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.

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.
• Cameras:
  • Front-facing
  • Rear-facing
  • Left-side
  • Right-side

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)
• Gazebo Sim Harmonic
• RViz2
• Nav2

Installation and Usage

Run the following commands to set up and launch the simulation:

1. Clone the repository: git clone https://github.com/alitekes1/ros2-ackermann-vehicle-gz-sim-harmonic-nav2 cd ros2-ackermann-vehicle-gz-sim-harmonic-nav2/
2. Build the project: colcon build && source install/setup.bash
3. Set environment variables: export GZ_SIM_RESOURCE_PATH=$GZ_SIM_RESOURCE_PATH:/your/path/ros2-ackermann-vehicle-gz-sim-harmonic-nav2/src export ROS_PACKAGE_PATH=$ROS_PACKAGE_PATH:/your/path/ros2-ackermann-vehicle-gz-sim-harmonic-nav2/src
4. Launch the simulation: ros2 launch saye_bringup saye_spawn.launch.py

Future Work

1. Deep Reinforcement Learning (DRL):
   • Train the vehicle to handle complex scenarios autonomously using advanced DRL algorithms.
2. Enhanced Features:
   • Explore additional sensor configurations and navigation strategies.

Gallery

Gazebo Sim Harmonic	RViz2

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