Robotics and Autonomy MS
Major: Robotics and Autonomy
Degree Awarded: Master of Science (MS)
Calendar Type: Quarter
Minimum Required Credits: 45.0
Co-op Option: Available for full-time, on-campus master's-level students
Classification of Instructional Programs (CIP) code: 14.4201
Standard Occupational Classification (SOC) code: 11-9041
About the Program
The graduate program in Robotics and Autonomy will educate professionals who are prepared to lead and conduct research, development, and design in robotic systems and technologies. This MS degree is built upon four foundational concepts in robotics: perception, cognition, control, and action. Roughly, these four capabilities comprise: 1) obtaining data from the robot’s surroundings (perception); 2) reasoning about how that data yields information about the robot’s environment (cognition); 3) mapping environmental information to a decision about how to react to the environment (control); and 4) translating that reaction decision into movement and an interaction with the physical environment (action).
The program is an interdepartmental program in Drexel’s College of Engineering that educates and trains students in the theory, integration, and practical application of the core engineering and computer science disciplines that comprise robotics and autonomy. To be admitted, students must have a bachelor’s degree in a STEM field or demonstrate that they have acquired sufficient experience in a technical field to be able to satisfactorily complete engineering studies at the graduate level.
Students are also encouraged to engage in thesis research. The combined thesis and research cannot exceed 9.0 credits. The MS program is organized so that a student may complete the degree requirements in less than 2 years of full-time study or 2-3 years of part-time study.
Students within the Master of Science in Robotics and Autonomy are eligible to take part in the Graduate Co-op Program, which combines classroom coursework with a 6-month, full-time work experience. For more information, visit the Steinbright Career Development Center's website.
Additional Information
For more information visit the MS in Robotics and Autonomy program and the Electrical and Computer Engineering Department website.
Admission Requirements
Applicants must satisfy general requirements for graduate admission including a minimum 3.0 GPA (on a 4.0 scale) for the last two years of undergraduate studies, as well as for any subsequent graduate work, and hold a bachelor's degree in an engineering discipline from an accredited college or university. A degree in science (physics, mathematics, computer science, etc.) is also acceptable. Applicants with degrees in sciences may be required to take a number of undergraduate engineering courses. An undergraduate degree earned abroad must be deemed equivalent to a US bachelor's.
Full-time applicants must take the GRE exam. Students who do not hold a degree from a US institution must take the TOEFL or IELTS exam within two years of application submission.
Additional Information
For more information, visit the Department of Electrical and Computer Engineering webpage.
Degree Requirements
Foundation Courses | 6.0 | |
Choose 2 courses in mathematics and/or signal processing | ||
Mathematics | ||
Probability & Random Variables | ||
Linear Algebra & Matrix Analysis | ||
Applied Probability and Statistics I | ||
Ordinary Differential Equations I | ||
Complex Variables I | ||
Applied Engr Analy Methods I | ||
Applied Engr Analy Methods II | ||
Applied Engr Analy Methods III | ||
Signal Processing | ||
Random Process & Spectral Analysis | ||
Detection & Estimation Theory | ||
Optimal Estimation & Stochastic Control | ||
Fundamentals of Deterministic Digital Signal Processing | ||
Systems Courses | 6.0 | |
Choose 2 courses in robotics and autonomy from the perspective of full systems or use | ||
Introduction to Artificial Intelligence | ||
Machine Learning & Artificial Intelligence | ||
Applied Machine Learning Engineering | ||
Fundamentals of Systems I | ||
Fundamentals of Systems II | ||
Fundamentals of Systems III | ||
Medical Robotics I | ||
Medical Robotics II | ||
Introduction to Robot Technology | ||
Mechanics of Robot Manipulators | ||
Industrial Application of Robots | ||
Core Components | ||
Take 1 course in each of the four disciplines critical to robotics | ||
Perception | 3.0 | |
Pattern Recognition | ||
Fundamentals of Computer Vision | ||
Fundamentals of Image Processing | ||
Wireless Systems | ||
Special Topics in ECET | ||
Nondestructive Evaluation Methods | ||
Cognition and Behavior | 3.0 | |
Introduction to Artificial Intelligence | ||
Introduction to Computer Vision | ||
Machine Learning | ||
Cognitive Systems | ||
Machine Learning & Artificial Intelligence | ||
Applied Machine Learning Engineering | ||
Optimal Estimation & Stochastic Control | ||
Fundamentals of Deterministic Digital Signal Processing | ||
Action | 3.0 | |
Fundamentals of Systems I | ||
Fundamentals of Systems II | ||
Fundamentals of Systems III | ||
Aircraft Flight Dynamics & Control I | ||
Advanced Dynamics I | ||
Advanced Dynamics II | ||
Advanced Dynamics III | ||
Control | 3.0 | |
Applied Machine Learning Engineering | ||
Optimal Estimation & Stochastic Control | ||
Optimal Control | ||
Robust Control Systems I | ||
Robust Control Systems II | ||
Robust Control Systems III | ||
Theory of Nonlinear Control I | ||
Theory of Nonlinear Control II | ||
Theory of Nonlinear Control III | ||
Applied Optimal Control I | ||
Applied Optimal Control II | ||
Advanced Topics in Optimal Control | ||
Technical Focus Areas | 9.0 | |
Take 3 courses in a maximum of two core component areas listed above | ||
Experiential Learning (optional) | ||
Transformational Electives | 6.0 | |
Choose 2 elective courses that promote the development of leadership, communication, and ethics * | ||
Theories of Communication and Persuasion | ||
Culture, Society & Education in Comparative Perspective | ||
Education for Global Citizenship, Sustainability, and Social Justice | ||
Mastery | 6.0 | |
Thesis Option: A minimum of two terms of laboratory-based research that leads to a publicly defended MS thesis. Students will be advised by a faculty member, and when applicable, a representative of industry or government sponsor. | ||
Non-thesis Option: In lieu of the research and thesis, students will complete 6.0 credits of additional coursework in a Technical Focus Area. Graduate Co-op is encouraged for non-thesis students but is not required. | ||
Total Credits | 45.0 |
Sample Plan of Study
First Year | |||||||
---|---|---|---|---|---|---|---|
Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |
ECES 511 | 3.0 | ECE 612 | 3.0 | ECES 513 | 3.0 | VACATION | |
ECES 631 | 3.0 | ECES 512 | 3.0 | ECES 681 | 3.0 | ||
MEM 591 | 3.0 | ECES 642 | 3.0 | EDGI 522 | 3.0 | ||
9 | 9 | 9 | 0 | ||||
Second Year | |||||||
Fall | Credits | Winter | Credits | ||||
ECE 610 | 3.0 | ECE 687 | 3.0 | ||||
ECE 697 | 3.0 | ECE 697 | 3.0 | ||||
MEM 571 | 3.0 | EDGI 510 | 3.0 | ||||
9 | 9 | ||||||
Total Credits 45 |