DRT 472 · 3(2-3-5) · Year/term 4/2

Autonomous Delivery Systems

ระบบจัดส่งอัตโนมัติ

Progress

Notional hours: 150 h (online/self-study 75 · in class/lab 75)

Course description

Architecture of autonomous delivery systems; route planning and task allocation for multiple aircraft; drone–ground vehicle delivery models; communication, tracking and proof of delivery; integration with logistics information systems; system performance evaluation.

Thai description

สถาปัตยกรรมระบบจัดส่งอัตโนมัติ การวางแผนเส้นทางและการจัดสรรงานให้อากาศยานหลายลำ แบบจำลองการจัดส่งร่วมกับยานพาหนะภาคพื้น การสื่อสาร ติดตาม และยืนยันการส่งมอบ การบูรณาการกับระบบข้อมูลโลจิสติกส์ และการประเมินประสิทธิภาพของระบบ

Description source: New draft for the curriculum committee

Notes for the curriculum committee

ทับซ้อนกับ DRT 471 ควรกำหนดให้ DRT 472 เป็นวิชาต่อยอดด้านอัลกอริทึมและระบบ โดยมี DRT 471 เป็นวิชาบังคับก่อน

Course learning outcomes (CLO)

CLO	Outcome	PLO
CLO1	Explain autonomous delivery system architecture	PLO2PLO4
CLO2	Develop delivery planning algorithms	PLO4
CLO3	Integrate delivery with logistics data	PLO5

Learning modules

1Autonomous delivery systems
Weeks 1–3 · 30 h1 KU

Online (before class) · 15 h

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

Drone delivery systemsIn 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

2Route planning
Weeks 4–6 · 30 h2 KU

Online (before class) · 15 h

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

Delivery routing and task allocationIn 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

3Communication and tracking
Weeks 7–9 · 30 h2 KU

Online (before class) · 15 h

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

Cloud and IoT data servicesIn development
Deep dive: MQTT and Eclipse Mosquitto for drone and IoT systemsTH

In class / field · 15 h

Lab or field practice from worksheets with a safety checklist

Learning evidence: Checked worksheets and quiz results

Open module

4Drop points and ground safety
Weeks 10–12 · 30 h2 KU

Online (before class) · 15 h

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

Suspended loads, winches and item deliveryTH
Safety management systems (SMS)In 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

5Logistics integration
Weeks 13–15 · 30 h1 KU

Online (before class) · 15 h

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

IoT and smart warehouse systemsIn 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

D01 Mathematics, physics and statistics D02 Programming and digital technology D05 Aircraft, structures and design D06 Control, autopilot and navigation D07 Communications, networks and IoT D09 Law, safety and risk D12 Automation, robotics and swarms D15 Delivery, indoor operations and warehousing

Key references

Macrina, G., Di Puglia Pugliese, L., Guerriero, F., & Laporte, G. (2020). Drone-aided routing: A literature review. Transportation Research Part C, 120, 102762.
Otto, A., Agatz, N., Campbell, J., Golden, B., & Pesch, E. (2018). Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey. Networks, 72(4), 411–458.