School/Faculty/Institute Graduate School
Course Code MECH 506
Course Title in English Robotics and Vision
Course Title in Turkish Robotik ve Görü
Language of Instruction EN
Type of Course Flipped Classroom
Level of Course Select
Semester Spring
Contact Hours per Week
Lecture: 3 Recitation: Lab: Other:
Estimated Student Workload 178 hours per semester
Number of Credits 7.5 ECTS
Grading Mode Standard Letter Grade
Pre-requisites None
Expected Prior Knowledge Basic knowledge of electric and electronic engineering, and prior knowledge or some experience in linear algebra, feedback control systems, differential and integral calculus, system analysis, signal processing, embedded system programming, computer vision, MATLAB, Python, and C/C++ are expected.
Co-requisites None
Registration Restrictions Only Graduate Students
Overall Educational Objective To learn the basic principles of analysis, design, and control of robotic manipulators and mobile robots
Course Description This course will introduce the fundamental concepts of control, sensing, and intelligence of robotic systems by taking advantage of machine vision. The course will briefly discuss trajectory planning, control, and programming of robotic manipulators as well as visual and navigational sensors, pose estimation, navigation, and reasoning in mobile robots. Students will gain experience by carrying out projects, simulating the behaviors of robotic arms and mobile robots as well as working on them for hardware implementations.
Course Description in Turkish Bu ders, yapay görmeden de yararlanarak robotik sistemlerin kontrol, algılama ve zeka temel kavramlarını tanıtacaktır. Ders, robotik aygıtların yörünge planlaması, kontrolü ve programlamasının yanı sıra görsel ve navigasyon sensörlerini, mobil robotlarda poz kestirimi, navigasyonu ve muhakemeyi kısaca tartışacaktır. Öğrenciler, robotik kolların ve mobil robotların davranışlarını simüle ederek ve onlar üzerine donanım dahil olmak üzere projeler geliştirerek deneyim kazanacaklardır.

Course Learning Outcomes and Competences

Upon successful completion of the course, the learner is expected to be able to:
1) analyze kinematics of robotic manipulators and apply principles of trajectory generation methods
2) design and implement a robotic project on a physical robot platform
3) understand, use, and apply localization and mapping techniques with the aid of sensors and vision
4) understand, use, and apply navigation and path planning techniques
Program Learning Outcomes/Course Learning Outcomes 1 2 3 4
1)
2)
3)
4)
5)
6)
7)
8)
9)
10)

Relation to Program Outcomes and Competences

N None S Supportive H Highly Related
     
Program Outcomes and Competences Level Assessed by
1) H Exam,HW,Participation,Project
2) H Exam,HW,Participation,Project
3) H Exam,HW,Participation,Project
4) S Exam,HW,Participation,Project
5) S Project
6) N
7) S Project
8) S Project
9) S Presentation,Project
10) N
Prepared by and Date ,
Course Coordinator YUSUF AYDIN
Semester Spring
Name of Instructor Dr. Öğr. Üyesi YUSUF AYDIN

Course Contents

Hafta Konu
1) Introduction to Manipulators
2) Spatial Descriptions and Transformations
3) Manipulator Kinematics
4) Inverse Manipulator Kinematics
5) Jacobians: Velocities and Static Forces
6) Trajectory Generation and Path Planning
7) Control of Manipulators
8) Introduction to Mobile Robots
9) Sensing, Vision, and Perception
10) Sensing, Vision, and Perception
11) Reasoning
12) Locomotion
13) Mapping
14) Path Planning and Navigation
15) Final Examination Period
16) Final Examination Period
Required/Recommended ReadingsRecommended: Introduction to Robotics: Mechanics and Control, John J. Craig, 3rd Edition, Pearson Robotics, Vision & Control. Fundamental Algorithms in MATLAB, Peter Corke, 2nd Ed. Springer Robotics: Modelling, Planning and Control, B. Siciliano, L. Sciavicco, L. Villani, G. Oriolo, Springer Computational Principles of Mobile Robotics, Gregory Dudek, Michael Jenkin, Cambridge University Press, 2010. Autonomous Robots, George A. Bekey, MIT Press, 2005 Introduction to Autonomous Mobile Robots, 2nd Edition, Roland Siegwart, Illah R. Nourbakhsh and Davide Scaramuzza, MIT Press, 2011
Teaching MethodsFlipped Classroom/Exercise/Laboratory/Active Learning
Homework and Projects
Laboratory WorkThere will be
Computer UseRequired
Other ActivitiesNone
Assessment Methods
Assessment Tools Count Weight
Devam 14 % 10
Ödev 4 % 10
Projeler 3 % 40
Ara Sınavlar 2 % 40
TOTAL % 100
Course Administration aydiny@mef.edu.tr
02123953600

ECTS Student Workload Estimation

Activity No/Weeks Hours Calculation
No/Weeks per Semester Preparing for the Activity Spent in the Activity Itself Completing the Activity Requirements
Ders Saati 14 2 3 2 98
Proje 3 12 1 1 42
Ara Sınavlar 2 15 2 2 38
Total Workload 178
Total Workload/25 7.1
ECTS 7.5