Regulated Pure Pursuit

name: regulated-pure-pursuit description: 'Configure Nav2 Regulated Pure Pursuit controller. Use when setting lookahead distance, curvature regulation, or approach velocity scaling.'

Regulated Pure Pursuit Controller (RPP)

Algorithm Overview

Regulated Pure Pursuit is a path-tracking controller that follows a "carrot" (lookahead) point on the global path. The robot drives toward the carrot point using curvature-based steering. It adds regulation behaviors to slow the robot for tight turns, high-cost areas, and approach to the goal.

Plugin: nav2_regulated_pure_pursuit_controller::RegulatedPurePursuitController

RPP is the simplest and most predictable controller in Nav2. It has low CPU cost and is easy to tune.

Core Algorithm

1. Find the lookahead point on the global path at distance lookahead_dist ahead of the robot.
2. Compute the curvature to reach that point (pure pursuit geometry).
3. Command linear vel = desired_linear_vel, angular vel = curvature × linear_vel.
4. Apply regulation: slow for tight curvature, high costmap costs, proximity to goal.

Lookahead Configuration

Fixed Lookahead

lookahead_dist: 0.6  # meters ahead on path

Robot always looks 0.6m ahead. Simple but limited — at high speed you want to look further ahead.

Velocity-Scaled Lookahead (Recommended)

use_velocity_scaled_lookahead_dist: true
min_lookahead_dist: 0.3
max_lookahead_dist: 0.9
lookahead_time: 1.5  # seconds; lookahead = current_vel × lookahead_time

The lookahead distance scales with the robot's current speed: at low speed, look nearby (tight control); at high speed, look further (smooth anticipation). Clamped to [min_lookahead_dist, max_lookahead_dist].

Velocity and Acceleration

Regulation Behaviors

Curvature Regulation

use_regulated_linear_velocity_scaling: true
regulated_linear_scaling_min_radius: 0.9  # meters
regulated_linear_scaling_min_speed: 0.25  # m/s

When the path curvature is tight (radius < regulated_linear_scaling_min_radius), the linear velocity is scaled down toward regulated_linear_scaling_min_speed. Prevents the robot from taking tight turns at full speed.

Cost Regulation

use_cost_regulated_linear_velocity_scaling: true
cost_scaling_dist: 0.6          # meters from obstacle at which to start slowing
cost_scaling_gain: 1.0          # proportional gain for slowdown
inflation_cost_scaling_factor: 3.0  # must match costmap inflation_layer factor

When the robot is near high-cost cells (obstacles/inflation), it slows down. The inflation_cost_scaling_factor must match the cost_scaling_factor in your costmap inflation layer, or the cost-to-distance mapping is wrong.

Proximity to Goal

As the robot approaches the goal, it automatically decelerates. The deceleration profile is built in.

Rotate to Heading

Critical for differential-drive robots. When the path direction changes significantly, RPP can rotate the robot in place before driving.

use_rotate_to_heading: true
rotate_to_heading_min_angle: 0.785  # radians (~45°); angular error to trigger rotation
rotate_to_heading_angular_vel: 1.8  # rad/s during in-place rotation
max_angular_accel: 3.2              # acceleration limit during rotation

Behavior: If the heading error to the lookahead point exceeds rotate_to_heading_min_angle, the robot stops and rotates in place until aligned, then resumes driving.

Important: use_rotate_to_heading and allow_reversing are mutually exclusive. You cannot enable both.

allow_reversing: false  # must be false when use_rotate_to_heading is true

Complete YAML for Differential Drive

controller_server:
  ros__parameters:
    controller_frequency: 20.0
    min_x_velocity_threshold: 0.001
    min_theta_velocity_threshold: 0.001
    FollowPath:
      plugin: "nav2_regulated_pure_pursuit_controller::RegulatedPurePursuitController"
      desired_linear_vel: 0.5
      max_angular_accel: 3.2
      # Lookahead
      use_velocity_scaled_lookahead_dist: true
      min_lookahead_dist: 0.3
      max_lookahead_dist: 0.9
      lookahead_time: 1.5
      # Curvature regulation
      use_regulated_linear_velocity_scaling: true
      regulated_linear_scaling_min_radius: 0.9
      regulated_linear_scaling_min_speed: 0.25
      # Cost regulation
      use_cost_regulated_linear_velocity_scaling: true
      cost_scaling_dist: 0.6
      cost_scaling_gain: 1.0
      inflation_cost_scaling_factor: 3.0
      # Rotate to heading (diff-drive)
      use_rotate_to_heading: true
      rotate_to_heading_min_angle: 0.785
      rotate_to_heading_angular_vel: 1.8
      max_angular_accel: 3.2
      allow_reversing: false
      # Tolerances
      transform_tolerance: 0.1

When to Use RPP

Advantages:

• Very low CPU cost. Negligible compared to MPPI.
• Predictable behavior — always follows the path carrot.
• Easy to tune — fewer parameters, intuitive behavior.
• Reliable in simple environments.

Disadvantages:

• Does not optimize trajectories — follows the carrot naively.
• Less capable in narrow passages or around complex obstacles (no trajectory scoring).
• No dynamic obstacle reaction beyond what the planner provides.
• Cannot exploit gaps or find shortcuts.

Best for: Simple patrol tasks in open areas, robots with low computational resources, or as a baseline controller before upgrading to MPPI.

Common Tuning Issues

• Robot oscillates on straight paths: lookahead_dist too small or use_velocity_scaled_lookahead_dist not enabled.
• Robot cuts corners: Increase min_lookahead_dist or regulated_linear_scaling_min_radius.
• Robot stops at sharp turns: rotate_to_heading_min_angle too low (triggers on small corrections). Increase to 0.785–1.0.
• Robot too slow near walls: Check inflation_cost_scaling_factor matches costmap; reduce cost_scaling_gain.
• Robot jerks at start of path: Needs RotationShimController to orient before RPP starts.