Course Name

Robot Motion Planning Algorithms

Summary

Lecture Objectives

• This course aims to learn robot motion planning methods and apply them to real robots. To this end, the topics covered include: the relationship between shape space and work space, collision detection of articulated robots, sampling-based path planning algorithms, and related in-depth topics.

Course Name

Robotics

Summary

Lecture Objectives

• This course aims for students to understand the kinematics and dynamics of robot manipulators and mobile robots, and learn basic theories required for robot control

Course Name

Robot Control

Summary

Lecture Objectives

• This course deals with various control theories and stability analysis theories for robotic control system design. The researches on stability proof are introduced and derived from the Lyapunov stability theory. It also introduces a method of designing a robot control system based on a real-time motion network, introduces a control method for robot system control, and a method of verifying the stability of a controller using Lyapunov stability theory and understands how to apply it.

Course Name

Motor Design and Control

Summary

Lecture Objectives

• Students learn the basic theory and practice on the design tool of the drive actuator. Shape design This course is designed to evaluate electromagnetic force through electromagnetic field analysis, thermal characteristic analysis through thermal fluid analysis, and design methods for stiffness and immunity to noise and vibration through structural analysis.

Course Name

Bio Inspired Robotics

Summary

Lecture Objectives

• Bio-inspired robotics is about making robots that are inspired by biological systems. and It is about learning concepts from nature and applying them to the design of real-world engineered systems.

Course Name

Introduction to Medical and Rehabilitation Robotics

Summary

Lecture Objectives

• The purpose of this lecture is to cultivate the knowledge necessary for grafting robotics technology into medicine and biomechanics, and based on a basic understanding of robotics technology, understanding medical and rehabilitation robot technologies, and practical examples applied to the medical field. Through this lecture, students can get the various direct/indirect experiences and they can apply and verify the knowledge acquired by carrying out the Term project based on the contents of the lecture.

Course Name

Human-Robot Interaction

Summary

Lecture Objectives

• Learn about the basics and applications of Human Robot Interaction, and learn about emotion-based human-robot interaction technology used in personal service robots.

Course Name

Introduction to Artificial Intelligence

Summary

Lecture Objectives

• Understand the definition and development process of artificial intelligence, and learn about technology, programming, and application problem solving methods for implementing artificial intelligence.

Course Name

Artificial Intelligence and Machine Learning

Summary

Lecture Objectives

• Machine learning methods applicable to robots such as pattern recognition, evolutionary computation, and reinforcement learning, neural networks. Learn about Deep Neural Network architecture design technology for object recognition (classification, localization, etc.) from images and basic mathematical concepts and algorithms required for this.

Course Name

Autonomous Driving Theory

Summary

Lecture Objectives

• Students learn related technologies of autonomous driving systems through general theoretical lectures and some practice. Focusing on the basic principles of cognition, judgment, and control of an autonomous vehicle system, concepts and cases of each stage are taught in the form of lectures and seminars. In addition, students will understand and acquire hardware, motion control, and algorithms of autonomous vehicles through platform software practice and controller software practice.

Course Name

Mathematical Optimization

Summary

Lecture Objectives

• Most engineering problems involve an optimization problem that determines the parameter vector that maximizes or minimizes it for a certain performance index. This course aims to learn mathematical optimization algorithms based on linear algebra and calculus among various optimization techniques. The topics covered include: Linear Algebra, Calculus, Linear Optimization, Nonlinear Optimization, Constrained Optimization, Stochastic Optimization.