Gaze tracking : seeking critical information for autonomous excavation
Author(s)
Shiozawa, Kaymie.
Download1130062405-MIT.pdf (3.042Mb)
Alternative title
Seeking critical information for autonomous excavation
Other Contributors
Massachusetts Institute of Technology. Department of Mechanical Engineering.
Advisor
Harry H. Asada.
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Show full item recordAbstract
Automating excavation in mining and construction applications is crucial today as the supply of skilled operators cannot match market demand. To efficiently make control decisions for autonomous excavators without having to take in all visual inputs from a typical operator's field of view, gaze tracking is employed in solely extracting key visual information that skilled operators use in the field. Both a front facing camera depicting the world view of the subject and two eye facing cameras that track the subject's pupil movement are worn by a subject to identify regions and features that are of high interest to operators during a digging task. Key features, such as the interface between the soil and the bucket, are characterized using U-Net, a Convolutional Neural Network designed for image segmentation. Through this study, key regions, the inside of the bucket and the opening of the bucket, as well as key features, the soil-bucket interface, were identified to be of high interest to subjects. This information can serve to identify only the necessary visual inputs in the control decision process, thus shortening computation time.
Description
Thesis: S.B., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2019 Cataloged from PDF version of thesis. Includes bibliographical references (pages 37-38).
Date issued
2019Department
Massachusetts Institute of Technology. Department of Mechanical EngineeringPublisher
Massachusetts Institute of Technology
Keywords
Mechanical Engineering.