Abstract
The advent of smart manufacturing (SM) has led to the creation of collaborative environments with cyber-physical systems (CPS) that generate added value. However, the performance of combined industrial operations between mobile CPS such as autonomous mobile robots (AMRs) and collaborative robots (cobots) is hampered by the high uncertainty between their relative spatial locations and the existence of heterogeneous communication protocols that create a barrier to their integration into production processes. For this reason, a novel contact system method (CSM) is proposed to determine the position of the AMR without the need for any additional hardware making use of an architecture that facilitates efficient communication between AMRs and cobots. For this purpose, a mathematical model has been defined to characterize the position of a spatial object with six degrees of freedom in order to calculate the deviation between the AMR and the cobot base. The proposed method has also been evaluated by quantifying the position and orientation error before and after applying the CSM. The effectiveness of the CSM method has been assessed in a real application case based on the feasibility of performing an assembly operation between a bearing and different shafts. The results show a significant improvement of 96.2% in positional accuracy and 85.4% in orientation compared to AMR accuracy. In addition, a 92.5% success rate was achieved in the assembly operation analyzed between a bearing and a shaft of the same diameter. Furthermore, the proposed architecture has enabled the coordination between the cobot and the AMR by automating the processes. Therefore, this work contributes to the field of SM by proposing a practical solution to the challenges of generating added value through the creation of collaborative environments with CPS.
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References
Kusiak, A. (2018). Smart manufacturing. International Journal of Production Research, 56(1–2), 508–517.
Ludbrook, F., Michalikova, K. F., Musova, Z., & Suler, P. (2019). Business models for sustainable innovation in industry 4.0: Smart manufacturing processes, digitalization of production systems, and data-driven decision making. Journal of Self-Governance and Management Economics, 7(3), 21–26.
Lesch, V., Züfle, M., Bauer, A., Iffländer, L., Krupitzer, C., & Kounev, S. (2023). A literature review of IoT and cps—What they are, and what they are not. Journal of Systems and Software, 200, 111631.
Martínez-Gutiérrez, A., Díez-González, J., Verde, P., Ferrero-Guillén, R., & Perez, H. (2023). Hyperconnectivity proposal for smart manufacturing. IEEE Access.
Ghobakhloo, M., & Fathi, M. (2019). Corporate survival in industry 4.0 era: The enabling role of lean-digitized manufacturing. Journal of Manufacturing Technology Management, 31, 1–30.
Jafari, N., Azarian, M., & Yu, H. (2022). Moving from industry 4.0 to industry 5.0: What are the implications for smart logistics? Logistics, 6(2), 26.
Menon, K., Kärkkäinen, H., Wuest, T., & Gupta, J. P. (2019). Industrial internet platforms: A conceptual evaluation from a product lifecycle management perspective. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 233(5), 1390–1401.
Wang, C., Song, L., & Li, S. (2018). The industrial internet platform: Trend and challenges. Strategic Study of Chinese Academy of Engineering, 20(2), 15–19.
Martínez-Gutiérrez, A., Díez-González, J., Ferrero-Guillén, R., Verde, P., Álvarez, R., & Perez, H. (2021). Digital twin for automatic transportation in industry 4.0. Sensors, 21(10), 3344.
Martínez, A., Díez, J., Verde, P., Ferrero, R., Álvarez, R., Perez, H., & Vizán, A. (2021). Digital twin for the integration of the automatic transport and manufacturing processes. In IOP conference series: Materials science and engineering, Vol. 1193 (p. 012107). IOP Publishing.
Lazaroiu, G., Androniceanu, A., Grecu, I., Grecu, G., & Neguriţă, O. (2022). Artificial intelligence-based decision-making algorithms, internet of things sensing networks, and sustainable cyber-physical management systems in big data-driven cognitive manufacturing. Oeconomia Copernicana, 13(4), 1047–1080.
