一、課程說明(Course Desc<x>ription)
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<br>This course introduces students to localization, mapping, planning and control 
<br>of mobile robots and self-driving cars from the probabilistic perspective.  
<br>Topics include recursive state estimation, Gaussian filters, non-parametric 
<br>filters, robot motion and perception, localization, mapping, SLAM (simultaneous 
<br>localization and mapping), obstacle avoidance, navigation, and so on.  
<br>Laboratory assignments provide hands-on experience with servo drives, sensors, 
<br>interface circuitry, and microprocessor-ba<x>sed real-time control, Robot 
<br>Operating 
<br>System (ROS) programming. Students will build a working mobile robot system in a 
<br>group-ba<x>sed term project.
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<br> 二、指定用書(Text Books)
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<br>1. Lecture Notes. 
<br>2. Thrun, S., W. Burgard, and D. Fox. "Probability Robotics."&#160;MIT press&#160;(2005).
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<br> 三、參考書籍(References)
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<br>1. R. Siegwart, I. R. Nourbakhsh, and D. Scaramuzza, Introduction to 
<br>Autonomous Mobile Robots, 2nd Edition, MIT Press, 2011.
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<br>2. B. Siciliano, L. Sciavicco, L. Villani, and G. Oriolo, Robotics: Modeling, 
<br>Planning and Control, Springer, 2010.
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<br> 四、教學方式(Teaching Method)
<br>Lectures using chalkboard and PowerPoint presentations.
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<br> 五、教學進度(Syllabus)
<br>1. Introduction
<br>2. Actuators, Drive Systems
<br>3. Mobile Robot Basics
<br>4. Navigation
<br>5. Recursive State Estimation
<br>6. Gaussian and Nonparametric Filters
<br>7. Motion
<br>8. Perception
<br>9. Localization
<br>10. Mapping
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<br>六、成績考核(Evaluation)
<br>     Homework/Lab assignments 40%, Midterm Report/Presentation 30%, Term Project 
<br>30%
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<br>七、可連結之網頁位址
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<br>http://elearn.nthu.edu.tw