FACULTY OF ENGINEERING

Department of Mechatronics 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/Spring
2
2
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 Level
First Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course -
Course Coordinator
Course Lecturer(s)
Assistant(s)
Course Objectives 1. To provide basic knowledge on Autonomous Robotics 2. To introduce basic analysis and design methods with a curriculum enriched by application examples.
Learning Outcomes The students who succeeded in this course;
  • 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 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
X
Major Area Courses
Supportive Courses
Media and Management Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Related Preparation
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.
2 Linear Algebra Review + Sheet 2 (Linear Algebra practice in Python) Matrix Cookbook
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.
4 Sensors CH4, Computational Principles of Mobile Robotics, Gregory Dudek and Michael Jenkin-2nd Edition, Cambridge University Press, 2010.
5 Probabilities and Bayes Review + Sheet 4 (Bayes Rule) CH2, Probabilistic Robotics, Sebastian Thrun, Wolfram Burgard and Dieter Fox, MIT Press, 2000
6 Probabilistic Motion Models + Sheet 5 (Motion Models in Python) CH5, Probabilistic Robotics, Sebastian Thrun, Wolfram Burgard and Dieter Fox, MIT Press, 2000
7 Probabilistic Sensor Models + Sheet 6 (Sensor Models in Python) CH6, Probabilistic Robotics, Sebastian Thrun, Wolfram Burgard and Dieter Fox, MIT Press, 2000
8 The Kalman Filter CH3, Probabilistic Robotics, Sebastian Thrun, Wolfram Burgard and Dieter Fox, MIT Press, 2000. --- CH4, Computational Principles of Mobile Robotics, Gregory Dudek and Michael Jenkin-2nd Edition, Cambridge University Press, 2010.
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
10 Discrete Filters CH8, Probabilistic Robotics, Sebastian Thrun, Wolfram Burgard and Dieter Fox, MIT Press, 2000
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
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
13 SLAM CH10, Probabilistic Robotics, Sebastian Thrun, Wolfram Burgard and Dieter Fox, MIT Press, 2000
14 Working on a Project
15 Working on a Project
16 Working on a Project

 

Course Notes/Textbooks
  1. Probabilistic Robotics, Sebastian Thrun, Wolfram Burgard and Dieter Fox, MIT Press, 2000
  2. Computational Principles of Mobile Robotics, Gregory Dudek and Michael Jenkin-2nd Edition, Cambridge University Press, 2010
Suggested Readings/Materials
  1. Introduction to Autonomous Mobile Robots, Roland Siegwart and Illah R. Nourbakhsh, 2004
  2. Handbook oƒ Robotics, Bruno Siciliano and Oussama Khatib
  3. Matrix Cookbook, http://matrixcookbook.com
  4. Hands-On Python: A Tutorial Introduction for Beginners, Andrew N. Harrington
  5. Introduction to Probability, Dimitri P. Bertsekas and John N. Tsitsiklis

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
10
10
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
6
50
Presentation / Jury
Project
1
20
Seminar / Workshop
Oral Exams
Midterm
Final Exam
1
20
Total

Weighting of Semester Activities on the Final Grade
17
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
16
2
32
Field Work
0
Quizzes / Studio Critiques
0
Portfolio
0
Homework / Assignments
6
7
42
Presentation / Jury
0
Project
1
40
40
Seminar / Workshop
0
Oral Exam
0
Midterms
0
Final Exam
1
20
20
    Total
182

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1

To have knowledge in Mathematics, science, physics knowledge based on mathematics; mathematics with multiple variables, differential equations, statistics, optimization and linear algebra; to be able to use theoretical and applied knowledge in complex engineering problems

X
2

To be able to identify, define, formulate, and solve complex mechatronics engineering problems; to be able to select and apply appropriate analysis and modeling methods for this purpose.

X
3

To be able to design a complex electromechanical system, process, device or product with sensor, actuator, control, hardware, and software to meet specific requirements under realistic constraints and conditions; to be able to apply modern design methods for this purpose.

X
4

To be able to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in Mechatronics Engineering applications; to be able to use information technologies effectively.

X
5

To be able to design, conduct experiments, collect data, analyze and interpret results for investigating Mechatronics Engineering problems.

X
6

To be able to work effectively in Mechatronics Engineering disciplinary and multidisciplinary teams; to be able to work individually.

X
7

To be able to communicate effectively in Turkish, both in oral and written forms; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to present effectively, to be able to give and receive clear and comprehensible instructions.

8

To have knowledge about global and social impact of engineering practices on health, environment, and safety; to have knowledge about contemporary issues as they pertain to engineering; to be aware of the legal ramifications of engineering solutions.

9

To be aware of ethical behavior, professional and ethical responsibility; information on standards used in engineering applications.

10

To have knowledge about industrial practices such as project management, risk management and change management; to have awareness of entrepreneurship and innovation; to have knowledge about sustainable development.

X
11

Using a foreign language, he collects information about Mechatronics Engineering and communicates with his colleagues. ("European Language Portfolio Global Scale", Level B1)

12

To be able to use the second foreign language at intermediate level.

13

To recognize the need for lifelong learning; to be able to access information; to be able to follow developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Mechatronics Engineering.

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

 


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