The minor in Robotics is designed to provide students with a solid and coherent introduction to the field and consists of two parts: four required core courses (9 credit hours) to give students a strong, working foundation in the associated technology and three elective courses (9 credit hours) that allow students to explore various sub-areas within the field or specialize more deeply in one area.
What is Robotics?
Robotics is an interdisciplinary field that involves the application of mechanical engineering, electrical, computer and software engineering, and computer science knowledge for the design, construction and operation of automated machines that can take the place of humans in dangerous environments or manufacturing processes, or resemble humans in appearance, behavior, and/or cognition.
Commercial and industrial robots are now in widespread use performing jobs more cheaply or with greater accuracy and reliability than humans, or that are too dirty, dangerous or dull to be suitable for humans, e.g., in manufacturing, assembly and packing, transport, earth and space exploration, surgery, weaponry, laboratory research, and the mass production of consumer and industrial goods.
Core Courses (required)
To graduate with a minor in Robotics, students must earn an average GPA of 2.0 in six courses (18 credit hours).
MA339 - Applied Linear Algebra (3 credits) OR MA330 - Advanced Engineering Mathematics (3 credits)
EE455 - Introduction to Mobile Robotics (3 credits)
EE456 - Introduction to Robot Manipulators (3 credits)
MP414 - Applied Robotics or equivalent robotics project experience (0 credits)
With elective courses, some course substitutions are possible - a list of acceptable substitutions will be maintained by the Coulter School of Engineering in conjunction with the Mechanical and Aeronautical Engineering department, the Electrical and Computer Engineering department, and the Computer Science department and updated annually.
EE260 - Embedded Systems
EE401 - Digital Signal Processing
EE408 - Software Design For Visual Environments
EE446 - Instrumentation
EE450 - Control Systems
EE451 - Digital Control
EE452 - Optimization Techniques in Engineering
EE465 - Computer Graphics
EE506 - Image Processing and Computer Vision
EE652 - Computer Vision
ME385 - Design of Electromechanical Systems
ME443 - Optimal Engineering
ME444 - Computer Aided Engineering
ME450 - Control Systems
CS449 - Computational Learning
CS451 - Artificial Intelligence
CS452 - Computer Graphics
CS459 - Human-Computer Interaction
CS461 - Mixed Reality
CS465 - Mobile Robotics/Human-Robot Interaction
CS652 - Computer Vision