DWA Local Planner for TurtleBot3 - ROS2 Humble

This project implements a custom Dynamic Window Approach (DWA) local planner for TurtleBot3 navigation in ROS2 Humble with Gazebo simulation.

Demo

Prerequisites

• Ubuntu 22.04 LTS
• ROS2 Humble
• Python 3.10+
• Gazebo 11

Installation and Setup

1. Install ROS2 Humble

# Add ROS2 repository
sudo apt update && sudo apt install curl gnupg lsb-release
sudo curl -sSL https://raw.githubusercontent.com/ros/rosdistro/master/ros.asc | sudo apt-key add -
echo "deb [arch=$(dpkg --print-architecture)] http://packages.ros.org/ros2/ubuntu $(lsb_release -cs) main" | sudo tee /etc/apt/sources.list.d/ros2.list > /dev/null

# Install ROS2 Humble
sudo apt update
sudo apt install ros-humble-desktop
sudo apt install ros-humble-gazebo-ros-pkgs
sudo apt install ros-humble-cartographer
sudo apt install ros-humble-cartographer-ros
sudo apt install ros-humble-navigation2
sudo apt install ros-humble-nav2-bringup

2. Install TurtleBot3 Packages

sudo apt install ros-humble-turtlebot3*

3. Set Environment Variables

Add to your ~/.bashrc:

source /opt/ros/humble/setup.bash
export TURTLEBOT3_MODEL=burger
export GAZEBO_MODEL_PATH=$GAZEBO_MODEL_PATH:/opt/ros/humble/share/turtlebot3_gazebo/models

Then reload:

source ~/.bashrc

4. Clone the Planner Repository

mkdir -p ~/ros2_ws/src
cd ~/ros2_ws/src
git clone https://github.com/mohittalwar23/dwa_local_planner.git

5. Build the Package

cd ~/ros2_ws
colcon build --packages-select dwa_local_planner
source install/setup.bash

Usage

1. Launch TurtleBot3 Simulation

Terminal 1 - Launch Gazebo world:

ros2 launch turtlebot3_gazebo turtlebot3_world.launch.py

Terminal 2 - Launch RViz for visualization:

ros2 launch turtlebot3_bringup rviz2.launch.py

Terminal 3 - Launch the DWA planner:

ros2 run dwa_local_planner dwa_planner

2. Set Navigation Goal

In RViz:

1. Add MarkerArray display with topic /dwa_trajectories
2. Use the "2D Goal Pose" tool to set a target destination
3. Watch the robot navigate while avoiding obstacles

3. Monitor Performance

View debug messages:

ros2 topic echo /rosout

Check current velocity commands:

ros2 topic echo /cmd_vel

Algorithm Overview

Dynamic Window Approach (DWA)

The DWA algorithm implemented here follows these steps:

1. Dynamic Window Generation: Sample velocity commands (linear and angular) within the robot's dynamic constraints
2. Trajectory Prediction: For each velocity sample, predict the robot's trajectory over a time horizon
3. Cost Evaluation: Evaluate each trajectory using a multi-objective cost function:
   • Goal Distance: Distance from trajectory endpoint to goal
   • Obstacle Avoidance: Minimum distance to obstacles along trajectory
   • Path Smoothness: Velocity magnitude and angular velocity penalties
4. Best Command Selection: Choose the velocity command with the lowest total cost

Cost Function

Total Cost = α * goal_cost + β * obstacle_cost + γ * velocity_cost

Where:

• α = 1.0 (goal weight)
• β = 2.0 (obstacle weight)
• γ = 0.1 (velocity weight)

Parameters

Key configurable parameters:

• max_linear_vel: 0.5 m/s
• max_angular_vel: 1.5 rad/s
• linear_acceleration: 2.0 m/s²
• angular_acceleration: 3.0 rad/s²
• prediction_time: 3.0 seconds
• dt: 0.1 seconds (simulation step)
• goal_tolerance: 0.2 meters

Troubleshooting

Common Issues

1. Robot not moving: Check if goal is set and within reasonable distance
2. Oscillating behavior: Reduce angular velocity samples or adjust cost weights
3. Collision with obstacles: Increase obstacle cost weight or reduce prediction time
4. RViz not showing trajectories: Ensure MarkerArray topic /dwa_trajectories is added

Performance Tuning

• Increase responsiveness: Reduce dt or prediction_time
• Smoother paths: Increase velocity cost weight
• Better obstacle avoidance: Increase obstacle cost weight
• Faster goal reaching: Increase goal cost weight

File Structure

dwa_local_planner/
├── dwa_local_planner/
│   ├── __init__.py
│   └── dwa_planner.py
├── launch/
│   └── dwa_navigation.launch.py
├── package.xml
├── setup.py
└── README.md

Testing Scenarios

1. Simple Navigation: Place goal in open space
2. Obstacle Avoidance: Navigate around static obstacles in Gazebo world
3. Narrow Passages: Test navigation through doorways