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 COE Graduate Co-op and 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 | ||
Analytical Methods in Systems | ||
Probability & Random Variables | ||
Applied Probability and Statistics I | ||
Numerical Analysis II | ||
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 | ||
Genomic Signal Processing | ||
Bioinformatics | ||
Systems Courses | 6.0 | |
Choose 2 courses in robotics and autonomy from the perspective of full systems or use | ||
Computing and Control | ||
Decision-Making for Robotics | ||
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 | ||
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 | |
Mobile Sensing and Motion Planning | ||
Pattern Recognition | ||
Fundamentals of Computer Vision | ||
Fundamentals of Image Processing | ||
Wireless Systems | ||
Special Topics in ECET | ||
Nondestructive Evaluation Methods | ||
Cognition and Behavior | 3.0 | |
Machine Learning & Artificial Intelligence | ||
Applied Machine Learning Engineering | ||
Reinforcement Learning | ||
Cell & Tissue Image Analysis | ||
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 | |
Computing and Control | ||
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. | ||
Optional Co-op Experience * | 0-1 | |
Career Management and Professional Development for Master's Degree Students | ||
Total Credits | 45.0-46.0 |
- *
Co-op is an option for this degree for full-time on-campus students. To prepare for the 6-month co-op experience, students will complete: COOP 500. The total credits required for this degree with the co-op experience is 46.0
Students not participating in the co-op experience will need 45.0 credits to graduate.
Sample Plan of Study
Full Time, No CO-OP
First Year | |||||||
---|---|---|---|---|---|---|---|
Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |
ECE 608 | 3.0 | ECE 609 | 3.0 | COM 610 | 3.0 | VACATION | |
ECES 511 | 3.0 | ECE 612 | 3.0 | ECE 603 | 3.0 | ||
ECES 521 | 3.0 | ECES 512 | 3.0 | ECES 513 | 3.0 | ||
9 | 9 | 9 | 0 | ||||
Second Year | |||||||
Fall | Credits | Winter | Credits | ||||
ECE 687 | 3.0 | ECE 898 | 3.0 | ||||
ECE 898 | 3.0 | ECES 681 | 3.0 | ||||
ECES 682 | 3.0 | EDGI 510 | 3.0 | ||||
9 | 9 | ||||||
Total Credits 45 |
Full Time With CO-OP
First Year | |||||||
---|---|---|---|---|---|---|---|
Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |
COOP 500 | 1.0 | ECE 609 | 3.0 | ECE 603 | 3.0 | ECE 612 | 3.0 |
ECE 608 | 3.0 | ECES 512 | 3.0 | ECE 610 | 3.0 | ECE T580 | 3.0 |
ECES 511 | 3.0 | EDGI 510 | 3.0 | ECES 513 | 3.0 | ECES 681 | 3.0 |
ECES 521 | 3.0 | ||||||
10 | 9 | 9 | 9 | ||||
Second Year | |||||||
Fall | Credits | Winter | Credits | Spring | Credits | ||
COOP EXPERIENCE | COOP EXPERIENCE | COM 610 | 3.0 | ||||
ECES 682 | 3.0 | ||||||
ECES 686 | 3.0 | ||||||
0 | 0 | 9 | |||||
Total Credits 46 |