UAT 322 · 3(1-4-4) · Year/term 1/2

Artificial Intelligence and Autonomous Unmanned Aircraft Systems Integration Laboratory

ปฏิบัติการปัญญาประดิษฐ์และการประกอบรวมระบบอากาศยานไร้คนขับอัตโนมัติ

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

Course description

configuration of autopilots, navigation, and autonomous missions; integration of sensors and perception systems, real-time data processing, deployment of AI models, path planning and obstacle avoidance, simulation, ground and field testing, and performance evaluation of intelligent systems

Thai description

การตั้งค่าออโตไพลอต ระบบนำร่อง และภารกิจอัตโนมัติ การเชื่อมต่อเซนเซอร์และระบบรับรู้ การประมวลผลข้อมูลแบบเวลาจริง การติดตั้งแบบจำลองปัญญาประดิษฐ์ การวางแผนเส้นทางและหลีกเลี่ยงสิ่งกีดขวาง การจำลอง การทดสอบภาคพื้นและภาคสนาม และการประเมินสมรรถนะของระบบอัจฉริยะ

Description source: Curriculum draft (revised 28 Sep 2026)Previous site code: DRT 348

Course learning outcomes (CLO)

CLOOutcomePLO
CLO1Integrate control, navigation and sensorsPLO2
CLO2Develop autonomous missions using edge AIPLO4
CLO3Test safely in simulation and the fieldPLO3

Learning modules

1Autonomy integration
Weeks 1–3 · 27 h
Lesson 90 minquiz4 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

Intensive lab and field practice recorded in a lab notebook

Learning evidence: Lab notebook signed by the instructor

2Sensor fusion
Weeks 4–6 · 27 h
Lesson 90 minquiz3 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

Intensive lab and field practice recorded in a lab notebook

Learning evidence: Lab notebook signed by the instructor

3Obstacle avoidance and mapping
Weeks 7–9 · 27 h
Lesson 90 minquiz4 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

Intensive lab and field practice recorded in a lab notebook

Learning evidence: Lab notebook signed by the instructor

4Edge AI on drones
Weeks 10–12 · 27 h
Lesson 90 minquiz3 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

Intensive lab and field practice recorded in a lab notebook

Learning evidence: Lab notebook signed by the instructor

5Autonomous missions from SITL to field
Weeks 13–15 · 27 h
Lesson 90 minquiz6 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

Intensive lab and field practice recorded in a lab notebook

Learning evidence: Lab notebook signed by the instructor

Assessment (draft)

Laboratory performance50%
Reports and lab notebook20%
Practical examination30%

Knowledge domain

Key references

  1. PX4 Autopilot. PX4 user and developer guide. link
  2. Open Robotics. ROS 2 documentation. link
  3. Warden, P., & Situnayake, D. (2020). TinyML: Machine learning with TensorFlow Lite on Arduino and ultra-low-power microcontrollers. O'Reilly Media.