Research Project Title:
Seeking Critical Information for Autonomous Mining and Construction Machines via Operator Gaze Tracking
abstract:Automating excavation in mining and construction applications is crucial today as the supply of skilled operators cannot match market demand. To make control decisions for autonomous excavators, gaze tracking can be employed in solely extracting key information that skilled operators use in the field. A camera that looks into both the eye and the surroundings will be worn by a subject to collect data. Using tools like Convolutional Neural Networks, human attention points will be characterized. This information will serve to identify appropriate inputs in the control decision process.
I am participating in SuperUROP because I would like to gain advanced research skills within robotics to apply to graduate studies. I have UROP’d at the D'Arbeloff Lab for the past year, and I also took 2.671, a class that has prepared me to present my research results effectively. Exposing myself to software aspects of robotics and training neural networks to improve the robot's intelligence and ability to mimic human behavior excite me.