Planner, Controller, Smoother and Recovery Servers. The planner generates a high-level path, the Controller executes low-level movements, the Smoother refines the trajectory for smoother motion, and the Recovery Servers handle unforeseen issues during navigation to ensure the robot can navigate effectively and safely in diverse environments.

The ROS Navigation Stack, also known as Nav2, is a comprehensive package designed for achieving autonomous navigation for mobile bases. It encompasses all the essential components required for navigation, including planning, sensing, and actuation, which are seamlessly integrated through the ROS packages included in Nav2. Furthermore, Nav2 offers a range of configurable options to tailor the behavior of the mobile base to specific requirements. This includes adjusting settings such as obstacle avoidance sensitivity, planning algorithms, velocity, and more.

Let me share an example - Imagine a delivery robot in a busy warehouse. The map server stores a detailed map of the warehouse layout. The localization system uses sensor data to continuously estimate the robot's position and orientation within the warehouse. When a delivery order comes in, the global planner calculates the optimal path to the target location while considering obstacles, equipment, and the fastest route. The local planner ensures the robot can navigate through the warehouse, avoiding moving forklifts and other dynamic obstacles in real time, ultimately enabling the robot to safely and efficiently reach its destination.

Use the Map Server to build a detailed map of the environment where the robot will navigate. Utilize the Goal Setter to define the destination or objective for the robot. Employ the Localization System to determine the robot's initial position on the map. The Global Planner calculates the optimal route considering the map and the goal. The Local Planner takes generating trajectories for the robot to follow, adapting to real-time data and environmental changes. Implement a mechanism for dynamic re-planning to handle unforeseen obstacles or alterations in the environment. Interface for Human Interaction: Utilize the Human-Robot Interaction Interface to facilitate user-friendly goal setting, progress monitoring, and intervention if needed.

Something which most people miss out on is the "namespace" while preparing the robot hardware. A namespace is a way to create a unique context for ROS nodes, topics, and services. In other words, it allows ROS nodes to have the same name but operate in different contexts. For example, two mobile robots in the same network may have nodes and topics with the same name, but the namespaces allow them to operate independently. This is one of the best practices I would follow while setting up the hardware for the robots.

While the hardware requirements laid out for ROS are on point, the dynamic nature of real-world environments presents unique challenges. The ROS navigation stack's capability to adapt to unpredictable elements, like sudden obstacles or environmental changes, is just as vital. Delving deeper into how ROS algorithms and tools tackle these real-world nuances would be a captivating topic for further exploration

When you are designing the platform , you should consider not only the sensor, but also a powerful enough computer for the application, in the case of a single laser lidar with a raspberry pi should be enough, but if you plan to use a velodyne or implement visual slam , should use a jetson Xavier or a laptop, also there is the possibility of remote implementation with an small computer in the robot an the complex calculus made in the cloud. This possible approach should be considered in the design stage.

It takes a day to set up the ROS navigation stack and several weeks to tune it. Every parameter matters and every parameter creates/solves an issue. Documentations take you 30% of the way, other people [code/forums] who have used a similar setup/planners/parameters take you an additional 40% of the way and the final 30% is a grind that you have to perform yourself for fine-tuning the parameters so that they can work for your own use-case. A rapid way for tuning is using rqt-reconfigure. Note: Sometimes it is better to forego the navigation stack for a simpler and cleaner approach for particular use-cases (especially for ROS1).

ROS can be powerful in hands of someone really dedicated that can spend most of his time figuring out how to manage this software. Sadly for beginners and people that need to do one project in ROS there is just not enough information in official documentation. 75% of the problems I have encountered during my 2 ROS projects could only be solved by reaching out on forums and by the help of my colleague who had years of expirience in ROS. Without senior ROS user helping you out it is very hard to begin the journey.

The only thing that changes from robot to robot is the navigation parameters. ROS navigation stack can be directly used on any mobile robot base provided one modifies the parameters according to the robot. A perfectly tuned navigation stack can avoid static and dynamic obstacles, and create the most efficient paths for navigation. I remember a case with Turtlebot4 where I forgot to change the parameter for obstacle avoidance and the robot kept hitting the obstacles.

While the steps to launch the ROS navigation stack are efficiently streamlined with the roslaunch command, I wonder about the adaptability of the stack across diverse robot configurations beyond the Turtlebot. Given that ROS is designed for flexibility, how does the AMCL localization system perform with less conventional robot designs or in more complex environments? Delving into these nuances could offer a broader understanding of ROS's true potential and versatility.

Launching the navigation stacks sounds easy while working with the tutorials but in reality, one has to pay attention to the logs while launching the stack. There are times when the servers are inactive and will not work as expected. When this happens check for the lifecycle nodes and check their status. It is important to understand the basic steps of SLAM while working with the ROS navigation stack. Also make sure the map you provided was properly made and includes all the static obstacles.

There are several ways to do this and the easiest way is to use the rviz tool to send the goal via the interface. But this is convenient when you do not know the exact poses on the map. But when the pose of the goals is known then we can use the Action client to send goals. This also allows to provide a set of goal poses as a list and iterate over it.

Here are some potential advantages of MoveBase Flex over MoveBase: MoveBase Flex is designed with a plugin-based architecture, which allows for greater flexibility and customization. This means you can easily extend or replace components like global planners, local planners, and recovery behaviors to better suit your specific use case. MoveBase Flex is designed to support multiple robots simultaneously. This can be crucial in scenarios where you have a fleet of robots that need to coordinate their movements. If you have non-standard hardware or sensors, MBF's plugin-based architecture can make it easier to integrate them into the navigation system.

These articles are probably the least helpful thing that exists on the Internet these days. They are super low effort with advice that is not really usable

Forbidden Zones: To create zones that robots do not go into or ideally navigate around: 1. Duplicate the original map.pgm and map.yaml and draw in with black color the zones you want to keep the robot out of 2. Create a separate map server for the duplicate maps with a name say map_zones e.g. /zones/zone_map 3. Create a plugin for the global_static_layer that takes in this map 4. Add the plugin to the global cost map - Note: High dependency on your local planner following your global planner, so you would need to tweak say the TEB planner to stay close to the global planner as it can still plan through the forbidden zones. The idea of creating a separate map server is to avoid interfering with the map being provided for localization.

Articles like this would be more valuable with (complete) examples. It helps to treat them like code, with links to dependencies and context, followed by steps which are entirely reproducible without requiring assumptions.

Just to get a "Top Voice" badge, people are making such low effort posts. ROS is just a pub-sub model. Not to mention that it is tough to make an article that would be valuable and generic enough to resonate with the community at large because practically, almost everyone writes their own "package" (which is just any other program that communicates through ROS's messaging framework). Every robot is different and the extent to which the navigation stack will be used varies greatly on hardware capabilities and the use case.