A shared-autonomy heavy-lift assistive robot for manufacturing Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/kh04dt42q

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  • Automating pick-and-place operations for large steel castings in a foundry is challenging, particularly from a perception point of view. Accurately estimating the identity and orientation of a given part is an open and complex problem, especially if that part sits in a bin of very similar parts. The problem is made worse when the castings are made in small mixed batches, where many parts appear very similar from a variety of viewpoints. In this dissertation, we investigate automating the identification and pose estimation of large steel castings in a realistic foundry setting. We look at both fully-automatic and human-assisted techniques, and compare their effectiveness. We report the results of testing our system with foundry workers, wearing full personal protective equipment, in their everyday work environment. By using human guidance in our system we were able to raise part recognition rates from 40% to 98%, and were able to estimate the pose of parts to within a few degrees 86% of the time.
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