UAT 487 · 3(1-4-4) · Year/term 4/1

Smart Robotics and Automation

หุ่นยนต์อัจฉริยะและระบบอัตโนมัติ

Progress

Notional hours: 135 h (online/self-study 60 · in class/lab 75)

Course description

robot perception, localization, planning and decision making, motion control, robot operating frameworks, sensor and actuator interfacing, integration of robots with automation systems, and development of intelligent robotic prototypes

Thai description

การรับรู้ของหุ่นยนต์ การระบุตำแหน่ง การวางแผนและการตัดสินใจ การควบคุมการเคลื่อนที่ ระบบปฏิบัติการหุ่นยนต์ การเชื่อมต่อเซนเซอร์และตัวกระตุ้น การทำงานร่วมกันของหุ่นยนต์กับระบบอัตโนมัติ และการพัฒนาต้นแบบหุ่นยนต์อัจฉริยะ

Description source: Curriculum draft (revised 28 Sep 2026)Previous site code: DRT 496

Course learning outcomes (CLO)

CLO	Outcome	PLO
CLO1	Explain intelligent robots and modern automation	PLO4
CLO2	Develop robots with perception and decision-making	PLO4
CLO3	Integrate intelligent robots with industry	PLO5

Learning modules

1Intelligent robots
Weeks 1–3 · 27 h2 KU

Online (before class) · 12 h

Study the assigned knowledge units in advance, review media and take the module quiz

In class / field · 15 h

Lab or field practice from worksheets with a safety checklist

Learning evidence: Checked worksheets and quiz results

Open module

2Robot perception
Weeks 4–6 · 27 h2 KU

Online (before class) · 12 h

Study the assigned knowledge units in advance, review media and take the module quiz

Computer vision and the path to edge AITH
LiDAR, radar and obstacle-avoidance sensorsIn development

In class / field · 15 h

Lab or field practice from worksheets with a safety checklist

Learning evidence: Checked worksheets and quiz results

Open module

3Planning and control
Weeks 7–9 · 27 h2 KU

Online (before class) · 12 h

Study the assigned knowledge units in advance, review media and take the module quiz

Robot kinematics and motion planningIn development
Autonomous path planning and obstacle avoidanceIn development

In class / field · 15 h

Lab or field practice from worksheets with a safety checklist

Learning evidence: Checked worksheets and quiz results

Open module

4Applied ROS 2
Weeks 10–12 · 27 h1 KU

Online (before class) · 12 h

Study the assigned knowledge units in advance, review media and take the module quiz

ROS 2 and flight-stack integrationIn development

In class / field · 15 h

Lab or field practice from worksheets with a safety checklist

Learning evidence: Checked worksheets and quiz results

Open module

5Industrial automation
Weeks 13–15 · 27 h1 KU

Online (before class) · 12 h

Study the assigned knowledge units in advance, review media and take the module quiz

Industrial automation and PLCsIn development

In class / field · 15 h

Lab or field practice from worksheets with a safety checklist

Learning evidence: Checked worksheets and quiz results

Open module

Assessment (draft)

Labs and worksheets	35%
Module quizzes	10%
Midterm examination	20%
Mini-project or practical exam	35%

Knowledge domain

D02 Programming and digital technology D04 Sensors and embedded systems D06 Control, autopilot and navigation D07 Communications, networks and IoT D11 Artificial intelligence and computer vision D12 Automation, robotics and swarms

Key references

Lynch, K. M., & Park, F. C. (2017). Modern robotics: Mechanics, planning, and control. Cambridge University Press. link
Corke, P. (2023). Robotics, vision and control: Fundamental algorithms in Python. Springer. link
Groover, M. P. (2018). Automation, production systems, and computer-integrated manufacturing (5th ed.). Pearson. link