A machine learning approach for collaborative robot smart manufacturing inspection for quality control systems1-10

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A machine learning approach for collaborative robot smart manufacturing inspection for quality control systems1-10

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Brito, Thadeu / Queiroz, Jonas / Piardi, Luis / Fernandes, Lucas A. / Lima, José / Leitão, Paulo

The 4th industrial revolution promotes the automatic inspection of all products towards a zero-defect and high-quality manufacturing. In this context, collaborative robotics, where humans and machines share the same space, comprises a suitable approach that allows combining the accuracy of a robot and the ability and flexibility of a human. This paper describes an innovative approach that uses a collaborative robot to support the smart inspection and corrective actions for quality control systems in the manufacturing process, complemented by an intelligent system that learns and adapts its behavior according to the inspected parts. This intelligent system that implements the reinforcement learning algorithm makes the approach more robust once it can learn and be adapted to the trajectory. In the preliminary experiments, it was used a UR3 robot equipped with a Force-Torque sensor that was trained to perform a path regarding a product quality inspection task. © 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the FAIM 2021. ; This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/05757/2020 ; info:eu-repo/semantics/publishedVersion

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Title :

A machine learning approach for collaborative robot smart manufacturing inspection for quality control systems1-10

Contributors:

Brito, Thadeu (author) / Queiroz, Jonas (author) / Piardi, Luis (author) / Fernandes, Lucas A. (author) / Lima, José (author) / Leitão, Paulo (author)

Publication date :

2020-01-01

DOI :

https://doi.org/10.1016/j.promfg.2020.10.003

Type of media :

Conference paper

Type of material :

Electronic Resource

Language :

English

Keywords :

Actor-critic , Collaborative robots , Human-robot interaction , Quality control systems , Reinforcement learning , Robot learning