The ROS2 Navigation Stack (Nav2)

How It Works

While the original ROS navigation stack was a simple finite state machine, Nav2 introduces a highly modular and extensible architecture based on Behavior Trees. This allows for complex navigation logic that is easier to debug and customize. The system operates through a pipeline of interacting nodes:

1. The Navigation Behavior Tree

At the core of Nav2 is the Behavior Tree (BT) Navigator. Unlike linear logic, a Behavior Tree creates a hierarchical structure of tasks. It decides when to plan a path, when to follow it, and when to trigger recovery actions (like backing up or spinning) if the robot gets stuck. This makes the robot's decision-making process highly dynamic and reactive.

2. Global and Local Planners

Navigation is split into two distinct planning phases:

• Global Planner: Calculates the optimal path from the robot's current position to the goal across the entire map. It uses algorithms like A* or Dijkstra to find a path free of static obstacles.
• Local Planner (Controller): Once the global path is set, the Controller generates velocity commands (cmd_vel) for the motors. It accounts for dynamic obstacles (like people walking by) and robot kinematics to keep the robot on the global path locally.

3. Costmaps

Nav2 maintains two "costmaps" (2D grid representations of the world where grid cells represent obstacle danger):

• Global Costmap: Used by the Global Planner, usually built from a static map (created via SLAM).
• Local Costmap: A rolling window around the robot that updates in real-time using sensor data (LiDAR, Depth Cameras). This allows the robot to react to immediate environmental changes.

4. Lifecycle Managed Nodes

A distinct feature of ROS2 is the use of Lifecycle Nodes. Nav2 ensures that all servers (planners, controllers, map servers) start up in a strict order (Unconfigured → Inactive → Active). This prevents the "undefined behavior" often seen in robotics where a robot tries to move before its sensors or maps are fully loaded.

Applications in Robotics

The modularity of Nav2 makes it the navigation solution of choice for a wide variety of autonomous machinery:

• Intralogistics & Warehousing: Powering Autonomous Mobile Robots (AMRs) that transport pallets and packages in dynamic warehouse environments.
• Service Robotics: Used in commercial floor scrubbers, hospital delivery bots, and hospitality waiters that must navigate crowded corridors safely.
• Agriculture: enabling autonomous tractors and harvesters to navigate rows of crops without damaging them using GPS and local sensing.
• Security and Patrol: allowing security robots to patrol geofenced perimeters and dynamically avoid unexpected obstacles.

Related ChipSilicon Tech

Running the ROS2 Navigation Stack requires significant computational resources, particularly for real-time costmap updates and complex path-planning algorithms. ChipSilicon technologies empower mobile robots to utilize Nav2 effectively:

• Edge AI Processors: Our high-performance SoCs are optimized to process LiDAR and camera data locally, allowing for high-frequency updates to the Local Costmap with minimal latency.
• Real-Time Microcontrollers (MCUs): ChipSilicon MCUs interface directly with motor drivers to execute the velocity commands sent by the Nav2 Controller with strict real-time guarantees, ensuring smooth motion.
• Hardware Acceleration for SLAM: Our dedicated DSPs accelerate the math behind Simultaneous Localization and Mapping (SLAM), ensuring the robot knows exactly where it is within the Global Costmap.