Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/148862
Title: What are the important technologies for bin picking? Technology analysis of robots in competitions based on a set of performance metrics
Authors: Fujita, M.
Domae, Yukiyasu
Noda, Akio
Garcia Ricardez, Gustavo Alfonso
Nagatani, Tatsuya
Zeng, Andy
Song, Shuran
Rodriguez, Alberto
Causo, Albert
Chen, I-Ming
Ogasawara, Tsukasa
Keywords: Engineering::Mechanical engineering
Issue Date: 2020
Source: Fujita, M., Domae, Y., Noda, A., Garcia Ricardez, G. A., Nagatani, T., Zeng, A., Song, S., Rodriguez, A., Causo, A., Chen, I. & Ogasawara, T. (2020). What are the important technologies for bin picking? Technology analysis of robots in competitions based on a set of performance metrics. Advanced Robotics, 34(7-8), 560-574. https://dx.doi.org/10.1080/01691864.2019.1698463
Journal: Advanced Robotics
Abstract: Bin picking is still a challenge in robotics, as patent in recent robot competitions. These competitions are an excellent platform for technology comparisons since some participants may use state-of-the-art technologies, while others may use conventional ones. Nevertheless, even though points are awarded or subtracted based on the performance in the frame of the competition rules, the final score does not directly reflect the suitability of the technology. Therefore, it is difficult to understand which technologies and their combination are optimal for various real-world problems. In this paper, we propose a set of performance metrics selected in terms of actual field use as a solution to clarify the important technologies in bin picking. Moreover, we use the selected metrics to compare our four original robot systems, which achieved the best performance in the Stow task of the Amazon Robotics Challenge 2017. Based on this comparison, we discuss which technologies are ideal for practical use in bin picking robots in the fields of factory and warehouse automation.
URI: https://hdl.handle.net/10356/148862
ISSN: 0169-1864
DOI: 10.1080/01691864.2019.1698463
Rights: © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/ 4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

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