Robotics and Autonomous Systems (MS)

The Master of Science in Robotics and Autonomous Systems (MSRAS) offers the skills necessary to analyze, design and develop emerging robotic and autonomous platforms that are increasingly becoming part of human society. These platforms embody technologies and concepts from multiple disciplines spanning electrical and electronic hardware, autonomous control, computer vision, machine learning, manipulation, mechatronics, autonomous vehicles, and medical robotics. The MS degree is a 30-credit hour graduate program that offers a thesis option. Thesis students can replace 6 credit hours of technical electives with thesis credits. There are 4 required courses of 3 hours each from Perception, Cognition, Action, and Hardware areas (thus totaling 12 credit hours). The remaining 6 additional courses (18 credit hours) must be selected from a list of restricted electives, each of which is 3 credit hours. No thesis is required, but one can be completed and will count for 6 credit hours of restricted electives.

Program Prerequisites

For admission, a 3.0 GPA and an undergraduate degree in Engineering, Computer Science, Mathematics, Statistics, or Physics is preferred.

In addition to the general UCF graduate application requirements, applicants to this program must provide:

• One official transcript (in a sealed envelope) from each college/university attended.
• Resume
• Letters of recommendation (encouraged but not required)

Graduation with an MSRAS requires 30 credit hours beyond the bachelor’s degree, including 12 credit hours of required courses and 18 credit hours of electives. Students must receive a B or better grade in all courses in the program. However, if a student receives a B- or worse grade in a course, they may repeat that course in a future semester.

Degree Requirements

Required Courses

• Complete all of the following
  Perception
  • Complete the following:
    • CAP5415 - Computer Vision (3)
  Cognition
  • Complete at least 1 of the following:
    • CAP5610 - Machine Learning (3)
    • EEL5825 - Machine Learning and Pattern Recognition (3)
  Action
  • Complete all of the following
    • Complete at least 1 of the following:
      • EEL5669 - Introduction to Robotics and Autonomous Vehicles (3)
      • EEL5690 - Medical Robotics (3)
    • If students would like to take both courses in the Action set, one course can be taken as the required course with the other counting as an elective.
  Hardware
  • Complete the following:
    • EAS5407C - Mechatronic Systems (3)

Elective Courses

Electives
• Earn at least 12 credits from the following:
  • CAP6419 - 3D Computer Vision (3)
  • CAP6411 - Computer Vision Systems (3)
  • CAP6412 - Advanced Computer Vision (3)
  • CAP6614 - Current Topics in Machine Learning (3)
  • CAP6671 - Intelligent Systems: Robots, Agents, and Humans (3)
  • EEL6812 - Introduction to Neural Networks and Deep Learning (3)
  • EEL6662 - Advanced Robotics (3)
  • EEL6667 - Mobile Robotic Systems (3)
  • EEL6674 - Optimal Estimation for Control (3)
  • EEL6875 - Artificial Intelligence for Autonomous Systems (3)
  • EEL6683 - Cooperative Control of Networked Autonomous Systems (3)
  • EAS6415 - Guidance, Navigation and Control (3)
  • EML6808 - Analysis and Control of Robot Manipulators (3)
  • EML6295 - Sensors and Actuators for Micro Mechanical Systems (3)
  • CAP6908 - Directed Independent Studies (1 - 99)
  • EEL6908 - Directed Independent Studies (0 - 99)
  • EAS6908 - Directed Independent Studies (1 - 99)
  • EML6908 - Directed Independent Studies (1 - 99)

Thesis/Nonthesis Option

• Complete 1 of the following
  Thesis Option
  • Earn at least 6 credits from the following types of courses: Master's Thesis Course (CAP 6971, EAS 6971, EEL 6971, or EML 6971)
  Nonthesis Option
  • Earn at least 6 credits from the following types of courses: Nonthesis students must complete at least 6 additional credit hours of electives from the list above.

Grand Total Credits: 30

Financial Information

Graduate students may receive financial assistance through fellowships, assistantships, tuition support, or loans. For more information, see the College of Graduate Studies Funding website, which describes the types of financial assistance available at UCF and provides general guidance in planning your graduate finances. The Financial Information section of the Graduate Catalog is another key resource.

Fellowship Information

Fellowships are awarded based on academic merit to highly qualified students. They are paid to students through the Office of Student Financial Assistance, based on instructions provided by the College of Graduate Studies. Fellowships are given to support a student's graduate study and do not have a work obligation. For more information, see UCF Graduate Fellowships, which includes descriptions of university fellowships and what you should do to be considered for a fellowship.

The MS degree is 30 credit hours at the graduate level. There are 4 required courses of 3 hours each from Perception, Cognition, Action, and Hardware areas (thus totaling 12 credit hours). The remaining 6 additional courses (18 credit hours) must be selected from a list of restricted electives, each of which is 3 credit hours. Students can use one course in the Action required course grouping (EEL 5669 or EEL 5690) as a restricted elective; there are two courses in the grouping and one can be taken as the required course with the other counting as an elective. No thesis is required, but one can be completed and will count for 6 credit hours of restricted electives.

REQUIRED COURSES

Perception

CAP 5415 Computer Vision. 3(3,0). PR: COP 3503C, MAC 2312 and COT 3960. Image formation, binary vision, region growing and edge detection, shape representation, dynamic scene analysis, texture, stereo and range images, and knowledge representation.