Valaskova, K., Nagy, M., Zabojnik, S., & Lăzăroiu, G. (2022). Industry 4.0 wireless networks and cyber-physical smart manufacturing systems as accelerators of value-added growth in slovak exports. Mathematics, 10(14), 2452.
Nica, E., Stan, C. I., Lutan, A. G., & Oasa, R.-S. (2021). Internet of things-based real-time production logistics, sustainable industrial value creation, and artificial intelligence-driven big data analytics in cyber-physical smart manufacturing systems. Economics, Management, and Financial Markets, 16(1), 52–63.
Zeb, S., Mahmood, A., Hassan, S. A., Piran, M. J., Gidlund, M., & Guizani, M. (2022). Industrial digital twins at the nexus of nextG wireless networks and computational intelligence: A survey. Journal of Network and Computer Applications, 200, 103309.
Panigrahi, P. K., & Bisoy, S. K. (2022). Localization strategies for autonomous mobile robots: A review. Journal of King Saud University-Computer and Information Sciences, 34(8), 6019–6039.
Martínez-Gutiérrez, A., Díez-González, J., Verde, P., & Perez, H. (2023). Convergence of virtual reality and digital twin technologies to enhance digital operators’ training in industry 4.0. International Journal of Human–Computer Studies, 180, 103136.
Álvarez, R., Díez-González, J., Alonso, E., Fernández-Robles, L., Castejón-Limas, M., & Perez, H. (2019). Accuracy analysis in sensor networks for asynchronous positioning methods. Sensors, 19(13), 3024.
Diez-Gonzalez, J., Alvarez, R., Prieto-Fernandez, N., & Perez, H. (2020). Local wireless sensor networks positioning reliability under sensor failure. Sensors, 20(5), 1426.
Vicentini, F. (2021). Collaborative robotics: A survey. Journal of Mechanical Design, 143(4), 040802.
Negri, E., Fumagalli, L., & Macchi, M. (2017). A review of the roles of digital twin in CPS-based production systems. Procedia Manufacturing, 11, 939–948.
Zeid, A., Sundaram, S., Moghaddam, M., Kamarthi, S., & Marion, T. (2019). Interoperability in smart manufacturing: Research challenges. Machines, 7(2), 21.
Huang, X., Wang, Z., & Li, L. (2023). Study on the impact of positioning errors on the process performance of robotic bonnet polishing. International Journal of Precision Engineering and Manufacturing, 1–12.
Comari, S., Di Leva, R., Carricato, M., Badini, S., Carapia, A., Collepalumbo, G., Gentili, A., Mazzotti, C., Staglianò, K., & Rea, D. (2022). Mobile cobots for autonomous raw-material feeding of automatic packaging machines. Journal of Manufacturing Systems, 64, 211–224.
Tao, Y., Wu, L., Sidén, J., & Wang, G. (2021). Monte Carlo-based indoor RFID positioning with dual-antenna joint rectification. Electronics, 10(13), 1548.
Arnarson, H., Yu, H., Olavsbråten, M. M., Bremdal, B. A., & Solvang, B. (2023). Towards smart layout design for a reconfigurable manufacturing system. Journal of Manufacturing Systems, 68, 354–367.
Verde, P., Díez-González, J., Álvarez, R., & Perez, H. (2023). Characterization of AGV localization system in industrial scenarios using UWB technology. IEEE Transactions on Instrumentation and Measurement, 72, 1–13.
Martínez-Gutiérrez, A., Díez-González, J., Verde, P., & Perez, H. (2023). Convergence of virtual reality and digital twin technologies to enhance digital operators’ training in industry 4.0. International Journal of Human-Computer Studies, 180, 103136.
Hsu, C.-C., Hwang, P.-J., Wang, W.-Y., Wang, Y.-T., & Lu, C.-K. (2023). Vision-based mobile collaborative robot incorporating a multi-camera localization system. IEEE Sensors Journal, 23, 1–1.
