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
 112-1 
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
 工綜205 
備註
 尚需另排時段做兩小時的實作，實作地點為工綜B38室(機械手臂教室)。與何世池合授。與何世池合授
總人數上限：40人 
  
課程簡介影片
 
核心能力關聯
 本課程尚未建立核心能力關連
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, computer vision and peripherals (conveyors and grippers)
 
課程要求
 Python programming skill 
預期每週課後學習時數
 3 to 6 hours, depending on topics 
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 labsare counted 
2. 
 Midterm exam 
25% 
 Examination on TM robot operation for a specific task (team) 
3. 
 Final project proposal presentation 
10% 
 Presentation and review on final project proposal (team) 
4. 
 Final project report 
35% 
 Presentation and review on final project (team) 
5. 
 Lab exercise achievement 
20% 
 All lab topics are counted 
 
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
 團體報告取代個人報告, 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
課程進度
週次
日期
單元主題
第1週
 9/08  Introduction of collaborative robots and their industrial applications 
第2週
 9/15  Introduction of TM-5 collaborative robot and TM flow HMI 
第3週
 9/22  2D robotic computer vision 
第4週
 9/29  Mid-Autumn Festival 
第5週
 10/06  Use of RobotDK for tool center point calibration and communication protocol 
第6週
 10/13  Machine learning with TM AI+ 
第7週
 10/20  Presentation and review on final project proposal (team) 
第8週
 10/27  Midterm exam: examination on TM robot operation for a specific task (team) 
第9週
 11/03  Use of ROS for robotic manipulator control 
第10週
 11/10  Python programming: intrinsic camera calibration and camera transformation matrix 
第11週
 11/17  Python programming: autonomous object grasping with TM robots (Yolo/ CNN) 
第12週
 11/24  Python programming: task integration of conveyor and TM robot with computer vision 
第13週
 12/01  PBL on final project (I): system and architecture 
第14週
 12/08  PBL on final project (II): procedural implementation 
第15週
 12/15  PBL on final project (III): fine tuning on performance and robustness 
第16週
 12/22  Presentation and review on final project (team)