Integrating Industrial Robots Through a Contact Method

The evolving industrial paradigm demands high quality procedures and automation to adapt to dynamic market conditions. As a consequence, novel robotic systems are integrated within the current digital industrial environments ensuring real-time adaptation to production changes and safe operation with humans and machines. Collaborative robots (cobots) and Autonomous Mobile Robots (AMRs) are designed to follow these principles enhancing the current digital industrial revolution. However, while AMRs are subjected to navigation errors, cobots incur in positioning errors due to the stepper motors operation, resulting in a combined interaction error that do not allow a direct accurate operation between these robots. The addressing of this challenge requires the definition of a robust integration framework between these robots that enables the compensation for the interaction errors occurring during their joint operation. To achieve this challenge, in this paper, we propose a novel Fictitious Points Compensation Methodology (FPCM) based on contacts on the AMR structure and an appropriate error characterization that compensates the interaction errors, proving that these errors are systematic. Practically, we make use of fictitious points that define the final position of the cobot end-effector to minimize the final interaction error. The validity of the proposal is verified through two practical cases showing angular errors up to 0.04°, and placement errors up to 0.13 mm, which validates both the proposal and the error characterization. Thus, our method efficiently addresses the challenge of integrating heterogeneous robotic systems within the current industrial paradigm.

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Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3619845
Primary Topic
Robot Manipulation and Learning
Type
article
Field-Weighted Citation Impact
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article

Integrating Industrial Robots Through a Contact Method

Rubén Ferrero-Guillén, Javier Díez-González, Alberto Martínez-Gutiérrez, Iván Sánchez-Calleja
IEEE Access
Robot Manipulation and Learning
article

Integrating Industrial Robots Through a Contact Method

Rubén Ferrero-Guillén, Javier Díez-González, Alberto Martínez-Gutiérrez, Iván Sánchez-Calleja
article en

Abstract

The evolving industrial paradigm demands high quality procedures and automation to adapt to dynamic market conditions. As a consequence, novel robotic systems are integrated within the current digital industrial environments ensuring real-time adaptation to production changes and safe operation with humans and machines. Collaborative robots (cobots) and Autonomous Mobile Robots (AMRs) are designed to follow these principles enhancing the current digital industrial revolution. However, while AMRs are subjected to navigation errors, cobots incur in positioning errors due to the stepper motors operation, resulting in a combined interaction error that do not allow a direct accurate operation between these robots. The addressing of this challenge requires the definition of a robust integration framework between these robots that enables the compensation for the interaction errors occurring during their joint operation. To achieve this challenge, in this paper, we propose a novel Fictitious Points Compensation Methodology (FPCM) based on contacts on the AMR structure and an appropriate error characterization that compensates the interaction errors, proving that these errors are systematic. Practically, we make use of fictitious points that define the final position of the cobot end-effector to minimize the final interaction error. The validity of the proposal is verified through two practical cases showing angular errors up to 0.04°, and placement errors up to 0.13 mm, which validates both the proposal and the error characterization. Thus, our method efficiently addresses the challenge of integrating heterogeneous robotic systems within the current industrial paradigm.

IEEE AccessVol. 13
Openalex Percentile: Top 65%
Robot Manipulation and Learning
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Integrating Industrial Robots Through a Contact Method — Rubén Ferrero-Guillén, Javier Díez-González, et al. · IEEE Access (2025) | TGRS Research Map | TGRS