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Improving Navigation Stack of a ROS-Enabled Industrial Autonomous Mobile Robot (AMR) to be Incorporated in a Large-Scale Automotive Production

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Abstract

Navigation, which is defined as determining the most convenient way to go from one point to another and make the journey on the planned path, is indispensable for developing a fully autonomous system. The navigation stack and its components are a widely studied area in the literature. However, in most of the studies, the developed approaches are tested in laboratory or office environments. Suppose such an approach is intended to be used in a real-world large-scale industrial application. In that case, it must operate to industry standards in a highly dynamic and ever-changing environment and show similar performance on every run or repetitive duty. In this study, for prototypes of industrial autonomous mobile robots (AMRs) equipped with encoders, inertial measurement unit (IMU) and laser rangefinders in order to navigate, an Extended Kalman Filter-based localization approach is implemented using the well-known packages available in the robot operating system (ROS) environment as a part of the navigation stack. Some enhancements are made by showing what kind of challenges may occur in a real factory environment. All developed and/or improved approaches have been tested and validated on the real automotive production lines of the Ford Otosan Kocaeli Plant, considering real working conditions such as real noises, disturbances and obstacles. This is the most important difference that distinguishes this study from similar publications in the literature.

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Availability of data and material

The authors declare that the data supporting the findings are available online at https://osf.io/cvah7/ on The Open Science Framework (OSF).

Code availability

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

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Acknowledgements

We would like to thank Ford Otosan Light Mobility Laboratory in Gölcük R&D Center and Istanbul Technical University Control and Automation Engineering Department Robotics Laboratory for their continuous support.

Funding

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

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Authors and Affiliations

  1. Ford Otosan Kocaeli Plants, Kocaeli, 41650, Turkey

    Ismail Hakki Savci, Sadettin Karaman & Hakan Ocakli

  2. Control and Automation Engineering Dept., Istanbul Technical University, Istanbul, 34469, Turkey

    Abdurrahman Yilmaz & Hakan Temeltas

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  1. Ismail Hakki Savci
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Correspondence to Sadettin Karaman.

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The data collected during tests and evaluations are available through https://doi.org/10.17605/OSF.IO/CVAH7.

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Savci, I.H., Yilmaz, A., Karaman, S. et al. Improving Navigation Stack of a ROS-Enabled Industrial Autonomous Mobile Robot (AMR) to be Incorporated in a Large-Scale Automotive Production. Int J Adv Manuf Technol 120, 3647–3668 (2022). https://doi.org/10.1007/s00170-022-08883-0

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  • DOI: https://doi.org/10.1007/s00170-022-08883-0

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