Robot Design Practice

Course title

Y0156400

Course description

In this practicum, students will learn about the key components of robotics engineering, such as sensors, actuators, and processors, using LEGO MINDSTORMS EV3.
Along the way, students will also explore the application of machine learning techniques in robot control, implementing these techniques with Python programs.

Purpose of class

Students will deepen their understanding of each component in a robot system through hands-on robot-building exercises.
Additionally, students will gain knowledge of machine learning techniques and learn how to apply them to robotic control.

Goals and objectives

Students understand how a robot system works
Students understand the roles of sensors, actuators, controllers, and processors.
Students understand the theory behind machine learning techniques used in robot control and be able to apply them.
Students can design and build a robot system.

Relationship between 'Goals and Objectives' and 'Course Outcomes'

	midterm competition1	midterm competition2	Final competition	Total.
1.	10%	10%	10%	30%
2.	10%	0%	10%	20%
3.	0%	10%	15%	25%
4.	5%	5%	15%	25%
5.	0%			0%
Total.	25%	25%	50%	-

Language

Japanese

Class schedule

	Class schedule	HW assignments (Including preparation and review of the class.)	Amount of Time Required
1.	Course introduction and setup of the development environment	Understand an overview of robotic systems.	90minutes
2.	Constructing a basic robot system (line tracer building exercise)	Understand the role of various sensors and actuators	90minutes
3.	Constructing a basic robot system (line tracer building exercise)	Understand the role of a processor and a controller	90minutes
4.	Constructing a basic robot system (line tracer building exercise)	Understand the basic programming for a robot system	90minutes
5.	Fundamentals of machine learning and hands-on practice (supervised learning)	Understand the basic algorithms of supervised learning	90minutes
6.	Fundamentals of machine learning and hands-on practice (image processing)	Understand the machine learning based supervised learning algorithms such as classification and object detection.	90minutes
7.	Exercises in robot control using machine learning techniques (Automatic factory)	Understand the basic knowledge for developing dataset.	90minutes
8.	Exercises in robot control using machine learning techniques (Automatic factory)	Understand the application of the machine learning algorithms for the robot control	90minutes
9.	Exercises in robot control using machine learning techniques (Automatic factory)	Understand the application of the machine learning algorithms for the robot control	90minutes
10.	Planning for the final project	Planning for the robot design.	90minutes
11.	Robot construction	Complement the robot construction work.	90minutes
12.	Robot construction	Complement the robot construction work.	90minutes
13.	Robot construction	Complement the robot construction work.	90minutes
14.	Presentations on the robots and a final competition	Prepare for the final presentation.	90minutes
Total.	-	-	1260minutes

Evaluation method and criteria

Group work will be conducted to create a robot system, and multiple competitions will be held. The results of each competition, together with the final presentation score and the completed project, will be combined for a total of 100 points, and a score of 60 or higher will be considered a passing grade.

Feedback on exams, assignments, etc.