Ackermann Steering Vehicle Simulation in ROS2 with Gazebo Sim Harmonic

This project features the simulation of a custom vehicle with Ackermann steering capabilities, developed using ROS2 and the Gazebo Sim Harmonic environment. The model integrates a variety of sensors and navigation tools for autonomous operation, making it one of the first implementations of an Ackermann steering vehicle in this simulation framework.

3D LiDAR Point Cloud Visualization	Warehouse Environment Model

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Table of Contents

• Ackermann Steering Vehicle Simulation in ROS2 with Gazebo Sim Harmonic
• Features
  • 1 Ackermann Steering
  • 2 ROS2 Communication
  • 3 Sensors
  • 4 Navigation
  • 5 Manual Control with external joystick
  • 6 Visualization
• Requirements
• Local Installation
• Docker Installation
• Usage
  • 1 Basic Simulation and Manual Control
  • 2 SLAM Simultaneous Localization and Mapping
  • 3 Navigation with Nav2
• Future Work
• Gallery
• TF Tree
• Star History

Features

1. Ackermann Steering

• A custom vehicle model built with realistic Ackermann steering dynamics for accurate maneuverability.

2. ROS2 Communication

• All sensor data and control signals are fully integrated into the ROS2 ecosystem for seamless interoperability.

3. Sensors

• IMU: Provides orientation and angular velocity.
• Odometry: Ensures accurate vehicle state feedback.
• LiDAR: Mounted for obstacle detection and environmental scanning. Supports 3D point cloud generation for advanced perception tasks.
• Cameras:
  • Front-facing
  • Rear-facing
  • Left-side
  • Right-side

    Note: By default, only the front camera is bridged to ROS 2.If you want to use all cameras (left, right, rear) in ROS 2,remove the # at the beginning of the relevant camera sections in saye_bringup/config/ros_gz_bridge.yaml to activate them (e.g., /camera/left_raw, /camera/right_raw, /camera/rear_raw).

4. Navigation

• Integrated with the Nav2 stack for autonomous navigation.
• AMCL (Adaptive Monte Carlo Localization) for improved positional accuracy.
• SLAM techniques implemented for real-time mapping and understanding of the environment.
• Fine-tuned parameters for optimized navigation performance.

5. Manual Control (with external joystick)

• Added support for joystick-based manual control in the simulation environment, enabling users to test vehicle movement interactively.

6. Visualization

• Full model and sensor data visualization in RViz2, providing insights into robot states and environmental feedback.

Requirements

• ROS2 (Humble or Jazzy) — the Docker image is built on Jazzy
• Gazebo Sim Harmonic
• RViz2
• Nav2

Local Installation

1. Your need to sure that installation of Gazebo Harmonic and ROS (ros_gz):
   sudo apt-get install ros-${ROS_DISTRO}-ros-gz
sudo apt-get install ros-humble-ros-gzharmonic (Humble only)
   sudo apt-get install ros-${ROS_DISTRO}-teleop-twist-keyboard ros-${ROS_DISTRO}-joy (manual control)
   More details about installation Gazebo and ROS: Link
2. Clone the repository:
   mkdir -p ackermann_sim/src && cd ackermann_sim/src
git clone https://github.com/alitekes1/ackermann-vehicle-gzsim-ros2
cd ..
3. Build the project: colcon build && source install/setup.bash
4. GZ_SIM_RESOURCE_PATH is set automatically when you source the workspace (source install/setup.bash); no manual environment variables are needed. If you open a new terminal, just source the workspace again.

Docker Installation

You can also run the simulation using Docker, which ensures a consistent environment across different systems.

Prerequisites

• Docker
• NVIDIA Container Toolkit (for GPU support)

Steps to Run with Docker

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