Siber Fiziksel Sistemlerde İç-Mekân Akıllı Lojistik için Adaptif İşletim Modeli

Yıl 2021, Cilt: 9 Sayı: 4, 965 - 980, 04.12.2021

Emrah Dönmez Fatih Okumuş Fatih Kocamaz

https://doi.org/10.36306/konjes.833557

Cited By: 1

Öz

Lojistik operasyonlar, endüstriyel üretim alanlarında ana faaliyetler arasındadır. Günümüzde bu işlemleri gerçekleştirmek için genellikle elektrikli olan ve bir sürücü tarafından manuel olarak çalıştırılan araçlar kullanılmaktadır. Lojistik robotlar bu alanda kullanılabilecek önemli bir alternatiftir ve endüstriyel alanlarda siber fiziksel sistemlerle entegrasyonda kullanımları giderek yaygınlaşmaktadır. Lojistik robotların en büyük avantajı, endüstri 4.0 konseptine uygun olarak tüm sistem için otonom sürüş kabiliyetleri ve optimum parametreleri sağlamasıdır. Bu çalışmada, siber fiziksel sistem altyapısı olan bir ortamda Siber Fiziksel Sistem (SFS) içerisine entegre edilebilen uyarlanabilir bir lojistik robot sistemi geliştirilmiştir. Bu kapsamda konumlandırma, yol planlama, çoklu görev dağılımı, enerji yönetimi, görev önceliklendirme, optimizasyon ve engellerden kaçınma konuları analiz edilerek basit çözümler önerilmektedir. Deneyler sekiz farklı konfigürasyonda gerçekleştirilmiş ve ortalama mesafe ve enerji maliyetleri sırasıyla % 5,1 ve % 6,6 oranında iyileştirilmiştir.

Anahtar Kelimeler

Siber Fiziksel Sistem (CPS), Akıllı Lojistik, Otonom Robotlar, Endüstri 4.0, Robotik Kontrol

ADAPTIVE OPERATION MODEL FOR INTERIOR SMART LOGISTICS IN CYBER PHYSICAL SYSTEMS

Öz

Logistics operations are among the main activities in industrial production areas. Today, vehicles that are usually electric and manually operated by a driver are used to perform these operations. Logistics robots are an important alternative that can be used in this field, and their use in integration with cyber physical systems in industrial fields is increasingly common. The biggest advantage of the logistics robots is that they provide autonomous driving capabilities and optimum parameters for the entire system in accordance with industry 4.0 concept. In this study, an adaptive logistics robot system that can be integrated into the Cyber Physical System (CPS) system in an environment with cyber physical system infrastructure has been developed. In this context, positioning, path planning, multi-task allocation, energy management, task prioritization, optimization and obstacle avoidance issues are analyzed and simple solutions are proposed. The experiments have been carried out in eight different configurations and the average distance and energy costs have been improved by 5.1% and 6.6%, respectively.

Anahtar Kelimeler

Cyber Physical System (CPS), Smart Logistics, Autonomous Robots, Industry 4.0, Robotic Control

