Nav2 Local & Global Planners

Now that we have discussed the need to validate safety protocols and data protection policies—and identified each—it is time to explore what makes a robot autonomous. Specifically, we will dive into how robots make decisions in different scenarios and how planners influence these decisions during operation.

Robots rely on two types of planners to navigate their environments: local planners and global planners. Each plays a distinct role in ensuring safe, efficient, and autonomous operation. Take a look at the table below to understand the key differences between them:

Key Differences Between Local and Global Planners

Aspect	Local Planner	Global Planner
Scope	Handles short-term, real-time navigation and obstacle avoidance.	Plans long-term paths from the robot’s start point to its goal.
Focus	Dynamic obstacle handling and maintaining a smooth, collision-free trajectory.	Optimizing the overall route based on the map, environment, and defined goals.
Frequency	Operates at a higher frequency (e.g., 10 Hz) for real-time adjustments.	Operates at a lower frequency (e.g., 1 Hz) to minimize computational overhead.
Impact on Safety	Ensures immediate responsiveness to nearby obstacles and path deviations.	Avoids hazardous routes or areas that could compromise the robot’s operation.
Key Parameters	Velocity limits, inflation radius, yaw tolerance for precise control.	Map cost layers, goal tolerance for efficient and safe long-term planning.

Note

Robots leverage these planners to make informed decisions, balancing safety, efficiency, and adaptability in diverse scenarios.

Nav2 Key Parameters

The Nav2 stack provides a robust framework for implementing local and global planners, each tailored to specific use cases and operational environments. The table below categorizes the available planners and controllers:

Nav2 Global Planners and Local Controllers

Type	Name	Description
Global Planner	NavFn Planner	Utilizes Dijkstra’s algorithm to compute the shortest path on a costmap.
Global Planner	Smac Planner	Offers different variants, including 2D and Hybrid-A* planners, suitable for various robot types and environments.
Global Planner	Theta Planner	Computes paths that are more direct by allowing diagonal movements, reducing unnecessary turns.
Local Controller	DWB (Dynamic Window Approach)	Evaluates a set of possible trajectories and selects the one that optimally balances progress toward the goal, speed, and obstacle avoidance.
Local Controller	Regulated Pure Pursuit	Focuses on following the global path accurately, adjusting the robot’s speed based on proximity to obstacles and path curvature.
Local Controller	MPPI (Model Predictive Path Integral)	Uses a model predictive control approach to optimize control commands over a future horizon, considering the robot’s dynamics and environmental constraints.
Local Controller	Rotation Shim Controller	Handles in-place rotation behaviors, ensuring the robot can correctly orient itself before proceeding along the path.

Note

In the following sections, we will explore how these planners influence robot behavior in specific scenarios:

1. Going in a Straight Line

2. Navigating Around Static Obstacles

3. Navigating Around Dynamic Obstacles

For each scenario, we will analyze how different local and global planners affect safety, efficiency, and overall performance.

Nav2 Experimental Parameters

These are the default parameters of the Nav2 stack. In the following experiments, these parameters will be tweaked for different scenarios, planners, and controllers.

Navigation Experiment Parameters

Parameter Group	Parameter Name	Value	Purpose/Description
AMCL Configuration	use_sim_time	True	Ensures simulation time is used for synchronization.
	base_frame_id	base_footprint	Specifies the robot’s base frame for localization.
	global_frame_id	map	Specifies the global frame for localization.
	max_particles	2000	Sets the maximum number of particles for localization accuracy.
	min_particles	500	Sets the minimum number of particles for efficiency.
	transform_tolerance	1.0	Ensures smooth transform updates.
Controller Server	controller_frequency	20.0	Frequency at which local controllers compute control commands.
	min_x_velocity_threshold	0.001	Minimum allowable velocity along the x-axis.
	FollowPath.max_vel_x	0.26	Maximum linear velocity in the x-direction.
	FollowPath.max_vel_theta	1.0	Maximum angular velocity.
	FollowPath.sim_time	1.7	Simulation time for trajectory evaluation.
	FollowPath.xy_goal_tolerance	0.25	Tolerance for reaching the goal in the x and y directions.
Costmap Parameters	local_costmap.width	3.0	Defines the width of the local costmap in meters.
	local_costmap.height	3.0	Defines the height of the local costmap in meters.
	local_costmap.inflation_radius	0.55	Adds a buffer zone around obstacles for safety.
	global_costmap.inflation_radius	0.55	Adds a buffer zone in the global map for safety.
Planner Server	GridBased.plugin	nav2_navfn_planner/NavfnPlanner	Uses the NavFn global planner for path computation.
	GridBased.tolerance	0.5	Tolerance for reaching the goal in global planning.
	GridBased.allow_unknown	true	Allows planning through unknown areas on the map.
Behavior Server	cycle_frequency	10.0	Frequency at which behaviors are checked.
	transform_tolerance	0.1	Ensures smooth transform updates for behaviors.
Velocity Smoother	smoothing_frequency	20.0	Frequency of smoothing velocity commands.
	max_velocity	[0.26, 0.0, 1.0]	Maximum linear and angular velocities for the robot.
	max_accel	[2.5, 0.0, 3.2]	Maximum acceleration limits for the robot.
General Parameters	use_sim_time	True	Enables simulation time for all components.
	robot_base_frame	base_link	Specifies the base frame for the robot’s operations.
	global_frame	map	Specifies the global frame for navigation.