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.
Authors
- Rubén Ferrero-Guillén (ORCID: https://orcid.org/0000-0002-8360-0604)
- Javier Díez-González (ORCID: https://orcid.org/0000-0002-6566-1630)
- Alberto Martínez-Gutiérrez (ORCID: https://orcid.org/0000-0002-4079-9457)
- Iván Sánchez-Calleja (ORCID: https://orcid.org/0009-0009-5321-230X)
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
- 0.00