Cognition
CAP 5610. Machine Learning. 3(3,0). PR: CAP 4630 or C.I. Origin/evaluation of machine intelligence; machine learning concepts and their applications in problem solving, planning and "expert systems" symbolic role of human and computers.

OR

EEL 5825. Machine Learning and Pattern Recognition. 3(3,0) PR: EEL 3021 or STA 3032 or similar course in probability. Preliminaries of machine learning and pattern recognition, classification and regression, Neural Networks, decision tree classifiers, unsupervised learning, and other state-of-the-art topics.

Action
EEL 5669. Introduction to Robotics and Autonomous Vehicles. 3(3,0). PR: EEL 5173 or C.I. Forward and inverse kinematics, velocity kinematics, dynamics, constrained motions, path and trajectory planning, position and trajectory control, single and multivariable control, introduction to force/impedance control, introduction to consensus-based control.

OR

EEL5690. Medical Robotics. 3(3,0). PR: EEL 3657 or medical students in their second year or later. Medical robots for minimally invasive surgery, kinematics, constrained workspace and dexterity, haptics, tele-operation and network based control, basics of laparoscopic surgery.

Hardware
EAS 5407 Mechatronic Systems. 3(3,0). PR: EML 3034C. Discrete control techniques for aerospace mechatronic systems. Controller design, test and evaluation.

ELECTIVE COURSES

CAP 6419 3D Computer Vision. 3(3,0). PR: CAP 5415. 2D/3D Projective Geometry, Projective Transformation Estimation, Camera Calibration, Single View Modeling, Bi-focal Modeling, Fundamental Matrix, Stratified Structure, Homography, Tri-focal Tensor, Auto-Calibration, Chirality.

CAP 6411 Computer Vision Systems. 3(3,0). PR: COP 5711 or C.I. Recent systems contributing toward recognition, reasoning, knowledge representation, navigation, and dynamic scene analysis. Comparisons, enhancements, and integrations of such systems.

CAP 6412 Advanced Computer Vision. 3(3,0). PR: CAP 5415. Computational theories of perception, shape from IX techniques, multi-resolution image analysis, 3-D model based vision, perceptual organization, spatiotemporal model, knowledge-based vision systems

CAP 6614 Current Topics in Machine Learning. 3(3,0). PR: CAP 5610 or C.I. Machine learning, the study of algorithms that allow computer programs to learn from experience, is a rapidly changing area. This course will be a deep dive into current topics in machine learning, collected from papers appearing at recent machine learning conferences.

CAP 6671 Intelligent Systems: Robots, Agents, and Humans. 3(3,0). PR: CAP 5610 or C.I. Includes practical techniques for designing intelligent agents capable of planning, learning, and cooperation. Discussion of psychological/social issues.

EEL 6812 Introductions to Neural Networks and Deep Learning. 3(3,0). PR: EEL 5825 or EEL 4798 or EEL 4750 or C.I. Advanced Machine Learning and Applications. Perception Network, Convolutional NN, Recurrent NN, GAN, and Deep Reinforcement Learning.

EEL 6662 Advanced Robotics. 3(3,0). PR: EEL 5559 or C.I. Geometric Nonlinear Control, Control of Redundant Robots, Computer Vision and Vision-based control, Formation Control, and Cooperative Rules and Behaviors of Robotic Vehicles.

EEL 6667 Mobile Robotic Systems. 3(3,0). PR: EEL 5173 or EEL 5630. Non-holonomic systems, kinematics and dynamics, trajectory planning and obstacle avoidance, canonical terms, control design, stability, performance, and robustness.

• EEL 6674 Optimal Estimation for Control. 3(3,0). PR: EEL 5173 or C.I. Optimal filtering, smoothing, and prediction methods are analyzed with applications to a number of linear and nonlinear dynamic systems.

• EEL 6875 Autonomous Agents. 3(3,0). PR: EEL 4872 or CAP 4630 or C.I. Agent architectures, including behavioral, decision theoretic and logic (BDI) based. Multi-agent systems, agent communication languages. Negotiation, argumentation, coalition formation. Project oriented.

• EEL 6683 Cooperative Control of Networked Autonomous Systems. 3(3,0). PR: EEL5173 or C.I. Fundamentals of cooperative control theory for autonomous vehicles and agents, with emphasis on consensus, effects of intermittent and delayed communication/sensing network, and cooperative control designs.

EAS 6415 Guidance, Navigation, and Control. 3(3,0). PR: EML 5060, EAS 6507. Inertial and GPS navigation techniques. Explicit and implicit guidance formulations. Robust control applications to aircraft, missile and space vehicles.

• EML 6808 Analysis and Control of Robot Manipulators. 3(3,0.) PR: EML 4312C, EML 5271, or C.I.

Kinematics and dynamics of multibody systems, especially robot manipulators. Design and control of robot manipulators.

EML 6295 Sensors and Actuators for Micro Mechanical Systems. 3(3,0). PR: EML 5060, EML 6211, or C.I. Classifications of sensors and actuators. Physics of sensing and actuation. Evaluation of sensors and actuators.

Independent Study (CAP 6908, EEL 6908, EAS 6908, or EML 6908 ): up to 6 credit hours can count towards electives

Masters Thesis (CAP 6971, EEL 6971, EAS 6971, or EML 6971): up to 6 credit hours can count towards electives