Chiaravalli, D., Palli, G., Monica, R., Aleotti, J., & Rizzini, D. L. (2020). Integration of a multi-camera vision system and admittance control for robotic industrial depalletizing. In 2020 25th IEEE international conference on emerging technologies and factory automation (ETFA), vol. 1 (pp. 667–674).
Mathaba, T. (2021). Multi-objective optimal RFID reader deployment using a leaders and followers algorithm. Computers & Electrical Engineering, 94, 107323.
Ma, Y., Wang, B., Gao, X., & Ning, W. (2019). The gray analysis and machine learning for device-free multitarget localization in passive UHF RFID environments. IEEE Transactions on Industrial Informatics, 16(2), 802–813.
Chen, L.-Y., Vinod, A. K., McMillan, J., Wong, C. W., & Yang, C.-K.K. (2021). A 6\(\mu\)m-precision pulsed-coherent lidar with a 40-db tuning range inverter-based phase-invariant PGA. In 2021 IEEE custom integrated circuits conference (CICC) (pp. 1–2). IEEE.
Zhang, H., Yu, L., & Fei, S. (2022). Design of dual-lidar high precision natural navigation system. IEEE Sensors Journal, 22(7), 7231–7239.
Pereira, F., Freitas, L., Oliveira, R., Vicente, J., Malheiro, T., Gonçalves, A. M., & Machado, J. (2022). Design of a vision system for needles’ beds positioning inspection: An industrial application. In Advances in manufacturing III: Volume 3-quality engineering: research and technology innovations, industry 4.0 (pp. 138–153). Springer.
Javaid, M., Haleem, A., Singh, R. P., Rab, S., & Suman, R. (2022). Exploring impact and features of machine vision for progressive industry 4.0 culture. Sensors International, 3, 100132.
Li, R., Platt, R., Yuan, W., Pas, A., Roscup, N., Srinivasan, M. A., & Adelson, E. (2014). Localization and manipulation of small parts using gelsight tactile sensing. In 2014 IEEE/RSJ international conference on intelligent robots and systems (pp. 3988–3993). IEEE.
Zhao, D., Sun, F., Wang, Z., & Zhou, Q. (2021). A novel accurate positioning method for object pose estimation in robotic manipulation based on vision and tactile sensors. The International Journal of Advanced Manufacturing Technology, 116, 2999–3010.
Chen, G., Chen, W., Yang, Q., Xu, Z., Yang, L., Conradt, J., & Knoll, A. (2020). A novel visible light positioning system with event-based neuromorphic vision sensor. IEEE Sensors Journal, 20(17), 10211–10219.
Díez-González, J., Álvarez, R., González-Bárcena, D., Sánchez-González, L., Castejón-Limas, M., & Perez, H. (2019). Genetic algorithm approach to the 3D node localization in TDOA systems. Sensors, 19(18), 3880.
Li, P., Cai, K., Saputra, M. R. U., Dai, Z., & Lu, C. X. (2022). Odombeyondvision: An indoor multi-modal multi-platform odometry dataset beyond the visible spectrum. In 2022 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 3845–3850). IEEE.
Cui, G., Chu, M., Wangjun, W., & Li, S. (2021). Recognition of indoor glass by 3d lidar. In 2021 5th CAA international conference on vehicular control and intelligence (CVCI) (pp. 1–4). IEEE.
Ko, S.-W., Chae, H., Han, K., Lee, S., Seo, D.-W., & Huang, K. (2021). V2x-based vehicular positioning: Opportunities, challenges, and future directions. IEEE Wireless Communications, 28(2), 144–151.
Hsu, C.-C., Hwang, P.-J., Wang, W.-Y., Wang, Y.-T., & Lu, C.-K. (2023). Vision-based mobile collaborative robot incorporating a multi-camera localization system. IEEE Sensors Journal.
Pagani, R., Nuzzi, C., Ghidelli, M., Borboni, A., Lancini, M., & Legnani, G. (2021). Cobot user frame calibration: Evaluation and comparison between positioning repeatability performances achieved by traditional and vision-based methods. Robotics, 10(1), 45.