Kaynakça

• Afrin, M.; Jin, J.; Rahman, A.; Tian, Y.C.; Kulkarni, 2019, “A. Multi-objective resource allocation for Edge Cloud based robotic workflow in smart factory.”, Future Gener. Comput. Syst. 2019, 97, 119–130, doi:10.1016/j.future.2019.02.062.
• Chowdhury, M.; Maier, M., 2017, “Collaborative Computing for Advanced Tactile Internet Human-to-Robot (H2R) Communications in Integrated FiWi Multirobot Infrastructures.”, IEEE Internet Things J., 4, 2142–2158, doi:10.1109/JIOT.2017.2761599.
• D’Auria, D., & Persia, F., 2017, “A collaborative robotic cyber physical system for surgery applications.”, In Proceedings - 2017 IEEE International Conference on Information Reuse and Integration, IRI 2017. https://doi.org/10.1109/IRI.2017.84
• Donmez, E., & Kocamaz, A. F., 2019, “The eye-out-device multi-camera expansion for mobile robot control.”, 2019 International Conference on Artificial Intelligence and Data Processing Symposium, IDAP 2019. https://doi.org/10.1109/IDAP.2019.8875981
• Dönmez, E., & Kocamaz, A. F., 2019, “Multi Target Task Distribution and Path Planning for Multi-Agents.”, 2018 International Conference on Artificial Intelligence and Data Processing, IDAP 2018. https://doi.org/10.1109/IDAP.2018.8620932
• Dönmez, E., & Kocamaz, A. F., 2019, “Çoklu Hedeflerin Çoklu Robotlara Paylaştırılması İçin Bir Yük Dengeleme Sistemi.” Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. https://doi.org/10.17798/bitlisfen.467757
• Dönmez, E., Kocamaz, A. F., & Dirik, M., 2017, “Bi-RRT path extraction and curve fitting smooth with visual based configuration space mapping.”, IDAP 2017 - International Artificial Intelligence and Data Processing Symposium. https://doi.org/10.1109/IDAP.2017.8090214
• Ernst, R., 2018, “Automated Driving: The Cyber-Physical Perspective”, Computer. https://doi.org/10.1109/MC.2018.3620974
• Iarovyi, S., Mohammed, W. M., Lobov, A., Ferrer, B. R., & Lastra, J. L. M., 2016, “Cyber-Physical Systems for Open-Knowledge-Driven Manufacturing Execution Systems.”, Proceedings of the IEEE. https://doi.org/10.1109/JPROC.2015.2509498
• Krainer, C., & Kirsch, C. M., 2014, “Cyber-physical cloud computing implemented as PaaS.”, Proceedings of the 4th ACM Workshop on Design, Modeling and Evaluation of Cyber Physical Systems, CyPhy 2014. https://doi.org/10.1145/2593458.2593461
• Krueger, V., Chazoule, A., Crosby, M., Lasnier, A., Pedersen, M. R., Rovida, F., … Veiga, G., 2016, “A vertical and cyber-physical integration of cognitive robots in manufacturing.”, Proceedings of the IEEE. https://doi.org/10.1109/JPROC.2016.2521731
• Laux, H., Bytyn, A., Ascheid, G., Schmeink, A., Kurt, G. K., & Dartmann, G., 2018, “Learning-based indoor localization for industrial applications.”, 2018 ACM International Conference on Computing Frontiers, CF 2018 - Proceedings. https://doi.org/10.1145/3203217.3203227
• Lee, B. M., & Yang, H., 2018, “Massive MIMO for Industrial Internet of Things in Cyber-Physical Systems.”, IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2017.2787988
• Lee, C. K. M., Lin, B., Ng, K. K. H., Lv, Y., & Tai, W. C., 2019, “Smart robotic mobile fulfillment system with dynamic conflict-free strategies considering cyber-physical integration.”, Advanced Engineering Informatics. https://doi.org/10.1016/j.aei.2019.100998
• Levshun, D., Chevalier, Y., Kotenko, I., & Chechulin, A., 2020, “Design and verification of a mobile robot based on the integrated model of cyber-Physical systems.”, Simulation Modelling Practice and Theory, 105, 102151. https://doi.org/10.1016/j.simpat.2020.102151
• Li, F., Wan, J., Zhang, P., Li, D., Zhang, D., & Zhou, K., 2016, “Usage-specific semantic integration for cyber-physical robot systems.”, ACM Transactions on Embedded Computing Systems. https://doi.org/10.1145/2873057
• Lu, Y., & Asghar, M. R., 2020, “Semantic communications between distributed cyber-physical systems towards collaborative automation for smart manufacturing.”, Journal of Manufacturing Systems. https://doi.org/10.1016/j.jmsy.2020.05.001
• Okumuş, F., & Fatih, A., 2019, “Exploring the Feasibility of a Multifunctional Software Platform for Cloud Robotics.”, 2018 International Conference on Artificial Intelligence and Data Processing, IDAP 2018. https://doi.org/10.1109/IDAP.2018.8620865
• Okumus, F., & Kocamaz, A. F., 2018, “Comparing Path Planning Algorithms for Multiple Mobile Robots.”, 2018 International Conference on Artificial Intelligence and Data Processing (IDAP) (pp. 1–4). IEEE. https://doi.org/10.1109/IDAP.2018.8620785
• Okumus, F., & Kocamaz, A. F., 2019, “Cloud based indoor navigation for ros-enabled automated guided vehicles.”, 2019 International Conference on Artificial Intelligence and Data Processing Symposium, IDAP 2019. https://doi.org/10.1109/IDAP.2019.8875993
• Okumus, F., Donmez, E., & Kocamaz, A. F., 2020, "A Cloudware Architecture for Collaboration of Multiple AGVs in Indoor Logistics: Case Study in Fabric Manufacturing Enterprises.", Electronics. 9(12). https://doi.org/10.3390/electronics9122023
• Schillinger, P.; Bürger, M.; Dimarogonas, D.V., 2018, “Simultaneous task allocation and planning for temporal logic goals in heterogeneous multi-robot systems.”, Int. J. Robot. Res. 2018, 37, 818–838, doi:10.1177/0278364918774135.
• Schirner, G., Erdogmus, D., Chowdhury, K., & Padir, T., 2013, “The future of human-in-the-loop cyber-physical systems.”, Computer. https://doi.org/10.1109/MC.2013.31
• Semwal, T., Jha, S. S., & Nair, S. B., 2017, “On ordering multi-robot task executions within a cyber physical system.”, ACM Transactions on Autonomous and Adaptive Systems. https://doi.org/10.1145/3124677
• Sztipanovits, J., Koutsoukos, X., Karsai, G., Kottenstette, N., Antsaklis, P., Gupta, V., … Wang, S., 2012, “Toward a science of cyber-physical system integration.”, Proceedings of the IEEE. https://doi.org/10.1109/JPROC.2011.2161529
• Turner, J.; Meng, Q.; Schaefer, G.; Whitbrook, A.; Soltoggio, A., 2017, “Distributed Task Rescheduling with Time Constraints for the Optimization of Total Task Allocations in a Multirobot System.”, IEEE Trans. Cybern. 2018, 48, 2583–2597, doi:10.1109/TCYB.2017.2743164.
• Wan, J., Tang, S., Shu, Z., Li, D., Wang, S., Imran, M., & Vasilakos, A. V., 2016, “Software-Defined Industrial Internet of Things in the Context of Industry 4.0.”, IEEE Sensors Journal. https://doi.org/10.1109/JSEN.2016.2565621
• Yaacoub, J. P. A., Salman, O., Noura, H. N., Kaaniche, N., Chehab, A., & Malli, M., 2020, “Cyber-physical systems security: Limitations, issues and future trends.”, Microprocessors and Microsystems. https://doi.org/10.1016/j.micpro.2020.103201