Course Syllabus

Course Information：

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2026	366205	Robotics Lab	1	3.0	3	★	Chang,Wen-Chung	4EE4A 4EE4B 4EE4C	17	0	電機大四合開,預選限36人

Course Plan：

Instructor	Chang,Wen-Chung　Office Hours
Email	wchang@ntut.edu.tw
Last Updated	2026-07-06 10:53:05
Course Objective	Robotics Lab (RLab) is offered with all experimental in-class projects, same as its previous version “Robotics and Automation Applications (RAA)”. Compared with Robotics course, this Robotics Lab (RLab) course is https://aps.ntut.edu.tw/course/tw/Select.jsp?format=-1&code=366205 offered for undergraduate students with programming experience, but no particular prerequisites, such as Robotics, are required. RLab focuses on projects in the areas of optical inspection, mobile robots, manipulators, visual servoing, and automation expanding the application of AI approaches. 本課程為全實驗課程，等同於原先「機器人與自動化應用」之新版課程。相較於「機器人學」基礎分析課程，機器人學實習(RLab)屬應用課程、適合具備基礎程式能力的大學生修習，並無機器人學等先修課程要求。課程專題聚焦於光學檢測、移動式機器人、機械手臂、視覺伺服與自動化並擴展運用人工智慧方法。 The goals of the course can be summarized as below: [Projects will be arranged based on available equipment] 1.影像處理實驗：灰階分類，膨脹與侵蝕處理Image Processing: Gray-level Classification, Dilation, and Erosion Operations, 2. 影像處理實驗：斷開與閉合處理Image Processing: Opening and Closing Operations, 3.光學檢測實驗Optical Inspection, 4. 移動式機器人之眼在手與眼看手視覺控制實驗 Mobile Robot Control with Eye-in-Hand and Eye-to-Hand Vision, 5. Mobile Robot Mapping移動式機器人建圖, 6. Mobile Robot Navigation 移動式機器人導航, 7. 移動型機器人之 AI人體追蹤與跟隨實驗AI-Based Human Tracking and Following for Mobile Robot, 8. 機器臂模擬&實驗教學Tutorials on Simulation & Experiments for Manipulator, 9. 機器臂控制實驗:骨牌拾取與放置Manipulator Control Experiment: Domino Picking and Placing, 10. 機器臂控制實驗: 以骨牌拾取與放置完成河內塔任務 Manipulator Control Experiment: Tower of Hanoi by Domino Picking and Placing, and 11. 機器臂 AI 控制實驗: 視覺語言模型輔助物體拾取與放置Manipulator AI Control Experiment: Vision-Language-Model-Assisted Object Picking and Placing. Course materials will be available via iStudy. Homework needs to be submitted before each in-class experiment. All experiments must be completed and evaluated in class under supervision of TA. The final project is scheduled for last few weeks of the term. 本課程於每週課前公布講義於網路學園，作業必須在實驗進行前繳交，課堂期間學生進行團隊實驗並由助教驗收成果。使用設備時必須正確操作並注意安全，遇到問題務必通知助教尋求協助。本課程無法於課堂外時間進行，請於課堂結束前完成實驗以利考核。本課程之期末專題實作與展示規劃於期末進行。
Course Schedule	Tentative schedule 暫定課程進度 1 09/09 課程介紹Course introduction 2 09/16 影像處理實驗: 灰階分類、膨脹、侵蝕、斷開與閉合 Image processing: gray-level classification, dilation, erosion, opening, and closing 3 09/23 光學檢測實驗Optical inspection 4 09/30 移動式機器人之眼在手與眼看手視覺控制實驗 Mobile robot control with eye-in-hand and eye-to-hand vision 5 10/07 移動式機器人建圖Mobile robot mapping 6 10/14 移動式機器人導航Mobile robot navigation 7 10/21 移動型機器人之 AI人體追蹤與跟隨實驗 AI-based human tracking and following for mobile robot 8 10/28 機器臂模擬&實驗教學 Tutorials on simulation & experiments for manipulator 9 11/04 機器臂控制實驗:骨牌拾取與放置 Manipulator control experiment: domino picking and placing 10 11/11 機器臂控制實驗: 以骨牌拾取與放置完成河內塔任務 Manipulator control experiment: tower of Hanoi by domino picking and placing 11 11/18 機器臂 AI 控制實驗: 視覺語言模型輔助物體拾取與放置 Manipulator AI control experiment: vision-language-model-assisted object picking and Placing 12 11/25 期末專題實驗&提案簡報(1/4) Final project experiments & proposal presentations (1/4) 13 12/02 期末專題實驗&提案簡報(2/4) Final project experiments & proposal presentations (2/4) 14 12/09 期末專題實驗&提案簡報(3/4) Final project experiments & proposal presentations (3/4) 15 12/16 期末專題實驗&提案簡報(4/4) Final project experiments & proposal presentations (4/4) 16 12/23 期末專題演式及展示 Final project presentations and demonstrations 17 12/30 彈性學習自主研讀 & 期末專題報告繳交 Flexible learning and self-study & Final project reports due 18 01/06 彈性學習報告繳交 Flexible learning reports due PS: The contents and order of in-class projects and presentations will be adjusted according to actual needs上述專題與報告內容及順序將因應實際狀況調整
Evaluation and grading policy	The final grade will be a weighted average of attendance and homework 10%, in-class projects 50%, final project (including proposal presentations, project presentations, demonstrations, and reports) 30%, flexible learning reports 10%, and in-class performance credits evaluated by Professor. 學期成績權重比例：出席及作業10%，課程專題50%，期末專題(含提案簡報、演示、展示及書面報告) 30 %，彈性學習報告10%及授課教授評估課堂表現之額外調整。
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Introduction to Robotics: Mechanics and Control 4th Edition, John J. Craig, Pearson Education.代理商：東華書局/新月圖書，TEL：(02)2311-4027 FAX：(02)2311-6615. Robotics, Vision and Control: Fundamental Algorithms in MATLAB, Springer by Peter Corke Publication Year: 2013 | ISBN 3642201431 | 1st Edition
The access to curricular consultation	Prof. Wen-Chung Chang (02)27712171 Ext. 2125, wchang@ntut.edu.tw Office hours: Check official EE website TAs: Shao-Jie Kuo 郭少杰 t114318112@ntut.edu.tw、TBA (02)27712171 Ext. 2130
Expanding teaching and resources	● 混成學習 (Blended Learning) 課程結合實體授課與線上學習（同步視訊／非同步活動）。 Combines face-to-face instruction with online learning (including synchronous video sessions or asynchronous activities). ● 參與式創新教學 (Participatory & Innovative Teaching) 強調學生參與與產出，導入翻轉、PBL/專題、設計思考或遊戲化等策略。 Emphasizes student engagement and outcomes by adopting strategies such as Flipped Classroom, PBL (Problem/Project- Based Learning), Design Thinking, or Gamification. ● 數位科技融入 (Digital Technology Integration) 運用生成式AI、VR/AR、IoT、IRS等新興科技輔助教學與評量。 Utilizes emerging technologies such as Generative AI, VR/AR, IoT, and IRS (Interactive Response Systems) to facilitate teaching and assessment.
The course corresponds to the SDGs	SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
Does the course incorporate AI	● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）
Note	Response to government epidemic alert, course lecture and presentations will be offered online via Microsoft Teams at class hours and recorded lectures are available in iStudy. Please refer to Taipei Tech's iStudy for details about this course. 疫情警戒期間將透過Microsoft Teams線上授課及報告，授課錄影請登入北科i學園+，細節請參照北科i學園+課程公告。