The Master of Engineering (M.Eng.) program in robotics is interdisciplinary in nature and spans a range of disciplines which include computer engineering, computer science, mechanical engineering, systems engineering, and aerospace engineering. Faculty and professionals teaching our courses are at the forefront of the latest breakthroughs and advances in robotics, which are incorporated into the program curriculum.
The curriculum is designed to cover fundamental and applied topics in design, modeling, and control of robotic systems as well as planning and perception for autonomous robots. We offer courses which cover artificial intelligence, computer vision, motion planning, space and planetary robotics, robot kinematics and dynamics, control, networked robotic systems, robotics at micro- and nano-scale. Students are able to tailor their studies in Optimization, Decision Making, and Algorithms; Performance Analysis and Design Methods; Modeling, Systems and Control; and Sensing, Vision and Perception.
- 10 Courses (Including 4 Core Courses and 6 Technical Electives)
- No Thesis/Research
- No Comprehensive Exam
- 30 Credits
CORE COURSES (required):
ENPM661 Planning for Autonomous Robots (3 credits; offered Spring)
Planning is a fundamental capability needed to realize autonomous robots. Planning in the context of autonomous robots is carried out at multiple different levels. At the top level, task planning is performed to identify and sequence the tasks needed to meet the mission requirements. At the next level, planning is performed to determine a sequence of motion goals that satisfy individual task goals and constraints. Finally, at the lowest level, trajectory planning is performed to determine actuator actions to realize the motion goals. Different algorithms are used to achieve planning at different levels. This course introduces planning techniques for realizing autonomous robots. In addition to covering traditional motion planning techniques, this course emphasizes the role of physics in the planning process. This course will also discuss how the planning component is integrated with control component. Mobile robots will be used as examples to illustrate the concepts during this course. However, techniques introduced in the course will be equally applicable to robot manipulators
ENPM662 Introduction to Robot Modeling (3 credits; offered Fall)
ENPM667 Control of Robotic Systems (3 credits; offered Fall)
This is a basic course on the design of controllers for robotic systems. The course starts with mainstay principles of linear control, with focus on PD and PID structures, and discusses applications to independent joint control. The second part of the course introduces a physics-based approach to control design that uses energy and optimization principles to tackle the design of controllers that exploit the underlying dynamics of robotic systems. The course ends with an introduction to force control and basic principles of geometric control if time allows.
ENPM673 Perception for Autonomous Robots (3 credits; offered Spring)
Perception is a basic fundamental capability for the design of autonomous robots. Perception begins at the sensor level and the class will examine a variety of sensors including inertial sensors and accelerometers, sonar sensors (based on sound), visual sensors (based on light) and depth sensors (laser, time of flight). Perception, in the context of autonomous robots, is carried out in a number of different levels. We begin with the capabilities related to the perception of the robot’s own body and its state. Perception contributes to kinetic stabilization and ego-motion (self motion) estimation. Next come the capabilities needed for developing representations for the spatial layout of the robot’s immediate environment. These capabilities contribute to navigation, i.e. the ability of the robot to go from one location to another. During navigation, the robot needs to recognize obstacles, detect independently moving objects, as well as make a map of the space it is exploring and localize itself in that map. Finally, perception allows the segmentation and recognition of objects in the environment as well as their three dimensional descriptions that can be used for manipulation activities. The course will introduce techniques with hands on projects that cover the capabilities listed before.
TECHNICAL ELECTIVES (all are 3 credit courses):
Optimization and Algorithms
|Performance Analysis and Design Methods
ENME600 Engineering Design Methods
ENME695 Failure Mechanisms and Reliability
ENAE697 Space Human Factors and Life Support
ENSE621 Systems Concepts, Issues, and Processes
|Modeling, Systems and Control
ENME675 A Mathematical Introduction to Robotics
ENME605 Advanced Systems Control
ENEE660 System Theory
ENEE661 Nonlinear Control Systems
ENEE664 Optimal Control
ENEE765 Adaptive Control
ENAE 692 Introduction to Space Robotics
Vision and Perception
ENPM 808P Manufacturing and Automation (3 credits; offered Spring)
|ENPM 808 Independent Study|
For course descriptions, click here.
Current Course Offerings
For current course offerings, click here.
- A bachelor’s degree in an engineering discipline from an accredited institution.
- Courses in mathematics (Calculus I, II, III, and Differential Equations)
- 3.0 GPA or better
- Three letters of recommendation
- Graduate Record Exam (GRE) is NOT required
- TOEFL is required for international students
- Official copies of transcripts
Completed applications are reviewed and considered for admission on a case-by-case basis.
For more information on international admissions, click here
Tuition and Fees
For tuition and fees, click here.
DOMESTIC APPLICATION DEADLINES
Spring 2019 - December 14, 2018
Summer 2019 - May 15, 2019
Fall 2019 - July 26, 2019
Robotics degree programs are administered through the Office of Advanced Engineering Education (OAEE),
INTERNATIONAL APPLICATION DEADLINES
Spring 2019 - September 28, 2018
Fall 2019 - March 15, 2019
Robotics Program Director
Admissions questions: Anna Damm at email@example.com
Program requirements and graduation questions: Sonja Dietrich at firstname.lastname@example.org
Curriculum and coursework questions: Ania Picard at email@example.com