Path Planning with ROS2 Nav2: Turn Your Map into Motion

Once your robot has a map and knows where it is on that map, the next question becomes simple: Where do you want to go? That’s the promise of ROS2 Nav2—an open-source navigation stack that can translate user goals into actionable movement using maps, real-time data, and motion planning algorithms. In this blog, we’ll walk through the first real Nav2 deployment—transforming your floorplan into a mission-ready route. Consider this your ROS2 navigation beginner guide.

1. Switching on Nav2 — “Giving the Robot Google Maps”

Analogy: Installing a GPS into a self-aware car.

Once your robot can localize itself using AMCL and interpret maps built via SLAM, it’s time to bring up the Nav2 stack. Nav2 acts like a high-level navigator. Instead of driving line by line, you assign goals, and the robot figures out the path using global and local planners.

What You Need:

• A map file generated from SLAM (my_map.yaml and my_map.pgm)
• A robot-specific YAML config file for parameters (wheel radius, max velocity, TF transforms, etc.)

How:

ros2 launch nav2_bringup bringup_launch.py \
  use_sim_time:=false \
  map:=my_map.yaml \
  params_file:=my_robot_nav2_config.yaml

This command starts up all necessary components: AMCL, lifecycle manager, map server, planner, controller, recovery behaviors, and the behavior tree engine. Your robot now listens to goals and computes paths accordingly.

Make sure the TF tree is properly set. You need transforms between base_link, odom, and map. If you haven’t configured TF yet, check Robotisim’s ROS2 Localization Guide to align all coordinate frames.

2. Tuned vs. Untuned — “Driving on Snow Tires vs. Slicks”

Why Tuning Matters: A default Nav2 config will likely get your robot moving, but it might result in jerky motion, excessive stops, or even incorrect pathing around obstacles. Proper tuning affects path smoothness, responsiveness, and energy efficiency.

Visual Example:

Default values often cause hesitation at corners or erratic zig-zagging. When tuned, the robot glides through tight spaces with confidence.

Key Parameters to Tune:

• inflation_radius: Defines how far around obstacles the robot should avoid. Too small, and you’ll clip furniture. Too large, and your path may become inefficient.
• max_vel_x: Sets the top linear velocity. Keep it conservative initially for testing.
• dwa_time_to_collision: Higher values let the robot plan farther ahead; lower values make it react quicker to nearby threats.

Refer to the official Nav2 tuning guide for detailed explanations of local and global planner parameters.

3. Step-by-Step First Mission

Once your launch file is running and parameters tuned, it’s time for a test mission using RViz.

Step-by-Step:

1. Open RViz and click “2D Nav Goal.”
2. Click anywhere on the map with an orientation arrow.
3. The global planner computes a green path from current pose to goal.
4. The local planner (e.g., DWB or TEB) plots a blue line with real-time obstacle avoidance.
5. The robot moves. If configured properly, AMCL tracks its pose with less than 5 cm error.

The blue and green paths represent the multi-level planning system in Nav2. Global planning sets the destination; local planning adjusts the route based on real-time sensory feedback.

This layered system ensures that even if a new chair rolls into the path, the robot detours without needing a full replan.

4. Where It Can Trip Up

Even a fully mapped and configured robot might fail during execution. Here’s a quick fix table for common ROS2 Nav2 issues:

Failure Scene	Analogy	Quick Fix
Laser blind spot at glossy floor	Sunglasses in fog	Lower min_height in point cloud filter
Gets stuck in door frame	Truck too wide for alley	Reduce footprint padding in costmap config
Spins forever at goal	Dog chasing its tail	Increase xy_goal_tolerance and yaw_goal_tolerance

Additional Tips:

• Check AMCL convergence by monitoring the /amcl_pose topic.
• Use ros2 topic echo /local_costmap/costmap to debug obstacles.
• If pose error grows during movement, revisit ROS2 sensor fusion tuning to stabilize input streams.

5. Five Killer Features of Nav2

ROS2 Nav2 isn’t just path planning—it’s a complete navigation framework built for autonomy. Here are five standout features:

1. Behavior Trees

Design complex navigation logic (e.g., patrol, search, return) using modular blocks. You can dynamically swap these behaviors with minimal code changes.

2. Dynamic Re-Planning

When a new obstacle appears, Nav2 recalculates the path mid-run. This feature is vital for changing environments like offices or hospitals.

3. Plugin Interfaces

Global planners, local planners, and even costmaps are plugin-based. Want a custom A* variant? No need to fork the entire stack—just write a plugin.

4. Life-Cycle Nodes

Each Nav2 component can be individually activated, deactivated, or cleaned up. This helps save CPU resources and maintain modular control during runtime.

5. Simulation Compatibility

Everything you configure for real hardware works out-of-the-box in Gazebo. This means your tuning efforts are portable across simulation and real-world deployments.

These features place Nav2 at the forefront of modern robot navigation systems, especially when paired with strong sensor fusion and accurate localization.

6. Ready for the Open Road

Analogy: Passing your driving test—now the road is yours.

Once you’ve built a map, fine-tuned your motion parameters, and tested with a few navigation goals, your robot is now truly autonomous. With AMCL handling localization, costmaps configured for real obstacles, and Nav2 translating clicks into motion—it’s ready to explore.

Here’s how to go further:

• Try multi-goal missions using custom Behavior Trees.
• Integrate voice or app-based goal-setting via ROS 2 actions.
• Enable waypoint following and patrol behaviors.

And don’t forget to record bags of your missions

ros2 bag record /cmd_vel /amcl_pose /scan /tf

Reviewing these bags will help improve future navigation accuracy.

Internal and External Resources

• Robotisim: ROS2 Sensor Fusion — EKF & AMCL
• Robotisim: Mapping 101 — First SLAM Run
• Nav2 Official Docs
• ROS2 Tutorials on GitHub

Final Thoughts

Planning paths with ROS2 Nav2 isn’t about blindly executing instructions—it’s about giving your robot the power to interpret space, predict motion, and make smart navigation decisions.

From inflation_radius to AMCL pose nav2 error correction, your configuration decisions make all the difference. Invest in tuning, review sensor data, and leverage the extensibility of ROS2 Nav2 to create agile, reliable robotic navigation.