Robotics Master’s Degree Curriculum Overview
The Robotics master’s degree program is intended to extend and broaden the undergraduate preparation. It can be considered as a terminal degree or as preparation for the Ph.D. program. The program covers a wide spectrum of relevant 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.
By the end of this program, students will be able to:
- Use modeling, analysis, and design principles, capture the needs of the customer and translate them into a specification for a high-performance robotic system
- Use the state-of-the-art simulation tools to model, analyze, predict, and optimize the performance of robotic systems
- 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
- Acquire experience in embedded systems and software for all engineering jobs involving the design and implementation of robotics systems.
Below are some of the typical courses available in this program.
Robotics Core Courses Include:
- ME 598: Introduction to Robotics
- ME 621: Introduction to Modern Control Engineering
- ME 654: Advanced Robotics
- ME 522: Mechatronics
- ME 551: Microprocessor Applications in Mechanical Engineering
- ME 656: Autonomous Navigation for Mobile Robots
- ME 655: Wearable Robots and Sensors
- ME 641: Engineering Analysis I
- ME 631: Mechanical Vibrations I
- ME 635: Modeling and Simulation
- ME 651: Analytical Dynamics
- ME 622: Optimal Control and Estimation of Dynamical Systems
- ME 685: Mobile Microrobotic Systems
- CS 541: Artificial Intelligence
- CS 559: Machine Learning: Fundamentals & Applications
- CPE 521: Introduction to Autonomous Robots
- EE 621: Nonlinear Control
- EE 631: Cooperating Autonomous Mobile Robots
- MA 655: Optimal Control
- MA 661: Dynamic Programming and Reinforcement Learning
If you have existing graduate credits or experience in this area of study, contact [email protected] to discuss opportunities to include it in the curriculum.