Estimating a mean-path from a set of 2-d curves

Ghalamzan E., Amir, Bascetta, Luca, Restelli, Marcello and Rocco, Paolo (2015) Estimating a mean-path from a set of 2-d curves. 2015 IEEE International Conference on Robotics and Automation (ICRA) . pp. 2048-2053. ISSN 1050-4729

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Estimating a mean-path from a set of 2-d curves
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To perform many common industrial robotic tasks, e.g. deburring a work-piece, in small and medium size companies where a model of the work-piece may not be available, building a geometrical model of how to perform the task from a data set of human demonstrations is highly demanded. In many cases, however, the human demonstrations may be sub-optimal and noisy solutions to the problem of performing a task. For example, an expert may not completely remove the burrs that result in deburring residuals on the work-piece. Hence, we present an iterative algorithm to estimate a noise-free geometrical model of a work-piece from a given dataset of profiles with deburring residuals. In a case study, we compare the profiles obtained with the proposed method, nonlinear principal component analysis and Gaussian mixture model/Gaussian mixture regression. The comparison illustrates the effectiveness of the proposed method, in terms of accuracy, to compute a noise-free profile model of a task.

Keywords:robotic deburring, robot learning from demonstration
Subjects:H Engineering > H671 Robotics
Divisions:College of Science
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ID Code:34769
Deposited On:07 Mar 2019 16:09

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