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Code ME 425
Term 201702
Title Autonomous Mobile Robotics
Faculty Faculty of Engineering and Natural Sciences
Subject Mechatronics(ME)
SU Credit 3
ECTS Credit 6.00 / 6.00 ECTS (for students admitted in the 2013-14 Academic Year or following years)
Instructor(s) Mustafa Unel -munel@sabanciuniv.edu,
Language of Instruction English
Level of Course Undergraduate
Type of Course Click here to view.
Prerequisites
(only for SU students)
ENS211 ENS206
Mode of Delivery Formal lecture,Laboratory
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 Outcome

After taking this course, students should be able to:

- Evaluate advantages/disadvantages of various locomotion mechanisms including legged, wheeled, flying and swimming 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 Kalman and Extended Kalman filters
- Implement simple SLAM algorithms using Extended Kalman filter (EKF)
- Synthesize optimal paths using artificial potential functions
- Hands on experience with Lego Mindstorm EV3 robots

Programme Outcomes
 
Common Outcomes For All Programs
1 Understand the world, their country, their society, as well as themselves and have awareness of ethical problems, social rights, values and responsibility to the self and to others. 1
2 Understand different disciplines from natural and social sciences to mathematics and art, and develop interdisciplinary approaches in thinking and practice. 1
3 Think critically, follow innovations and developments in science and technology, demonstrate personal and organizational entrepreneurship and engage in life-long learning in various subjects. 1
4 Communicate effectively in Turkish and English by oral, written, graphical and technological means. 2
5 Take individual and team responsibility, function effectively and respectively as an individual and a member or a leader of a team; and have the skills to work effectively in multi-disciplinary teams. 2
1 Possess sufficient knowledge of mathematics, science and program-specific engineering topics; use theoretical and applied knowledge of these areas in complex engineering problems.
2 Identify, define, formulate and solve complex engineering problems; choose and apply suitable analysis and modeling methods for this purpose.
3 Develop, choose and use modern techniques and tools that are needed for analysis and solution of complex problems faced in engineering applications; possess knowledge of standards used in engineering applications; use information technologies effectively.
4 Ability to design a complex system, process, instrument or a product under realistic constraints and conditions, with the goal of fulfilling specified needs; apply modern design techniques for this purpose.
5 Design and conduct experiments, collect data, analyze and interpret the results to investigate complex engineering problems or program-specific research areas.
6 Knowledge of business practices such as project management, risk management and change management; awareness on innovation; knowledge of sustainable development.
7 Knowledge of impact of engineering solutions in a global, economic, environmental, health and societal context; knowledge of contemporary issues; awareness on legal outcomes of engineering solutions; understanding of professional and ethical responsibility.
1 Familiarity with concepts in statistics and optimization, knowledge in basic differential and integral calculus, linear algebra, differential equations, complex variables, multi-variable calculus, as well as physics and computer science, and ability to use this knowledge in modeling, design and analysis of complex dynamical systems containing hardware and software components.
2 Ability to work in design, implementation and integration of engineering applications, such as electronic, mechanical, electromechanical, control and computer systems that contain software and hardware components, including sensors, actuators and controllers.
1 Design, implement, test, and evaluate a computer system, component, or algorithm to meet desired needs and to solve a computational problem.
2 Demonstrate knowledge of discrete mathematics and data structures.
3 Demonstrate knowledge of probability and statistics, including applications appropriate to computer science and engineering.
1 Applying fundamental and advanced knowledge of natural sciences as well as engineering principles to develop and design new materials and establish the relation between internal structure and physical properties using experimental, computational and theoretical tools.
2 Merging the existing knowledge on physical properties, design limits and fabrication methods in materials selection for a particular application or to resolve material performance related problems.
3 Predicting and understanding the behavior of a material under use in a specific environment knowing the internal structure or vice versa.
Assessment Methods and Criteria
  Percentage (%)
Final 30
Midterm 30
Group Project 30
Homework 10
Recommended or Required Reading
Readings

- Introduction to Autonomous Mobile Robots, Roland Siegwart, Illah R. Nourbakhsh, MIT Press, 2004.

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

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