Robot Car: Localization and Mapping with Adaptive Monte Carlo Localization, SLAM, and Twist Mux Integration

This project builds on the previous robot car design, implementing advanced localization and mapping techniques. Initially tested with wheel odometry and probabilistic methods, the system was enhanced using Adaptive Monte Carlo Localization (MCL) and the ROS Navigation Stack (Nav Stack) integrated with SLAM. Additional functionality includes Twist Mux for relaying command velocities based on priority, enabling joystick-based control during mapping. The Waypoint Navigator was added to allow precise navigation to specified locations. A Lifecycle Node and Manager was implemented for smooth project state transition, allowing users to switch between mapping, localization, and planning modes via command line. These features were tested in multiple Gazebo environments, including an AWS house, warehouse, and custom world.

Installation

Prequisites

• Ubuntu 22.04 LTS

• ROS2 humble

• Gazebo

Step 1: Install ROS 2 Humble

Follow the ROS 2 Humble installation instructions for your operating system if ROS 2 is not already installed.

Step 2: Install Required ROS 2 Packages

Install the necessary ROS 2 packages.

sudo apt-get install ros-humble-ros2-control
sudo apt-get install ros-humble-ros2-controllers
sudo apt-get install ros-humble-xacro
sudo apt-get install ros-humble-ros-gz-*
sudo apt-get install ros-humble-*-ros2-control
sudo apt-get install ros-humble-joint-state-publisher-gui
sudo apt-get install ros-humble-turtlesim
sudo apt-get install ros-humble-robot-localization
sudo apt-get install ros-humble-joy
sudo apt-get install ros-humble-joy-teleop
sudo apt-get install ros-humble-tf-transformations

Step 3: Install Python Packages

Install Python and additional packages:

sudo apt-get install python3-pip
pip install transforms3d

Step 4: Additional Packages for Hardware Communication

To enable communication between the Arduino and ROS 2 using the Serial protocol:

sudo apt-get install libserial-dev

Run Locally

Clone the project

  git clone git@github.com:shivasamkumar/AGV_Kalman_Filter_Robot.git

Go to the project directory

  cd robot_car_v2

build the project

  colcon build 

Source your workspace

  source /opt/ros/humble/setup.bash
  source ~/workspace/install/setup.bash 

Launch the project

  ros2 launch robot_car_v2_bringup robot_car.launch.py use_slam:=false world_name:=small_house

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NOTE

• For mapping use_slam := True

• if using a different map for localization and planning specify it using map_name:= ... as the launch argument

• Maps and world files with models for each scenarios have been attached in the code hub

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Demo

The visulation of localization algorithm (creating voxel grid maps)

This image shows how to save the map using slam toobox

Waypoints created with the global trajectory.

Video

This demo shows the Mapping algorithm and the planning through rviz.The user first creates a map of the environment by controlling the robot car through joystick and then using the saved mad to localize and plannining. This project is digital twin and can deployed to external hardware (Rasberry pi) with slight modifications.

I have posted video in 3x speed !

click on the image for the video

click on the image for the video

This video shows the mapping in the Warehouse environment.

I have posted video in 3x speed !

Future versions

• Web-Based GUI

  Integrating a web-based control interface for remote access and operation, enabling users to control the robot arm from any device with internet access.

• Hardware Integration