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Reference cage architecture for autonomous docking of mobile robots in automotive production systems

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Abstract

This paper addresses the need for high-accuracy docking in material handling processes using autonomous mobile robots in factories. To satisfy this need for the tasks, such as loading, unloading, and reaching the charging station, traditional navigation methods often rely on physical restraints at the docking stations, which limits flexibility in production lines. To achieve high-level accuracy without such restrictions, this study proposes the reference cage architecture, which utilizes multi-reference points to maintain scan-matching-based localization performance during docking. The contributions of this research include achieving sub-centimeter accuracy in pose estimation near the target pose and the development of a real-time reference selection decision mechanism. To verify the effectiveness of the proposed approach, extensive testing and validation have been conducted on the automotive production lines of the Ford Otosan Golcuk Plant. These tests consider real-world operational conditions, such as noises, disturbances, and outliers, setting this study apart from similar publications in the literature. The results demonstrate the potential of the reference cage architecture in enabling high-accuracy docking in autonomous mobile robot applications within factory environments.

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Availability of data and materials

The authors declare that the data supporting the findings are available online at https://osf.io/ke4n9/ on The Open Science Framework (OSF).

Code Availability

The binary packages used in this research are available as ROS repositories at https://packages.ros.org/ros/ubuntu/. The rest of the code and scripts are reproducible as discussed in this article.

Notes

  1. https://www.fordotosan.com.tr/en/operations/production/plants

  2. https://github.com/yilmazabdurrah/synthetic_laser_measurement_MATLAB

  3. http://wiki.ros.org/vrpn_client_ros

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Acknowledgements

We express our gratitude for the ongoing support provided by the Ford Otosan Light Mobility Laboratory at the Gölcük R &D Center and the Robotics Laboratory of the Control and Automation Engineering Department at Istanbul Technical University. Moreover, we present our appreciation to ChatGPT for its support in enhancing the grammar and proofreading of this manuscript.

Funding

This research was financially supported by the Gölcük R &D Center at Ford Otosan.

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Authors and Affiliations

  1. Control and Automation Engineering Department, Istanbul Technical University, Ayazaga Campus, 34469, Istanbul, Turkey

    Abdurrahman Yilmaz & Hakan Temeltas

  2. Sancaktepe R& D Center, Ford Otomotiv Sanayi Inc, 34885, Istanbul, Turkey

    Aycan Deniz Vit, Ismail Hakki Savci & Hakan Ocakli

Authors
  1. Abdurrahman Yilmaz
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  2. Aycan Deniz Vit
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  3. Ismail Hakki Savci
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  4. Hakan Ocakli
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  5. Hakan Temeltas
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Contributions

All authors made significant contributions to the structure and design of the manuscript. Abdurrahman Yilmaz developed and implemented the reference cage method. Aycan Deniz Vit and Hakan Ocakli were responsible for material preparation, field tests, data collection, and analysis. Ismail Hakki Savci and Hakan Temeltas provided guidance throughout the project. The initial draft of the manuscript was written by Abdurrahman Yilmaz and Aycan Deniz Vit, with input and feedback from all authors on earlier versions. The final manuscript was reviewed and approved by all authors.

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Correspondence to Abdurrahman Yilmaz.

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Appendix A: Ground truth reference system for field tests

Appendix A: Ground truth reference system for field tests

Field tests were performed in a 5 m \(\times \) 5 m \(\times \) 3 m volume, covered by OptiTrack motion capture (MoCap) system with four OptiTrack Prime 17W cameras providing the 6D pose of the AMR at 120 Hz. Thanks to this optical camera system, the robot’s position can be tracked with sub-millimeter precision at high frequency. The estimated pose data by the MoCap system is transferred to the ROS environment employing the ROS Virtual Reality Peripheral Network (VRPN) client node.Footnote 3 The overall system architecture is demonstrated in Appendix Fig. 15.

Fig. 15
figure 15

Ground truth reference data generation system overview—Optitrack MoCap system and ROS integration

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Yilmaz, A., Vit, A.D., Savci, I.H. et al. Reference cage architecture for autonomous docking of mobile robots in automotive production systems. Int J Adv Manuf Technol 129, 3497–3511 (2023). https://doi.org/10.1007/s00170-023-12456-0

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