M.SC. in Electrical and Electronics Engineering (With Thesis)

EEE 516 | Course Introduction and Application Information

Course Name

Autonomous Robotics and Mobile Sensing

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
EEE 516	Fall/Spring	3	0	3	7.5

Prerequisites	None
Course Language	English
Course Type	Elective
Course Level	Second / Third Cycle
Mode of Delivery	-
Teaching Methods and Techniques of the Course	-
National Occupation Classification	-
Course Coordinator	Dr. Öğr. Üyesi Pınar Oğuz Ekim
Course Lecturer(s)	Dr. Öğr. Üyesi Pınar Oğuz Ekim
Assistant(s)	-

Course Objectives

The objectives of this course are to provide basic knowledge on Autonomous Robotics and to introduce basic analysis and design methods with a curriculum enriched by application examples.

Learning Outcomes

#	Content	PC Sub	* Contribution Level
#	Content	PC Sub	1	2	3	4	5
1	Explain localization problem of a robot
2	Describe mapping problem of a robot
3	Define path planning for a robot
4	Analyse sensors on an autonomous robot
5	Design filtering algorithms for autonomous robot applications

Course Description

This course covers introduction to autonomous robotics, motion models of a robot, measurement models of different sensor types, filtering techniques, simultaneous localization and mapping method

Course Category	Core Courses
	Major Area Courses	X
	Supportive Courses
	Media and Management Skills Courses
	Transferable Skill Courses

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week	Subjects	Related Preparation	Learning Outcome
1	Introduction + Sheet 1 (Python Setup)	CH1 and CH2, Computational Principles of Mobile Robotics, Gregory Dudek and Michael Jenkin-2nd Edition, Cambridge University Press, 2010, ISBN: 9780521692120
2	Linear Algebra Review + Sheet 2 (Linear Algebra practice in Python)	Linear Algebra and its Applications, Gill Strang, Cengage Learning ISBN-10: 0534422004
3	Wheeled Locomotion + Sheet 3 (Locomotion-Differential Drive Kinematics in Python)	CH3, Computational Principles of Mobile Robotics, Gregory Dudek and Michael Jenkin-2nd Edition, Cambridge University Press, 2010, ISBN: 9780521692120
4	Sensors	CH4, Computational Principles of Mobile Robotics, Gregory Dudek and Michael Jenkin-2nd Edition, Cambridge University Press, 2010, ISBN: 9780521692120
5	Probabilities and Bayes Review + Sheet 4 (Bayes Rule)	CH2, Probabilistic Robotics, Sebastian Thrun, Wolfram Burgard and Dieter Fox, MIT Press, 2000, ISBN: 9780262201629
6	Probabilistic Motion Models + Sheet 5 (Motion Models in Python)	CH5, Probabilistic Robotics, Sebastian Thrun, Wolfram Burgard and Dieter Fox, MIT Press, 2000, ISBN: 9780262201629
7	Probabilistic Sensor Models + Sheet 6 (Sensor Models in Python)	CH6, Probabilistic Robotics, Sebastian Thrun, Wolfram Burgard and Dieter Fox, MIT Press, 2000, ISBN: 9780262201629
8	The Kalman Filter	CH3, Probabilistic Robotics, Sebastian Thrun, Wolfram Burgard and Dieter Fox, MIT Press, 2000, ISBN: 9780262201629 CH4, Computational Principles of Mobile Robotics, Gregory Dudek and Michael Jenkin-2nd Edition, Cambridge University Press, 2010, ISBN: 9780521692120
9	The Extended Kalman Filter + Sheet 8 (Extended Kalman Filter Implementation in Python)	CH7, Probabilistic Robotics, Sebastian Thrun, Wolfram Burgard and Dieter Fox, MIT Press, 2000, ISBN: 9780262201629
10	Discrete Filters	CH8, Probabilistic Robotics, Sebastian Thrun, Wolfram Burgard and Dieter Fox, MIT Press, 2000, ISBN: 9780262201629
11	The Particle Filter + Sheet 7 (Discrete Filter, Particle Filter Implementation in Python)	CH8, Probabilistic Robotics, Sebastian Thrun, Wolfram Burgard and Dieter Fox, MIT Press, 2000, ISBN: 9780262201629
12	Mapping with Known Poses + Sheet 9 (Mapping with Known Poses in Python)	CH9, Probabilistic Robotics, Sebastian Thrun, Wolfram Burgard and Dieter Fox, MIT Press, 2000, ISBN: 9780262201629
13	SLAM	CH10, Probabilistic Robotics, Sebastian Thrun, Wolfram Burgard and Dieter Fox, MIT Press, 2000, ISBN: 9780262201629
14	Working on a Project
15	Semester Review
16	Final Exam

