NCHU Course Outline
Course Name (中) 機器導航與探索(1600)
(Eng.) Robotic Navigation and Exploration
Offering Dept Department of Mechanical Engineering
Course Type Elective Credits 3 Teacher Bluest Lan
Department General Language Chinese Semester 2025-SPRING
Course Description 1. 本課程採遠距教學模式,由清華大學胡敏君老師主講。課程於每週一晚間 6:30 至 9:20 進行線上直播,並提供錄影檔供同學彈性觀看(詳見課程教材)

2. 本校協同教師將安排4至6次實體課程,針對課程內容與實作練習進行解惑與指導(詳見課程輔導時間)

3. 本課程依校際選課方式辦理,詳見https://oaa.nchu.edu.tw/zh-tw/news-detail/content-p.1737

4. 本課程不接受期中退選
Prerequisites
self-directed learning in the course Y
Relevance of Course Objectives and Core Learning Outcomes(%) Teaching and Assessment Methods for Course Objectives
Course Objectives Competency Indicators Ratio(%) Teaching Methods Assessment Methods
本課程模組分為三個主要的部分,分別為即時追蹤與地圖建置(SLAM)、基於機器學習之場景理解(Scene Understanding)與探索導航的動作控制(Action Control)。即時追蹤與地圖建置部分包含機率模型與相機模型等理論基礎,也包含基於深度學習之RGB-based的3DSLAM方法。場景理解的部分包含機器學習的基本概念,再帶到深度學習的技術與目前的物件偵測與語意切割技術。動作控制的部分則包含路徑規劃與導航演算法,並帶入強化學習的概念來引導行進的路徑。
Course Content and Homework/Schedule/Tests Schedule
Week Course Content
Week 1 Overview
Week 2 Motion Planning
Lab 1: Path Planning + HW1
Week 3 Kinetic Model & Path Tracking Control
Lab 2: Path Tracking Control + HW2
Week 4 Reinforcement Learning (I)
Week 5 Reinforcement Learning (II)
Lab 3: PPO + HW3
Week 6 Computer Vision Basics
Lab 4: YOLO + HW4
Week 7 SLAM Back-end (I)
Week 8 SLAM Back-end (II)
Week 9 3D SLAM (I)
Week 10 3D SLAM (II)
Week 11 Project Environment Building (I)
Week 12 Project Environment Building (II)
Week 13 3D Embodied Agent
Week 14 Paper Presentation (I)
Week 15 Paper Presentation (II)
Week 16 Project Presentation & Demo
Week 17
Week 18
Evaluation
Homework 50% (HW1 10%, HW2 15%, HW3 15%, HW4 10%), Paper Presentation 10%, Final Project 40%

本校協同教師將視修課狀況進行調整
Textbook & other References
- Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction, Second Edition, MIT Press, Cambridge, MA, 2018
- Sebastian Thrun, Wolfram Burgard, and Dieter Fox , Probabilistic Robotics,2005. (Intelligent Robotics and Autonomous Agents series)
- Kevin Murphy, Machine Learning: A Probabilistic Perspective.
- Daphne Koller and Nir Friedman, Probabilistic Graphical Models: Principles and Techniques, 1st Edition, 2009.
- Ian Goodfellow, Yoshua Bengio and Aaron Courville: Deep Learning.
Teaching Aids & Teacher's Website
https://www.youtube.com/@NTHURNE-l9v
Office Hours
待選課名單確定後,將進行調查找尋合適的時段
Sustainable Development Goals, SDGs(Link URL)
include experience courses:N
Please respect the intellectual property rights and use the materials legally.Please respect gender equality.
Update Date, year/month/day:2025/08/19 16:04:36 Printed Date, year/month/day:2026 / 2 / 19
The second-hand book website:http://www.myub.com.tw/