Robotics and Autonomy
Major: Robotics and Autonomy
Degree Awarded: Master of Science (MS)
Calendar Type: Quarter
Total Credit Hours: 45.0
Co-op Option: None
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.
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.
For additional information on how to apply, visit Drexel's Admissions page for Robotics and Autonomy.
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 | ||
| 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 | ||
| Cognition and Behavior | 3.0 | |
| Introduction to Artificial Intelligence | ||
| Introduction to Computer Vision | ||
| Machine Learning | ||
| Cognitive Systems | ||
| 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 | |
| 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 | ||
| Transformational Electives | 6.0 | |
| Choose 2 elective courses that promote the development of leadership, communication, and ethics * | ||
| Technical Writing | ||
| Theories of Communication and Persuasion | ||
| Culture, Society & Education in Comparative Perspective | ||
| Globalization and Educational Change | ||
| 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 six credits of additional coursework in a Technical Focus Area. Graduate Co-op is encouraged for non-thesis students, but is not required. | ||
| Experiential Learning (optional) | ||
| Total Credits | 45.0 | |
Sample Plan of Study
| First Year | ||
|---|---|---|
| Fall | Credits | |
| ECES 511 | Fundamentals of Systems I | 3.0 |
| ECES 681 | Fundamentals of Computer Vision | 3.0 |
| MEM 591 | Applied Engr Analy Methods I | 3.0 |
| Term Credits | 9.0 | |
| Winter | ||
| ECES 512 | Fundamentals of Systems II | 3.0 |
| ECES 642 | Optimal Control | 3.0 |
| ECES 631 | Fundamentals of Deterministic Digital Signal Processing | 3.0 |
| Term Credits | 9.0 | |
| Spring | ||
| CS 613 | Machine Learning | 3.0 |
| ECES 513 | Fundamentals of Systems III | 3.0 |
| EDGI 512 | Globalization and Educational Change | 3.0 |
| Term Credits | 9.0 | |
| Second Year | ||
| Fall | ||
| CS 510 | Introduction to Artificial Intelligence | 3.0 |
| ECE 697 | Research | 3.0 |
| MEM 571 | Introduction to Robot Technology | 3.0 |
| Term Credits | 9.0 | |
| Winter | ||
| CS 583 | Introduction to Computer Vision | 3.0 |
| ECE 697 | Research | 3.0 |
| EDGI 510 | Culture, Society & Education in Comparative Perspective | 3.0 |
| Term Credits | 9.0 | |
| Total Credit: 45.0 | ||
