Autonomous Mobile Robotics (ME 525)

2022 Fall
Faculty of Engineering and Natural Sciences
Mechatronics(ME)
3
10
Kemalettin Erbatur erbatur@sabanciuniv.edu,
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English
Doctoral, Master
--
Formal lecture,Laboratory
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Programs\Type Required Core Elective Area Elective
Computer Science and Engineering - With Bachelor's Degree *
Computer Science and Engineering - With Master's Degree *
Computer Science and Engineering - With Thesis *
Cyber Security - With Bachelor's Degree *
Cyber Security - With Master's Degree *
Cyber Security - With Thesis *
Data Science - With Thesis *
Electronics Engineering and Computer Science - With Bachelor's Degree *
Electronics Engineering and Computer Science - With Master's Degree *
Electronics Engineering and Computer Science - With Thesis *
Electronics Engineering - With Bachelor's Degree *
Electronics Engineering - With Master's Degree *
Electronics Engineering - With Thesis *
Energy Technologies and Management-With Thesis *
Industrial Engineering - With Bachelor's Degree *
Industrial Engineering - With Master's Degree *
Industrial Engineering - With Thesis *
Leaders for Industry Biological Sciences and Bioengineering - Non Thesis *
Leaders for Industry Computer Science and Engineering - Non Thesis *
Leaders for Industry Electronics Engineering and Computer Science - Non Thesis *
Leaders for Industry Electronics Engineering - Non Thesis *
Leaders for Industry Industrial Engineering - Non Thesis *
Leaders for Industry Materials Science and Engineering - Non Thesis *
Leaders for Industry Mechatronics Engineering - Non Thesis *
Manufacturing Engineering - Non Thesis *
Manufacturing Engineering - With Bachelor's Degree *
Manufacturing Engineering - With Master's Degree *
Manufacturing Engineering - With Thesis *
Materials Science and Nano Engineering-(Pre:Materials Science and Engineering) *
Materials Science and Nano Engineering-(Pre:Materials Science and Engineering) *
Materials Science and Nano Engineering - With Thesis (Pre.Name: Materials Science and Engineering) *
Mathematics - With Bachelor's Degree *
Mathematics - With Master's Degree *
Mathematics - With Thesis *
Mechatronics Engineering - With Bachelor's Degree *
Mechatronics Engineering - With Master's Degree *
Mechatronics Engineering - With Thesis *
Molecular Biology, Genetics and Bioengineering (Prev. Name: Biological Sciences and Bioengineering) *
Molecular Biology, Genetics and Bioengineering-(Prev. Name: Biological Sciences and Bioengineering) *
Molecular Biology,Genetics and Bioengineering-With Thesis (Pre.Name:Biological Sciences and Bioeng.) *
Physics - Non Thesis *
Physics - With Bachelor's Degree *
Physics - With Master's Degree *
Physics - With Thesis *

CONTENT

The course covers fundamental problems of autonomous mobile robotics including locomotion, reception, localization, planning and navigation. In the context of locomotion, legged, wheeled, flying and swimming mobile robots will be discussed. In the reception part, various sensors that are used on mobile robots will be introduced and several sensor fusion algorithms will be presented. Localization problems will be tackled in a probabilistic framework using Markov and Kalman Filtering techniques. Simultaneous Localization and Mapping (SLAM) problem and its variations will also be introduced and discussed. Finally planning and navigation strategies will be covered.

OBJECTIVE

To teach fundamentals of autonomous mobile robotics that include locomotion, perception, localization, mapping, planning and navigation of mobile robots so that students can acquire a solid theoretical background and hands-on experience in mobile robotics.

LEARNING OUTCOMES

  • After taking this course, students should be able to: - evaluate various locomotion mechanisms including legged, wheeled and flying locomotions.
  • - analyze motion kinematics of non-holonomic wheeled mobile robots
  • - quantify mobility and maneuverability of wheeled robots
  • - design feedback controllers for motion control of the wheeled mobile robots
  • - select appropriate sensors for perception including non-visual and visual sensors
  • - implement localization algorithms using Markov and Kalman filter
  • - Implement simple SLAM algorithms using Extended Kalman Filter (EKF)
  • - Synthesize optimal paths using artificial potential functions
  • - demonstrate hands on experience with Lego Mindstorm EV3 robots

ASSESSMENT METHODS and CRITERIA

  Percentage (%)
Final 30
Midterm 20
Quiz 3
Assignment 22
Participation 5
Homework 20

RECOMENDED or REQUIRED READINGS

Textbook

Introduction to Autonomous Mobile Robots, 2nd Edition, Roland Siegwart, Illah R. Nourbakhsh and Davide Scaramuzza, MIT Press, 2011.

Readings

- Computational Principles of Mobile Robotics, Gregory Dudek, Michael Jenkin, Cambridge University Press, 2010.

- Autonomous Robots, George A. Bekey, MIT Press, 2005.