Master of Engineering in Robotics
The Master of Engineering in Robotics degree is intended to address the multidisciplinary nature of the field of robotics. It can be considered as a terminal degree or as preparation for the Ph.D. program. The program exposes students to both the mathematical foundations of robotics, and to relevant hands-on laboratory projects in robotics and mechatronics. In doing so, the curriculum spans a wide spectrum of multidisciplinary topics, including the physical and mathematical modeling, analysis, and design principles needed to understand the geometry, kinematics, and dynamics of robotic systems, as well as the sensors, actuators, algorithms, computing, and energy resources needed to accomplish relevant, real-world tasks that may be tele-operated, automated, fully autonomous, or performed in cooperation with humans.
Program Objectives
The program will:
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establish a foundation for the modeling and analysis of robotic systems, including robot geometry, kinematics and dynamics.
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provide students with hands-on exposure to programming, testing, measuring, and deploying robotic systems, through relevant lab exercises and research opportunities.
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equip students with the skills needed to specify a suite of actuators, sensors, algorithms, and computing resources for a robotic system required by a real-world engineering task.
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introduce the typical system architectures used in the successful deployment of real-world robots, including the control systems, perceptual algorithms, and motion and task planning algorithms required.
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discuss the many commercial, industrial, and societal applications influenced by robotics in the 21st Century, and the roles of robotics engineers in these sectors.
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illustrate the many ways in which robots can provide an effective physical embodiment for modern advances in artificial intelligence and machine learning.
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prepare students effectively for the modern workforce which increasingly demands multi-disciplinary training, including experience in embedded systems and software, for all engineering jobs involving the design and implementation of robotics systems.
Program Outcomes
The program’s graduating students will be in a strong position to grow technically as well as in the management track. This is supported by the breadth of hands-on laboratory experiences, acquired in-depth knowledge, and cutting-edge research experiences attainable while attending the Masters of Engineering in Robotics program at Stevens. The highest-achieving graduates will establish a strong foundation for future career growth in the robotics sector.
By the time of graduation, students will be able to:
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use modeling, analysis, and design principles, capture the needs of the customer and translate them into a specification for a high-performance robotic system.
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understand the sensing, actuation, algorithmic, and computing requirements of a desired robot task or operational scenario, and select as well as implement appropriate sensors, actuators, and embedded and/or networked computing resources.
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use state-of-the-art simulation tools to model, analyze, predict, and optimize the performance of robotic systems in the early stages of research and development.
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evaluate accuracy, precision, energy consumption, and economics of the teleoperated, automated, and autonomous operation of robotic systems.
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apply human factors principles in the design and control of robotic systems that are supervised, operated by, or worn by humans.
Degree Requirements
The program is a 30-credit degree program. Students are required to complete:
A student may substitute a Project (ME 800 Special Problems in Mechanical Engineering, 3 credits) or a Master’s Thesis (ME 900 Thesis in Mechanical Engineering) for the appropriate number of courses – these options qualify as mechanical engineering robotics elective courses.
Courses not used toward those requirements from the engineering tools and methods courses and mechanical engineering robotics core courses may be taken as mechanical engineering robotics elective courses.
Engineering Tools and Methods Courses
| ME 564 | Optimization Principles in Mechanical Engineering | 3 |
| ME 594 | Numerical Methods in Mechanical Engineering | 3 |
| ME 635 | Modeling and Simulation | 3 |
| ME 641 | Engineering Analysis I | 3 |
| ME 651 | Analytic Dynamics | 3 |
M.E. Robotics Core Courses
| ME 598 | Introduction to Robotics | 3 |
| ME 621 | Introduction to Modern Control Engineering | 3 |
| ME 650 | Robot Manipulators | 3 |
| ME 655 | Wearable Robotics and Sensors | 3 |
| ME 656 | Autonomous Navigation for Mobile Robots | 3 |
Elective Courses
Six elective courses must be chosen. Of these six courses, 2 courses must be selected from the list of Mechanical Engineering Robotics Elective Courses, 2 courses must be selected from the list of Computer Science (CS), Electrical and Computer Engineering (EE & CE), and Mathematical Sciences (MS) Elective Courses, 1 course must be selected from the list of Mechanical Engineering Robotics, Computer Science (CS), Electrical and Computer Engineering (EE & CE), and Mathematical Sciences (MS) elective courses. A student may substitute a Project (ME 800 Special Problems in Mechanical Engineering, 3 credits) or a Master’s Thesis (ME 900 Thesis in Mechanical Engineering) for the appropriate number of courses – these options qualify as Mechanical Engineering Robotics Elective Courses. Additionally, courses from the above lists of Engineering Tools and Methods Courses, and M.E. Robotics Core Courses, not used toward those requirements, may be taken as Mechanical Engineering Robotics Elective Courses.
Mechanical Engineering Robotics Elective Courses
| ME 522 | Mechatronics | 3 |
| ME 551 | Microprocessor Applications in Mechanical Engineering | 3 |
| ME 622 | Robust and Adaptive Control for Dynamical Systems | 3 |
| ME 631 | Mechanical Vibrations | 3 |
| ME 685 | Mobile Microrobotic Systems | 3 |
| ME 702 | Practicum in Mechanical Engineering | 1 - 3 |
ME 702: Counts as an elective course if taken three times, otherwise counts for extra credit in addition to the required 30 credits.
Computer Science, Electrical and Computer Engineering, and Mathematical Sciences Elective Courses
| CS 532 | 3D Computer Vision | 3 |
| CS 541 | Artificial Intelligence | 3 |
| CS 558 | Computer Vision | 3 |
| CS 559 | Machine Learning: Fundamentals and Applications | 3 |
| CS 560 | Statistical Machine Learning | 3 |
| CS 570 | Introduction to Programming, Data Structures, and Algorithms | 3 |
| CS 583 | Deep Learning | 3 |
| CS 590 | Algorithms | 3 |
| CPE 521 | Autonomous Mobile Robotic Systems | 3 |
| CPE 595 | Applied Machine Learning | 3 |
| EE 551 | Engineering Programming: Python | 3 |
| EE 553 | Engineering Programming: C++ | 3 |
| EE 621 | Nonlinear Control | 3 |
| EE 631 | Cooperating Autonomous Mobile Robots | 3 |
| MA 655 | Optimal Control Theory | 3 |
| MA 661 | Dynamic Programming and Reinforcement Learning | 3 |
In order to graduate with a Master of Engineering – Robotics degree, a student must obtain a minimum average of “B”.