Warebot: ROS Intelligent Robot System Project

By Edmund Ngu and Jaden Chen, Computer Science Undergraduate (Major in Intelligent Machine) from Faculty of Information Science & Technology (FTSM),
The National University of Malaysia / Universiti Kebangsaan Malaysia (UKM)

Course: TTTC2343 Intelligent Robot System

Contents

• Introduction
• Features
• Simulation Environment in Gazebo
• Techniques
• AruCo Location
• Implementation
• Working Environment
• Video
• References

Introduction

Warebot is a robot system work as a warehouse worker. As you can see, the name Warebot come from Warehouse + robot.

Warebot works to receive the order at counter, move to the specified shelf/rack to take the items, and drop it at the drop point in the warehouse.

The purpose of this project is to build a robot system in Gazebo with ROS programming to stimulate the workflow.

Features

1. Autonomous Navigation

   Moving from Point A to Point B.

2. Object Following

   • Following known object.

   • Detect ArUco and follow the rules based on the ArUco.

Simulation Environment in Gazebo

Warehouse

Rack

Techniques

1. SLAM (Simultaneous Localization and Mapping)
   • To create a map of environment by predicting the real-time location of robot position in arbitary space.

2. Rviz (ROS Visualization)

3. find_object_2d + ArUco

ArUco Location

Implementation

How to Work (Step).

Preparation

1. Copy the ArUco Model package in /aruco_model folder to Gazebo Model directory

   $HOME/<USER>/.gazebo/models
   

2. Copy the warebot folder to your catkin workspace and catkin_make it in terminal.

   $ catkin_make

Step 1: (In 1st terminal tab)

• Run warebot_mission

  $ roslaunch warebot warebot_mission.launch

Step 2: (In 2nd terminal tab)

In directory /warebot/maps/, change the image path point to the map.pgm in map.yaml file as follow:

image: <your path>/map.pgm

• Run Rviz

  $ export TURTLEBOT3_MODEL=waffle_pi

  $ roslaunch turtlebot3_navigation turtlebot3_navigation.launch map_file:=$HOME/catkin_ws/src/warebot/warebot/maps/map.yaml

Step 3:

Since this is not a perfect work, use 2D Pose Estimate in Rviz to adjust the map position.

Step 4:

Press s to start.

Done!

Working Environment

OS: Ubuntu 16.04 LTS

Software: Gazebo

Machine Model: waffle_pi (turtlebot)

Video

Simulation of Warebot.

Youtube: Warebot

References

1. Visual object recognition by Husarion

License

Copyright © 2020, Edmund Ngu and Jaden Chen.