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 Master of Science in Robotics and Autonomy at Drexel University is an interdisciplinary program designed to prepare professionals for leadership roles in the research, development, and design of robotic systems and technologies. Offered through the Electrical and Computer Engineering department in conjunction with the Mechanical Engineering Department, the program addresses the rapid advancements in robotics and their applications across industries.
The curriculum is built around four foundational concepts of robotics:
- Perception: Gathering data from the robot's surroundings.
- Cognition: Processing data to understand the environment.
- Control: Making decisions based on environmental information.
- Action: Executing decisions through movement and interaction with the physical world.
The degree requires a minimum of 45.0 approved credits. The curriculum is customizable, with students developing their plan of study in consultation with their academic advisor. This program is ideal for those aiming to advance their careers in robotics or autonomy-related fields while addressing critical challenges through innovative robotic applications. Students will develop:
- Expertise in machine learning and robotics fundamentals.
- Skills for designing, building, programming, deploying, and evaluating robotic systems.
- Critical thinking and creativity to innovate solutions for societal and industrial challenges.
- Proficiency in integrating industry-leading tools for automation and robotics.
Graduates are equipped to impact many critical areas of society and industry, from medicine and healthcare to large retailers, and incorporation of Industry 4.0, advanced manufacturing, and the Internet of Things. The program also provides a strong foundation for entrepreneurial ventures or further academic pursuits.
Students also have the option to engage in research under the supervision of a faculty member. A total of 9.0 credits of research-oriented coursework may be counted towards the minimum credit hour requirement of the degree program. These credits are considered general electives.
The MS program is designed 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.
Full-time on-campus students who start in a fall term 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 or visit the Drexel Engineering Graduate Co-op webpage.
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. Preferred bachelor's programs include electrical engineering, computer engineering, mechanical engineering, and computer science. An undergraduate degree earned abroad must be deemed equivalent to a US bachelor's.
The GRE general test is optional for all applicants. TOEFL, IELTS, PTE, or Duolingo is required if the language of instruction of your previous degree was not English.
For additional information on how to apply and deadlines, visit the School of Engineering's Admission Guidelines or Drexel's admissions page for Robotics and Autonomy.
Degree Requirements
| Foundation Courses | ||
| Choose 2 courses in mathematics and/or signal processing | 6.0 | |
| Mathematics | ||
Any MATH 500-700 level course * | ||
| Analytical Methods in Systems | ||
| Probability & Random Variables | ||
| 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 | ||
| Choose 2 courses in robotics and autonomy from the perspective of full systems or use | 6.0 | |
| Applied Robotics Laboratory | ||
| 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 | ||
| 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 | |
| Mobile Sensing and Motion Planning | ||
| Pattern Recognition | ||
| Fundamentals of Computer Vision | ||
| Fundamentals of Image Processing | ||
| Wireless Systems | ||
| Special Topics in Telecommunications | ||
| 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 | |
| Mobile Sensing and Motion Planning | ||
| Reinforcement Learning | ||
| 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 | ||
| Take 3 courses from the Core Components areas or Systems area listed above | 9.0 | |
| Elective Courses ** | ||
| 6.0 credits at the 500-700 level from the College of Engineering or other approved subject areas. | 6.0 | |
| Mastery | ||
| Thesis Option: Students will complete two terms (3.0 credits per term; 6.0 total credits) of ECE 898, which is 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. | 6.0 | |
| Non-thesis Option: In lieu of research and a thesis, students will complete 6.0 credits of coursework from the Aligned Mathematical Theory, Applications, or Signal Processing areas. | ||
| Optional Co-op Experience *** | 0-1 | |
| Career Management and Professional Development for Master's Degree Students | ||
| Total Credits | 45.0-46.0 | |
- *
Recommended MATH courses include MATH 510, MATH 521, MATH 631, and MATH 630.”
- **
Electives include 15.0 credits at the 500-900 level in the following subject areas: ECE, ECEC, ECEE, ECEP, ECES, ECET, AE, BIO, BMES, CHE, CHEM, CIVE, CMGT, CS, EGMT, ENGR, ENTP, ENVE, ET, MATE, MATH, MEM, MGMT, MIS, OPR, PHYS, PROJ, or SYSE
- ***
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 | |
| ECE 608 | Decision-Making for Robotics | 3.0 |
| ECES 511 | Fundamentals of Systems I | 3.0 |
| ECES 521 | Probability & Random Variables | 3.0 |
| Credits | 9 | |
| Winter | ||
| ECE 609 | Mobile Sensing and Motion Planning | 3.0 |
| ECE 612 | Applied Machine Learning Engineering | 3.0 |
| ECES 512 | Fundamentals of Systems II | 3.0 |
| Credits | 9 | |
| Spring | ||
| ECE 603 | Computing and Control | 3.0 |
| ECES 513 | Fundamentals of Systems III | 3.0 |
| Elective Course 1 | 3.0 | |
| Credits | 9 | |
| Summer | ||
| VACATION | ||
| Credits | 0 | |
| Second Year | ||
| Fall | ||
| ECE 687 | Pattern Recognition | 3.0 |
| ECE 898 | Master's Thesis | 3.0 |
| ECES 682 | Fundamentals of Image Processing | 3.0 |
| Credits | 9 | |
| Winter | ||
| ECE 898 | Master's Thesis | 3.0 |
| ECES 681 | Fundamentals of Computer Vision | 3.0 |
| Elective Course 2 | 3.0 | |
| Credits | 9 | |
| Total Credits | 45 | |
Full Time With CO-OP
| First Year | ||
|---|---|---|
| Fall | Credits | |
| COOP 500 | Career Management and Professional Development for Master's Degree Students | 1.0 |
| ECE 608 | Decision-Making for Robotics | 3.0 |
| ECES 511 | Fundamentals of Systems I | 3.0 |
| ECES 521 | Probability & Random Variables | 3.0 |
| Credits | 10 | |
| Winter | ||
| ECE 609 | Mobile Sensing and Motion Planning | 3.0 |
| ECES 512 | Fundamentals of Systems II | 3.0 |
| Elective Course 1 | 3.0 | |
| Credits | 9 | |
| Spring | ||
| ECE 603 | Computing and Control | 3.0 |
| ECE 610 | Machine Learning & Artificial Intelligence | 3.0 |
| ECES 513 | Fundamentals of Systems III | 3.0 |
| Credits | 9 | |
| Summer | ||
| ECE 612 | Applied Machine Learning Engineering | 3.0 |
| ECE T580 | Special Topics in Electrical & Computer Engineering | 3.0 |
| ECES 681 | Fundamentals of Computer Vision | 3.0 |
| Credits | 9 | |
| Second Year | ||
| Fall | ||
| COOP EXPERIENCE | ||
| Credits | 0 | |
| Winter | ||
| COOP EXPERIENCE | ||
| Credits | 0 | |
| Spring | ||
| ECES 682 | Fundamentals of Image Processing | 3.0 |
| ECES 686 | 3.0 | |
| Elective Course 2 | 3.0 | |
| Credits | 9 | |
| Total Credits | 46 | |