SABANCI
UNIVERSITY
Faculty
of Eng. & Natural Sci.
ME-425 Autonomous Mobile Robotics
FALL
2020-2021
Instructor(s)
Kemalettin Erbatur
erbatur@sabanciuniv.edu
http://people.sabanciuniv.edu/erbatur FENS-1090
Course 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.
Objectives
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.
Recommended or required
reading
Textbooks:
- 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.
Course Outline
Mobile robots are becoming
increasingly important in many real-world applications. This course covers
fundamentals of mobile robotics that include robot locomotion, motion control,
perception, localization and mapping, planning and navigation. The course will
also provide hands-on experience through the lab sessions where students will
conduct several experiments on Lego Mindstorms EV3 robotic platforms.
Topics to be covered:
- Introduction and Overview of the Course
- Robot Locomotion: Legged Robots,
Wheeled Robots and Flying Robots
- Mobile Robot Kinematics: Kinematic Models and Constraints
- Motion Control: Positioning and trajectory tracking tasks for a differential
drive robot and a quadrotor type helicopter
- Perception: Sensors, Uncertainty Representation, Vision, Feature Extraction
- Localization and Mapping: Probabilistic Map-Based Localization (Markov and
Kalman filter localizations), SLAM Problem, Visual SLAM
- Planning and Navigation: Motion Planning, Navigation Strategies
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 filters
- 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
Course Policies
- Missed exams or assignments
Make-up only for official excuses
- Attendance, lateness
Attendance records will be added to the grades as bonus.
Percent |
Number |
|
Final |
30 % |
|
Midterm |
20 % |
1 |
Simulation Project |
30 % |
6 |
Homework |
20 % |
5 |