D’Souza, F., Costa, J., & Pires, J. N. (2020). Development of a solution for adding a collaborative robot to an industrial AGV. Industrial Robot: The International Journal of Robotics Research and Application, 47(5), 723–735.
Sherwani, F., Asad, M. M., & Ibrahim, B. S. K. K. (2020). Collaborative robots and industrial revolution 4.0 (ir 4.0). In 2020 International conference on emerging trends in smart technologies (ICETST) (pp. 1–5). IEEE.
Olivares-Alarcos, A., Foix, S., Borgo, S., & Alenyà, G. (2022). OCRA—an ontology for collaborative robotics and adaptation. Computers in Industry, 138, 103627.
Chico, A., Cruz, P. J., Vásconez, J. P., Benalcázar, M. E., Álvarez, R., Barona, L., & Valdivieso, Á. L. (2021). Hand gesture recognition and tracking control for a virtual ur5 robot manipulator. In 2021 IEEE fifth ecuador technical chapters meeting (ETCM) (pp. 1–6). IEEE.
Galin, R., & Meshcheryakov, R. (2019). Automation and robotics in the context of industry 4.0: the shift to collaborative robots. In IOP conference series: Materials science and engineering, vol. 537 (p. 032073). IOP Publishing.
Pollák, M., Kočiško, M., Paulišin, D., & Baron, P. (2020). Measurement of unidirectional pose accuracy and repeatability of the collaborative robot ur5. Advances in Mechanical Engineering, 12(12), 1687814020972893.
Jeon, H., Jun, M. B., Yang, S.-H., & Yun, H. (2023). Cost-effective calibration of collaborative robot arm with single wire encoder. International Journal of Precision Engineering and Manufacturing, 1–9.
Reddy, A. C. (2014). Difference between Denavit–Hartenberg (DH) classical and modified conventions for forward kinematics of robots with case study. In International conference on advanced materials and manufacturing technologies (AMMT) (pp. 267–286). JNTUH College of Engineering Hyderabad Chandigarh
Schwaner, K. L., Iturrate, I., Andersen, J. K. H., Dam, C. R., Jensen, P. T., & Savarimuthu, T. R. (2021). Mops: A modular and open platform for surgical robotics research. In 2021 International symposium on medical robotics (ISMR) (pp. 1–8). IEEE.
Ammar, M., Haleem, A., Javaid, M., Walia, R., & Bahl, S. (2021). Improving material quality management and manufacturing organizations system through industry 4.0 technologies. Materials Today: Proceedings, 45, 5089–5096.
Uddin, N., Nugraha, H., Manurung, A., Hermawan, H., & Darajat, T. M. (2022). Kinematics modeling and motions analysis of non-holonomic mobile robot. In 2022 5th international conference on information and communications technology (ICOIACT) (pp. 220–225). IEEE.
Uicker, J. J., Pennock, G. R., Shigley, J. E., & Mccarthy, J. M. (2003). Theory of machines and mechanisms. Oxford University Press.
Lee, H.-J., & Kim, J.-Y. (2021). Balance control strategy of biped walking robot SUBO-1 based on force-position hybrid control. International Journal of Precision Engineering and Manufacturing, 22, 161–175.
Acknowledgements
This research has been developed and funded by the project of the Spanish Ministry of Science and Innovation Grant No. PID2019-108277GB-C21/AEI/10.13039/501100011033 and the open access program supported by the University of León.
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Sánchez-Calleja, I., Martínez-Gutiérrez, A., Ferrero-Guillén, R. et al. Contact System Method for the Precise Interaction Between Cobots and Mobile Robots in Smart Manufacturing. Int. J. Precis. Eng. Manuf. 25, 303–318 (2024). https://doi.org/10.1007/s12541-023-00907-3
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DOI: https://doi.org/10.1007/s12541-023-00907-3