Vision-Guided Robotics is a major focus of research at the CVIP Lab, which enables robot arms to smartly track dynamic objects moving on random paths, and adjust the grabbing/touching force based on objects size, softness and shape.
Robotics and Automation
The future is here, as engineers have already introduced robots to healthcare, law enforcement, supply chains, etc. Speed School students don’t worry that robots are coming to steal our jobs. They’re too busy devising ways to make our jobs easier, more productive, and more beneficial to society. Robotics can be applied to nearly any discipline within engineering, with solutions emerging in nursing, surgery, manufacturing, distribution, and aeronautics — among many others.
Has been allocated in scholarships through the Robotics Education & Competition (REC) Foundation
Faculty Story"Autonomous robotics (AR) encapsulates mechanical designs for a particular purpose, with sensors, communications, power and control, to achieve the functionality required. The CVIP Lab uses AR for military, industrial and biomedical applications."
Dr. Aly Farag - Director, Computer Vision and Image Processing Lab
News
Working with student researchers, ME Professor Stuart Williams creates vibrant images from evaporated drops of diluted whiskey to identify counterfeits as well as provide insight into their chemical properties.
After exhaustive national searches, the ME Department is pleased to welcome three outstanding new faculty.
Graduate Fellowship established in honor of alumnus Edward E. Toutant
Facility Aerial Robotics Lab
In the Aerial Robotics Lab, Speed Students build unmanned drones with complex navigation systems, secure mobile networking, and fault tolerance. Most of this work is completed with off-the-shelf or 3D-printed components.
Featured Publications
An assistive robot in a health-care environment has to be able to perform routine tasks and be aware of the surrounding environment at the same time. Ankita Sahu
The introduction of a novel way of blending machine learning with fuzzy logic so that an adaptable, yet intuitive LQ prediction model can be formed. Christopher J. Lowrance