Simon Fraser University
Engaging the World

Course Outlines

Spring 2025 - MSE 112 D100

Mechatronic Design Studio I (3)

Class Number: 6228

Delivery Method: In Person

Overview

  • Course Times + Location:

    Jan 6 – Apr 9, 2025: Mon, 10:30–11:20 a.m.
    Surrey

    Jan 6 – Apr 9, 2025: Thu, 10:30 a.m.–12:20 p.m.
    Surrey

  • Prerequisites:

    CMPT 120.

Description

CALENDAR DESCRIPTION:

An introduction to the field of mechatronics and relevant hands-on experience in designing and programming robotic systems. Through a combination of theory, practical exercises, and project work, students will gain a solid foundation in programming using Python, learn the basics of a microcontroller platform, and develop the skills necessary to build and control simple robots. Topics include sensors, actuators and data acquisition techniques in sensory-based systems. Students with credit for MSE 110 may not take this course for further credit.

COURSE DETAILS:

Course Outline 
Module 1: Introduction to Mechatronics and Raspberry Pi
Module 2: Raspberry Pi Programming and GPIO (Part 1)
Module 3: Raspberry Pi Programming and GPIO (Part 2)
Module 4: Introduction to Machine Vision
Module 5: Introduction to Machine Learning and Object Detection
Module 6: Robot Arm Kinematics
Module 7: Advanced Robotics with Mecanum Wheels
Module 8: Fundamentals of Robot Design and Construction
Module 9: Open Topics in Mechatronics

COURSE-LEVEL EDUCATIONAL GOALS:

By the end of the course, students will be able to:
1. Understand the fundamentals of mechatronic design and its applications.
2. Write programs in Python to control robotic systems.
3. Utilize the RPi platform to interface with sensors and actuators.
4. Identify and analyze different types of sensors used in mechatronic systems.
5. Design algorithms to read signals from sensors and control actuators.
6. Construct and program a robot using an RPi platform.
7. Demonstrate the ability to accomplish simple tasks with the robot.
8. Acquire and process data from sensors in a sensory-based system.
9. Learn basics of computer vision using OpenCV (Open-Source Computer Vision Library) and its applications in Robotics
10. Learn the application of Machine Learning in Robotics

Grading

  • Labs 35%
  • Projects 40%
  • Term Exam I 10%
  • Term Exam II 15%

Materials

REQUIRED READING NOTES:

Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.

Registrar Notes:

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS

SFU’s Academic Integrity website http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating. Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the university community. Academic dishonesty, in whatever form, is ultimately destructive of the values of the university. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the university. http://www.sfu.ca/policies/gazette/student/s10-01.html

RELIGIOUS ACCOMMODATION

Students with a faith background who may need accommodations during the term are encouraged to assess their needs as soon as possible and review the Multifaith religious accommodations website. The page outlines ways they begin working toward an accommodation and ensure solutions can be reached in a timely fashion.