Course Notes/Textbooks

Probabilistic Robotics, Sebastian Thrun, Wolfram Burgard and Dieter Fox, MIT Press, 2000, ISBN: 9780262201629
Computational Principles of Mobile Robotics, Gregory Dudek and Michael Jenkin-2nd Edition, Cambridge University Press, 2010, ISBN: 9780521692120

Suggested Readings/Materials

Introduction to Autonomous Mobile Robots, Roland Siegwart and Illah R. Nourbakhsh, 2004, ISBN: 9780262195027
Handbook oƒ Robotics, Bruno Siciliano and Oussama Khatib, ISBN 978-3-319-32552-1
Linear Algebra and its Applications, Gill Strang, Cengage Learning, ISBN-10: 0534422004
Hands-On Python: A Tutorial Introduction for Beginners, Andrew N. Harrington, http://anh.cs.luc.edu/handsonPythonTutorial/
Introduction to Probability, Dimitri P. Bertsekas and John N. Tsitsiklis, ISBN-13: 978-1886529403

EVALUATION SYSTEM

Semester Activities	Number	Weighting	LO 1	LO 2	LO 3	LO 4	LO 5
Participation	1	10
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments	1	50
Presentation / Jury
Project	1	20
Seminar / Workshop
Oral Exams
Midterm
Final Exam	1	20
Total

Weighting of Semester Activities on the Final Grade	3	80
Weighting of End-of-Semester Activities on the Final Grade	1	20
Total

ECTS / WORKLOAD TABLE

Semester Activities	Number	Duration (Hours)	Workload
Theoretical Course Hours (Including exam week: 16 x total hours)	16	3	48
Laboratory / Application Hours (Including exam week: '.16.' x total hours)	16		0
Study Hours Out of Class	14	3	42
Field Work			0
Quizzes / Studio Critiques			0
Portfolio			0
Homework / Assignments	6	8	48
Presentation / Jury			0
Project	1	45	45
Seminar / Workshop			0
Oral Exam			0
Midterms			0
Final Exam	1	42	42
		Total	225

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
#	PC Sub	Program Competencies/Outcomes	1	2	3	4	5
1	Accesses information in breadth and depth by conducting scientific research in Electrical and Electronics Engineering; evaluates, interprets and applies information		-	-	-	X	-
2	Is well-informed about contemporary techniques and methods used in Electrical and Electronics Engineering and their limitations		-	-	X	-	-
3	Uses scientific methods to complete and apply information from uncertain, limited or incomplete data; can combine and use information from different disciplines		-	-	X	-	-
4	Is informed about new and upcoming applications in the field and learns them whenever necessary.		-	-	X	-	-
5	Defines and formulates problems related to Electrical and Electronics Engineering, develops methods to solve them and uses progressive methods in solutions.		-	-	X	-	-
6	Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs.		-	X	-	-	-
7	Designs and implements studies based on theory, experiments and modeling; analyses and resolves the complex problems that arise in this process.		-	-	-	-	-
8	Can work effectively in interdisciplinary teams as well as teams of the same discipline, can lead such teams and can develop approaches for resolving complex situations; can work independently and takes responsibility.		-	-	-	-	-
9	Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale.		-	-	-	-	-
10	Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.		-	-	-	-	-
11	Is knowledgeable about the social, environmental, health, security and law implications of Electrical and Electronics Engineering applications, knows their project management and business applications, and is aware of their limitations in Electrical and Electronics Engineering applications.		-	-	-	-	-
12	Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity. Adheres to the principles of research and publication ethics.		-	-	-	-	-