Department of Mechanical Engineering

MCE 412 | Course Introduction and Application Information

Course Name

Autonomous Robotics

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
MCE 412	FALL	3	0	3	6

Prerequisites	MATH 250 (To get a grade of at least FD) or EEE 281 (To get a grade of at least FD)
Course Language	English
Course Type	ELECTIVE_COURSE
Course Level	First Cycle
Mode of Delivery	Face-to-face
Teaching Methods and Techniques of the Course	Problem Solving Q&A Simulation Application: Experiment / Laboratory / Workshop Lecture / Presentation
National Occupational Classification Code	-
Course Coordinator	Doç. Dr. Pınar Oğuz Ekim
Course Lecturer(s)	Doç. Dr. Pınar Oğuz Ekim
Assistant(s)	Araş. Gör. Alperen Keser

Course Objectives

The objectives of this course are to provide basic information about autonomous robots and to introduce basic analysis and design methods with a curriculum enriched with application examples.

Learning Outcomes

The students who succeeded in this course;

Name	Description	PC Sub	* Contribution Level
Name	Description	PC Sub	1	2	3	4	5
LO1	Explain localization problem of a robot					X
LO2	Describe mapping problem of a robot			X
LO3	Define path planning for a robot			X
LO4	Analyse sensors on an autonomous robot				X
LO5	Design filtering algorithms for autonomous robot application					X

Course Description

Introduction to autonomous robotics, motion models of a robot, measurement models of different sensor types, filtering techniques, simultaneous localization and mapping method.

Related Sustainable Development Goals

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

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week	Subjects	Required Materials	Learning Outcome
1	Introduction + Sheet 1 (Python Setup)	Chapter 1 and Chapter 2, Computational Principles of Mobile Robotics, Gregory Dudek and Michael Jenkin-2nd Edition, Cambridge University Press, 2010.	LO1
2	Linear Algebra Review + Sheet 2 (Linear Algebra practice in Python)	Matrix Cookbook	LO2
3	Wheeled Locomotion + Sheet 3 (Locomotion-Differential Drive Kinematics in Python)	Chapter 3, Computational Principles of Mobile Robotics, Gregory Dudek and Michael Jenkin-2nd Edition, Cambridge University Press, 2010.	LO3
4	Sensors	Chapter 4, Computational Principles of Mobile Robotics, Gregory Dudek and Michael Jenkin-2nd Edition, Cambridge University Press, 2010.	LO4
5	Probabilities and Bayes Review + Sheet 4 (Bayes Rule)	Chapter 2, Probabilistic Robotics, Sebastian Thrun, Wolfram Burgard and Dieter Fox, MIT Press, 2000	LO5
6	Probabilistic Motion Models + Sheet 5 (Motion Models in Python)	Chapter 5, Probabilistic Robotics, Sebastian Thrun, Wolfram Burgard and Dieter Fox, MIT Press, 2000	LO5
7	Probabilistic Sensor Models + Sheet 6 (Sensor Models in Python)	Chapter 6, Probabilistic Robotics, Sebastian Thrun, Wolfram Burgard and Dieter Fox, MIT Press, 2000	LO4
8	The Kalman Filter	Chapter 3, Probabilistic Robotics, Sebastian Thrun, Wolfram Burgard and Dieter Fox, MIT Press, 2000. --- Chapter 4, Computational Principles of Mobile Robotics, Gregory Dudek and Michael Jenkin-2nd Edition, Cambridge University Press, 2010.	LO5
9	The Extended Kalman Filter + Sheet 8 (Extended Kalman Filter Implementation in Python)	Chapter 7, Probabilistic Robotics, Sebastian Thrun, Wolfram Burgard and Dieter Fox, MIT Press, 2000	LO5
10	Discrete Filters	Chapter 8, Probabilistic Robotics, Sebastian Thrun, Wolfram Burgard and Dieter Fox, MIT Press, 2000	LO5
11	The Particle Filter + Sheet 7 (Discrete Filter, Particle Filter Implementation in Python)	Chapter 8, Probabilistic Robotics, Sebastian Thrun, Wolfram Burgard and Dieter Fox, MIT Press, 2000	LO5
12	Mapping with Known Poses + Sheet 9 (Mapping with Known Poses in Python)	Chapter 9, Probabilistic Robotics, Sebastian Thrun, Wolfram Burgard and Dieter Fox, MIT Press, 2000	LO5
13	SLAM	Chapter 10, Probabilistic Robotics, Sebastian Thrun, Wolfram Burgard and Dieter Fox, MIT Press, 2000	LO2
14	Working on a Project		LO1
15	Working on a Project		LO1
16	Final Exam	-	-

Course Notes/Textbooks	Probabilistic Robotics Sebastian Thrun Wolfram Burgard and Dieter Fox MIT Press 2000 Computational Principles of Mobile Robotics Gregory Dudek and Michael Jenkin 2nd Edition Cambridge University Press 2010.
Suggested Readings/Materials	Introduction to Autonomous Mobile Robots Roland Siegwart and Illah R. Nourbaksh 2004 Handbook of Robotics Bruno Sciilano and Oussama Khatib Matrix Cookbook Hands -on Python: A Tutorial Introduction for Beginners Andrew N. Harrington Introduction to Probability Dimitri P. Bertsekas and John N. Tsisiklis.

EVALUATION SYSTEM

Semester Activities	Number	Weighting	LO1	LO2	LO3	LO4	LO5
Homework / Assignments	1	50	X	X	X	X	X
Project	1	25	X	X	X	X	X
Final Exam	1	25	X			X	X
Total	3	100

ECTS / WORKLOAD TABLE

Semester Activities	Number	Duration (Hours)	Workload
Participation	-	-	-
Theoretical Course Hours	16	3	48
Laboratory / Application Hours	-	-	-
Study Hours Out of Class	16	2	32
Field Work	-	-	-
Quizzes / Studio Critiques	-	-	-
Portfolio	-	-	-
Homework / Assignments	6	7	42
Presentation / Jury	-	-	-
Project	1	38	38
Seminar / Workshop	-	-	-
Oral Exams	-	-	-
Midterms	-	-	-
Final Exam	1	20	20
		Total	180

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
			1	2	3	4	5
No program competency data found.

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

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