Course

English-Medium Instruction

AC5607701 Autonomous Mobile Robot

Course Objectives
This course introduces various aspects of mobile robot technology, from core algorithms to mechanical design. It aims to equip students with the ability to design and implement robots capable of autonomous navigation and task execution in real-world environments.

Course syllabus

The course covers three major areas: theory, practice, and presentation.

Theory
- Perception: Principles of sensors and information acquisition
- Localization and Mapping: Self-localization and environment mapping
- Path Planning: Automatic generation of safe and efficient trajectories
- Motion Control: Precise and steady execution of robot movements
Practice
- Implementation and simulation on various types of mobile robots
  (aerial, ground, surface, underwater)
- Problem- and project-based learning, focusing on teamwork and practical application
Presentation
- Academic writing and publication: Learning to write scholarly papers in English (IEEE format)
- Organizing and presenting project outcomes to develop comprehensive research and reporting skills

AC5512701 Intelligent Control Systems

This course provides a comprehensive introduction to intelligent control systems, covering algorithms, system mechanisms, and applications. It aims to equip students with the ability to design intelligent systems capable of perception, learning, reasoning, and optimization under incomplete information.

Core Competency of Intelligent Systems: Handling Uncertainty

Course Syllabus

The course is divided into three major parts: theory, practice, and presentation.

Theory
- Soft Computing (Three Key Branches of AI)
- Reasoning Intelligence (Fuzzy Logic): Simulation ➡︎ Decision Making
- Learning Intelligence (Neural Networks): Modeling ➡︎ Error Reduction
- Evolutionary Intelligence (Genetic Algorithms): Optimization ➡︎ Adaptability
Practice
- Application in mobile robots (aerial, ground, surface, underwater) and simulation environments
- Problem- and project-based learning: Emphasis on teamwork and practical skills
Presentation
- Academic writing in English (IEEE paper format)
- Organizing and presenting project outcomes to cultivate comprehensive research and reporting abilities

AC5611701 Artificial Intelligence Robot

Course Goals

This course centers on artificial intelligence (with a focus on learning intelligence: machine learning, deep learning, reinforcement learning, etc.), integrating AI methods into mobile robot perception and control. All theories and methods are explained and discussed through practical mobile robot application cases, paired with hands-on exercises. Students will use real robots for implementation and evaluation.

Course Outline: Theory - Practice - Publication

Theory
Learn core concepts of artificial intelligence
Focus on learning intelligence (machine learning, deep learning, reinforcement learning)
Apply AI methods to typical mobile robot perception and control tasks
Practice
Implement and simulate on mobile robots
Robot application platforms: aerial, ground, surface, underwater
Problem-based and project-based learning (centered on projects and practical problems, emphasizing teamwork and application skills)
Publication
Learn academic English writing (IEEE paper format)
Organize and publish project outcomes, developing comprehensive research and reporting abilities

AC5611701 Visual Servo Control

Course Goals

This course focuses on visual servo control, combining visual perception, reasoning, and control for application in servo mobile robots.

Learning content includes:

Visual perception and cognition: supporting autonomous navigation tasks
Localization, mapping, and path planning
Behavior control: target searching, obstacle avoidance, trajectory tracking, and formation keeping

Course Summary

Theory
Learn visual servo control concepts (vision-based navigation)
Understand the application of visual perception in autonomous navigation of mobile robots
Practice
Implement visual servo control on mobile robots or in simulation environments
Robot application platforms: aerial, ground, surface, underwater
Problem-based and project-based learning (focused on projects and practical issues, emphasizing teamwork and application skills)
Publication
Learn academic English writing (IEEE paper format)
Organize and publish project results, cultivating comprehensive research and reporting abilities

Teaching Features

全英語授課課程獲頒『優化 EMI 教學技巧』獎項

English-Medium Instruction (EMI)

All courses integrate English-Medium Instruction (EMI) with custom-designed English writing materials, cultivating students’ skills in writing papers that conform to IEEE conference formats and enhancing their academic English expression and publication abilities.

Course content and training are aligned with international academic and industry needs. Through final project paper competitions, outstanding works are submitted to IEEE international conferences, providing a comprehensive academic training pathway from learning to international publication.

Problem-Based and Project-Based Learning (PBL)

All courses are fully implemented using dual modes: Problem-Based Learning (PBL) and Project-Based Learning (PjBL), with structured design for classroom tasks, mid-term, and final reports.

Through this learning approach, students can effectively enhance teamwork, critical thinking, and innovation capabilities, and apply theoretical knowledge to practical projects, thereby improving overall learning outcomes.

The teaching plan “Robotics Applications of Artificial Intelligence—Problem and Project-Based Learning Model” won Second Prize.

Second Prize

Robotics Applications of Artificial Intelligence—Problem and Project-Based Learning Model

Awardees | Kueiying Chang, Minfan Lee

New Taipei Municipal Sanchong Commercial and Industrial Vocational High School

National Taiwan University of Science and Technology

Design Concept

This course is dedicated to cultivating students’ understanding of the fundamental concepts and principles of artificial intelligence, inspiring their enthusiasm for exploring emerging information technology and developing a global perspective. It also emphasizes the importance of communication, coordination, and teamwork.

Using Problem-Based Learning (PBL), instructors pose real-life scenario questions in each class, and students enhance their learning by solving these problems.

Adopting Project-Based Learning (PjBL), students work in teams to choose topics of interest or practical relevance, collaborating to complete a tangible product or solution.

By leveraging NTUST’s extensive resources, students gain early exposure to AI applications in robotics. Through team organization and discussion, students apply AI technologies to solve real-life challenges and complete self-designed project implementations, thereby fostering confidence and practical ability.

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