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