Robotics Engineering

The ECE Department offers a program totaling 30 credit hours, leading to the degree of Master of Science in Engineering (Robotics Engineering). Students desiring admission to the program must have earned a Bachelor's degree in Robotics, Electrical, Computer, Mechanical, Industrial and Manufacturing Systems Engineering or Computer Science with an overall GPA of 3.0 or higher. Students whose undergraduate background is in other fields may be given conditional admission and would be required to take preparatory courses in the aforementioned fields as described in section V. Students admitted to the program are required to take courses as specified below. Students must earn a B or better in every graduate course to be credited toward the degree requirements. However, a maximum of two grades of B- will be accepted. In addition, students must maintain a cumulative GPA of 3.0 or higher in every semester. Students may be placed on probation, if their cumulative GPA falls below 3.0. Finally, a cumulative GPA of 3.0 or higher is required, in order to be eligible to receive the MSE (RE) degree. 

The program may be completed entirely on campus, entirely online, or through a combination of on-campus and online courses.

Program Requirements

Core Courses
ECE 5001Analytic and Comp Math3
ECE 545Intro Robot Syst3
Selected ONE course from the following:
ECE 543Kinem, Dynam Control Robots3
ECE 544Mobile Robots3
Concentration Courses9 to 11 credits
Sensing and Processing
ECE 555Stochastic Processes3
ECE 580Digital Signal Processing3
ECE 582Intro to Statistical DSP3
ECE 584Speech Processes3
ECE 586Digital Image Processing3
ECE 587Sel Top:Image Proc/Mach Vision3
ECE 588Robot Vision3
IMSE 606Advanced Stochastic Processes3
ECE 642Robotic Embed Sys3
Systems and Control
ECE 560Modern Control Theory3
ECE 565Digital Control Systems3
ECE 567Nonlinear Control Systems3
ECE 643Humanoids3
ECE 644Advanced Robotics3
ECE 665Optimal Control Systems3
ECE 661Sys Ident and Adaptive Control3
Machine Learning and Reasoning
ECE 528Cloud Computing3
ECE 537Data Mining3
ECE 552Fuzzy Systems3
ECE 574Adv Sftwr Technq in Eng Appl3
ECE 5752Reconfigurable Computing3
ECE 579Intelligent Systems3
ECE 5831Pat Rec & Neural Netwks3
Autonomous Vehicle
ECE 531Intelligent Vehicle Systems3
ECE 532Auto Sensors and Actuators3
ECE 533Active Automotive Safety Sys3
ECE 535Mob Dev & Ubiqys Comp Sys3
ECE 554Embedded Systems3
ECE 566Mechatronics3
ECE 5701Intro to Wireless Comm3
ECE 577Engineering in Virtual World3
ECE 679Adv Intelligent Sys3
Professional Electives
Select six credit hours6
Select 4 to 6 credit hours4-6

Professional Electives

Students may complete the professional elective in several ways: (1) Elect the thesis ECE 699 (6 credits) to work under the supervision of a faculty advisor, (2) Take directed study by ECE 591 (3 credits) and another RE course at graduate level, (3) take another two RE courses from the list above.

Cognate Courses

Students should select a minimum of 4 and a maximum of 6 credit hours of courses from other disciplines. Some courses from outside ECE may not meet cognate requirement. Please check with the ECE Department prior to registering.

Preparatory Courses

Students with inadequate background in Robotics, Electrical, or Computer Engineering may be required to meet with the department graduate advisor to determine the need for preparatory courses.

For further information please contact:

Department of Electrical and Computer Engineering
University of Michigan-Dearborn,
4901 Evergreen Road
Room 206 ELB, Dearborn, MI 48128-2406
Tel: 313-593-5420 Fax: 313-583-6336

Learning Goals

  1. A strong background in theories and a good knowledge of the latest technologies in the robotics engineering discipline.
  2. An ability to conduct research in advanced engineering fields. The students will possess appropriate skills in formulating problems, designing experiments, collecting, processing, analyzing and interpreting data, designing a system, component, or process to meet desired requirements, and evaluating the system performances.
  3. An ability to learn the latest research advancement, use advanced techniques and modern engineering tools in engineering practice, evaluate different strategies to derive a feasible solution.