The Nav2 project is the spiritual successor of the ROS Navigation Stack. This project seeks to find a safe way to have a mobile robot move from point A to point B. It can also be applied in other applications that involve robot navigation, like following dynamic points. This will complete dynamic path planning, compute velocities for motors, avoid obstacles, and structure recovery behaviors. To learn more about this project, such as related projects, robots using, ROS1 comparison, and maintainers, see About and Contact.

Nav2 uses behavior trees to call modular servers to complete an action. An action can be to compute a path, control effort, recovery, or any other navigation related action. These are each separate nodes that communicate with the behavior tree (BT) over a ROS action server. The diagram below will give you a good first-look at the structure of Nav2. Note: It is possible to have multiple plugins for controllers, planners, and recoveries in each of their servers with matching BT plugins. This can be used to create contextual navigation behaviors. If you would like to see a comparison between this project and ROS (1) Navigation, see ROS to ROS 2 Navigation.

The expected inputs to Nav2 are TF transformations conforming to REP-105, a map source if utilizing the Static Costmap Layer, a BT XML file, and any relevant sensor data sources. It will then provide valid velocity commands for the motors of a holonomic or non-holonomic robot to follow. We currently support all of the major robot types: holonomic, differential-drive, legged, and ackermann (car-like) base types! We support them uniquely with both circular and arbitrarily-shaped robots for SE2 collision checking.

It has tools to:

  • load, serve, and store maps (Map Server)

  • localize the robot on the map (AMCL)

  • plan a path from A to B around obstacles (Nav2 Planner)

  • control the robot as it follows the path (Nav2 Controller)

  • Smooth path plans to be more continuous and feasible (Nav2 Smoother)

  • convert sensor data into a costmap representation of the world (Nav2 Costmap 2D)

  • build complicated robot behaviors using behavior trees (Nav2 Behavior Trees and BT Navigator)

  • Compute recovery behaviors in case of failure (Nav2 Recoveries)

  • Follow sequential waypoints (Nav2 Waypoint Follower)

  • Manage the lifecycle and watchdog for the servers (Nav2 Lifecycle Manager)

  • Plugins to enable your own custom algorithms and behaviors (Nav2 Core)

Navigation2 Block Diagram

We also provide a set of starting plugins to get you going. NavFn computes the shortest path from a pose to a goal pose using A* or Dijkstra’s algorithm. DWB will use the DWA algorithm to compute a control effort to follow a path, with several plugins of its own for trajectory critics. There are recovery behaviors included: waiting, spinning, clearing costmaps, and backing up. There are a set of BT plugins for calling these servers and computing conditions. Finally, there are a set of Rviz plugins for interacting with the stack and controlling the lifecycle. A list of all user-reported plugins can be found on Navigation Plugins.

Here is the documentation on how to install and use Nav2 with an example robot, Turtlebot 3 (TB3), as well as how to customize it for other robots, tune the behavior for better performance, as well as customize the internals for advanced results.


If you use the navigation framework, an algorithm from this repository, or ideas from it please cite this work in your papers!

S. Macenski, F. Martín, R. White, J. Clavero. The Marathon 2: A Navigation System. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020.

IROS 2020 talk on Nav2 Marathon Experiments:

author = {Macenski, Steven and Martin, Francisco and White, Ruffin and Ginés Clavero, Jonatan},
title = {The Marathon 2: A Navigation System},
booktitle = {2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = {2020}

If you use our work on VSLAM and formal comparisons for service robot needs, please cite the paper:

A. Merzlyakov, S. Macenski. A Comparison of Modern General-Purpose Visual SLAM Approaches. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021.

author = {Merzlyakov, Alexey and Macenski, Steven},
title = {A Comparison of Modern General-Purpose Visual SLAM Approaches},
booktitle = {2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = {2021}


Below is an example of the TB3 navigating in a small lounge.