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

Ghalamzan E., Amir and Bascetta, Luca and 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

Full content URL: https://doi.org/10.1109/ICRA.2015.7139467

Documents
Estimating a mean-path from a set of 2-d curves
author's accepted manuscript
[img]
[Download]
[img] PDF
ICRA15_3368_FI.pdf - Whole Document

2MB
Item Type:Article
Item Status:Live Archive

Abstract

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
Related URLs:
ID Code:34769
Deposited On:07 Mar 2019 16:09

Repository Staff Only: item control page