1.      Platform Introduction

Turtlebot3 is a small-sized UGV product recommended by the official ROS website. It runs on the ROS platform and is also called the “son of ROS”. Its cost is low, its dimensions are small, and it uses ROS standards and open-source ROS control boards, with a scalable structure and open-source code. It can be classified into two products: Turtlebot3 burger (basic version) and Turtlebot3 waffle (high-configuration version). These two products use different hardware platforms but both run on the ROS system.

2.      Platform Features

1. ROS-Based: Turtlebot3 is perfectly combined with the ROS system and is an ideal platform for learning ROS principles and carrying out ROS practice;
2. Supports MBD: Matlab Simulink provides an ROS support package that can realize automatic generation of ROS system code for Simulink control models, thus reducing the difficulty in developing ROS system control models and suiting general undergraduate teaching experiments.
3. Supports Independent Encoding: Turtlebot3 supports C, C++, and python programming and is suitable for students who possess certain levels of code development capabilities. It can further refine and improve their code programming capabilities;
4. TurtleBot3 Basic Version: Equipped with the Raspberry Pi 3b mainboard and 360° laser radar, capable of conducting SLAM modeling experiments;
5. TurtleBot3 High-Configuration Version: Equipped with the Intel joule mainboard, 360° laser radar, and Intel Realsense R200 visual sensors and recognition SDK, capable of conducting SLAM modeling experiments and image tracking experiments;
6. Open-Source Code: All the above experiments provide complete open-source demos along with corresponding training description documents.

3.      Experiment Contents

l  ROS system development environment knowledge;

l  Learning and use of ROS support packages in the Simulink environment;

l  Learning and use of Raspberry Pi hardware support packages in the Simulink environment;

l  Basic Burger;

• Hardware composition knowledge;
• Software development environment learning and use;

• Robot self-locating navigation based on laser SLAM

l  Advanced waffle;

• Hardware composition knowledge;
• Software development environment learning and use;
• Robot self-locating navigation based on laser SLAM;
• Visual deep processing and image following based on 3D cameras

4.      Model Selection and Configuration