課程資訊
課程名稱
智慧機器人應用與實作
Application and Practical of Intelligent Robot 
開課學期
113-1 
授課對象
工學院  機械工程學系  
授課教師
郭重顯 
課號
ME5065 
課程識別碼
522 U6420 
班次
 
學分
3.0 
全/半年
半年 
必/選修
選修 
上課時間
星期五2,3,4(9:10~12:10) 
上課地點
機械116 
備註
實作地點為機械B119室(機器人實作實驗室)。與何世池合授
總人數上限:30人 
 
課程簡介影片
 
核心能力關聯
核心能力與課程規劃關聯圖
課程大綱
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課程概述

The course entitled “Application and Practical of Intelligent Robot” is a project-based learning (PBL) course, and it aims at cultivating the students with the capability of utilizing the collaborative robot (TM-5) for industry smart automation applications. The composition of this course consists of 2 hours lecture talk and 2 hours hands-on practice. The lecture topics are arranged as follows:
1. Introduction of collaborative robots and their industrial applications
2. Introduction of TM-5 collaborative robot and TM flow HMI
3. 2D robotic computer vision
4. Use of RobotDK for tool center point calibration and communication protocol
5. Machine learning with TM AI+
6. Use of ROS for robotic manipulator control
7. Python programming: intrinsic camera calibration and camera transformation matrix
8. Python programming: autonomous object grasping with TM robots (Yolo/ CNN)
9. Python programming: task integration of conveyor and TM robot with computer vision
10. PBL on final project (I): system and architecture
11. PBL on final project (II): procedural implementation
12. PBL on final project (III): fine tuning on performance and robustness 

課程目標
The students are capable of learning:
1. Popular collaborative robots (TM-5)
2. Operative software and tools (TMflow HMI and RobotDK)
3. 2D computer vision for image recognition and object grasping
4. Entry level AI programming and tools (TM AI+ and Python)
5. Robot operating system (ROS)
6. Mechatronic integration for the collaborative robot, python-programming computer vision and peripherals (conveyors and grippers) 
課程要求
Python programming skill 
預期每週課前或/與課後學習時數
3 to 6 hours, depending on topics and individuals 
Office Hours
每週一 09:00~12:50 
指定閱讀
Handouts  
參考書目
Conference and journal papers; open source codes and documents 
評量方式
(僅供參考)
 
No.
項目
百分比
說明
1. 
Class attendance and participation 
10% 
Lectures and labs are counted 
2. 
Final project proposal presentation 
10% 
Presentation and review on final project proposal (team) 
3. 
Midterm exam 
25% 
Examination on TM robot operation for a specific task (team) 
4. 
Lab exercise achievement 
20% 
All lab topics are counted 
5. 
Final project report 
35% 
Presentation and review on final project outcome and achievement (team) 
  1. 本校尚無訂定 A+ 比例上限。
  2. 本校採用等第制評定成績,學生成績評量辦法中的百分制分數區間與單科成績對照表僅供參考,授課教師可依等第定義調整分數區間。詳見學習評量專區 (連結)。
 
針對學生困難提供學生調整方式
 
上課形式
以錄影輔助, 提供學生彈性出席課程方式
作業繳交方式
延長作業繳交期限, 學生與授課老師協議改以其他形式呈現
考試形式
延後期末考試日期(時間)
其他
由師生雙方議定
課程進度
週次
日期
單元主題
第1週
09/06  Introduction of collaborative robots and their industrial applications 
第2週
9/13  Introduction of TM-5 collaborative robot and TM flow HMI 
第3週
9/20  2D robotic computer vision (Eye-in-hand) 
第4週
9/27  Use of RobotDK for tool center point calibration and communication protocol 
第5週
10/4  Machine learning with TM AI+ (I) 
第6週
10/11  Machine learning with TM AI+ (II) 
第7週
10/18  Presentation and evaluation on final project proposal (team)  
第8週
10/25  Midterm exam: examination on TM robot operation for a specific task (team) 
第9週
11/1  Use of ROS for robotic manipulator control
 
第10週
11/8  Python programming: intrinsic camera calibration and camera transformation matrix 
第11週
11/15  Python programming: autonomous object grasping with TM robots (Yolo/ CNN) 
第12週
11/22  Python programming: task integration of conveyor and TM robot with computer vision 
第13週
11/29  PBL on final project (I): system and architecture 
第14週
12/6  PBL on final project (II): procedural implementation 
第15週
12/13  PBL on final project (III): fine tuning on performance and robustness 
第16週
12/20  Presentation and evaluation on final project achievement (team)