Course Details

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Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
7EE4002ROBOTIC SYSTEM DESIGN CAPSULE6+3+071021.08.2025

 
Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program ELECTRICAL-ELECTRONICS ENGINEERING
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course Developing basic understanding of robot manipulators, autonomous mobile robots, and their applications.
Providing fundamental background on robot manipulator kinematics, dynamics, and control.
Demonstrating work mechanisms of various ground and aerial autonomous mobile robots.
Providing fundamental background on mobile robot estimation, planning, and control algorithms.
Presenting the common simulation tools and operating systems for robots such as Robot Operating System (ROS).
Course Content The course covers the kinematic and dynamic analysis of robot manipulators and theoretical and practical essentials of ground and aerial autonomous mobile robots. Topics include rigid transformations, a review of linear systems and probability, manipulator kinematics, dynamics, differential kinematics and Jacobian, mobile robot motion modeling, kinematics and dynamics, navigation, perception, and execution algorithms for mobile robots, robot arm trajectory planning, Bayesian filtering methods (Kalman, EKF, particle, etc.) and their applications, basic feedback control systems for robot arms and mobile robots. Special emphasis will be given to implementation of the algorithms on robots in realistic simulation environments.
Course Methods and Techniques 14 F2F classes,
8 Homework (25%)
4 individual projects (25%)
1 group project (20%)
1 midterm exam (10%)
1 final exam (20%)
Prerequisites and co-requisities ( EE3002 ) and ( MATH103 )
Course Coordinator Associate Prof.Dr. SAMET GÜLER
Name of Lecturers Associate Prof.Dr. SAMET GÜLER
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Mark W. Spong, Seth Hutchinson, M. Vidyasagar, “Robot Modeling and Control”, Wiley, 2005,
S. Thrun, W. Burgard, & D. Fox, “Probabilistic Robotics”, MIT Press, Cambridge, MA, 2005.
R. Siegwart, I. R. Nourbakhsh & D. Scaramuzza, “Introduction to autonomous mobile robots”, MIT Press, Cambridge, MA, 2011.

Course Category
Engineering %100

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Quiz/Küçük Sınav 1 % 10
Ödev 8 % 25
Proje/Çizim 5 % 45
Final examination 1 % 20
Total
15
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Araştırma Ödevi 14 2 28
Yazılı Sınav 1 3 3
Deney 5 7 35
Grup Projesi 1 10 10
Ev Ödevi 8 6 48
Medya İncelemesi 1 3 3
Sunum için Hazırlık 1 3 3
Sunum 1 1 1
Proje 5 8 40
Araştırma 10 2 20
Simülasyon 4 5 20
Yüz Yüze Ders 14 6 84
Final Sınavı 1 3 3
Total Work Load   Number of ECTS Credits 10 298

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Demonstrate essential understanding of robot manipulator kinematics, dynamics, motion planning, and control
2 Analyze a given robot’s kinematics, path/trajectory planning, and motion cont-rol analytically and through simulations
3 Analyze several path planning, control, and estimation techniques designed for autonomous mobile robots in unknown environments
4 Formulate a real-world problem for a mobile robot in terms of control, estima-tion, and navigation sub-tasks
5 Evaluate quantitatively the performance of estimation, control, and planning algorithm designs for mobile robots
6 Design and implement integrated navigation and perception algorithms on a set of mobile robots in realistic simulation environments.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to robot manipulators; linear systems review
2 Rigid motions; probability review, coordinate transforms
3 Homogeneous transformations; mobile robot motion modeling
4 Manipulator forward kinematics I; introduction to ROS-Gazebo
5 Manipulator forward kinematics II; measurement models, sensors
6 Manipulator inverse kinematics; Bayes filters, Kalman filter
7 Manipulator differential kinematics and Jacobian; extended Kalman filter, particle filter
8 Simulation Examples
9 Manipulator trajectory generation; mobile robot localization
10 Manipulator dynamics; mapping, EKF SLAM
11 Robot motion control; mobile robot path planning
12 Manipulator sensing methods; mobile robot path planning
13 application examples
14 Mobile manipulators

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
All 2 5 2 3 4 3
C1 2 4 3
C2 2 3 4 3
C3 2 4 3
C4 2 2 4 3
C5 2 4 3
C6 5 2 3 3

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
  https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=79002&lang=en
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