Master of Engineering

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. 

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.

Program Requirements

  • 10 Courses (Including 4 Core Courses and 6 Technical Electives)
    • No Thesis/Research
    • No Comprehensive Exam
  • 30 Credits

Courses

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)

This course introduces basic principles for modeling a robot. Most of the course is focused on modeling manipulators based on serial mechanisms. The course begins with a description of the homogenous transformation and rigid motions. It then introduces concepts related to kinematics, inverse kinematics, and Jacobians. This course then introduces Eulerian and Lagrangian Dynamics. Finally, the course concludes by introducing basic principles for modeling manipulators based on parallel mechanisms. The concepts introduced in this course are subsequently utilized in control and planning courses. 
 

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

ENPM 808F Robot Learning (3 credits; offered Spring)
ENPM 808K Human-Robot Interaction (3 credits; offered Fall)
CMSC 651 Analysis of Algorithms
CMSC712 Distributed Algorithms and Verification
CMSC722 Artificial Intelligence Planning
ENAE681 Engineering Optimization
ENME610 Engineering Optimization
ENME607 Engineering Decision Making
ENEE662 Convex Optimization

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
ENME664 Dynamics
ENEE661 Nonlinear Control Systems
ENEE664 Optimal Control
ENEE765 Adaptive Control
ENAE 692 Introduction to Space Robotics

Vision and Perception

CMSC733 Computer Processing of Pictorial Information
CMSC734 Information Visualization
ENEE631 Digital Image and Video Processing
ENEE633 Statistical Pattern Recognition
ENEE731 Image Understanding

Applications

ENPM 808P Manufacturing and Automation (3 credits; offered Spring)
ENPM 808J Rehabilitation Robotics (3 credits)
ENPM 808X Software Development for Robotics (3 credits)
ENPM 809B Building a Manufacturing Robot Software System (3 credits)

ENPM 808  Independent Study
 

Course Descriptions

For course descriptions, click here.

Current Course Offerings

For current course offerings, click here. 

Admission Requirements

  • 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.

International Students

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

Apply to M.Eng. program.

Learn More

Sign up for next virtual open house.

Download program brochure.

Robotics Program Director

Professor Miao Yu

Questions

Admissions questions: Anna Damm at adamm1@umd.edu

Program requirements and graduation questions: Sonja Dietrich at sdietric@umd.edu

Curriculum and coursework questions: Ania Picard at appicard@umd.edu