Home Service Robot

On this Udacity project we simulate a full home service robot capable of navigating to pick up and deliver virtual objects. The robot we use is the turtlebot provided by ROS. The robot is capable to localize, map and navigate using a fusion of LIDAR, camera and odometer. We have used the SLAM_gmapping module and the AMCL adaptive Monte Carlo localization provided by ROS.

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Introduction

Localization and Mapping

GMapping is a highly efficient Rao-Blackwellized particle filer to learn grid maps from laser range data. Using SLAM_gmapping, you can create a 2-D occupancy grid map from laser and pose data collected by the robot.

Navigation

For navigation and object avoidance we use the ROS Navigation stack, which is based on the Dijkstra's, a variant of the Uniform Cost Search algorithm.

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Dependencies

• Ubuntu 16.04
• ROS Kinetic Kame
• AMCL ROS Module
• slam_gmapping

Installation

• Create a workspace

    mkdir -p catkin_ws
    cd catkin_ws
  

• Clone this repository on the src folder location

    git clone --recursive [email protected]:hurtadosanti/home-service-robot.git ./src
  

• Initialize workspace

    cd src
    catkin_init_workspace
  

• Build

    cd ..
    catkin_make
  

Execution

Run the main program in a terminal with X support

cd catkin_ws
source devel/setup.bash
./src/scripts/home_service.sh

License

MIT License Copyright (c) 2021 Santiago Hurtado