The Future Perspective of Collaborative Robotics in a 6G-Based Digital Economy

Authors

  • Azzam al Alim Sekolah Tinggi Teknik Harapan Medan, Sumatra Utara, Indonesia
  • Gesita Fadya Yuyen Sekolah Tinggi Teknik Harapan Medan, Sumatra Utara, Indonesia
  • Irene Grace Evangelina Sekolah Tinggi Teknik Harapan Medan, Sumatra Utara, Indonesia
  • Kelvin Lie Sekolah Tinggi Teknik Harapan Medan, Sumatra Utara, Indonesia

DOI:

https://doi.org/10.51903/8289x083

Keywords:

6G Technology, Collaborative Robotics, Industrial Automation, Artificial Intelligence, Latency Optimization

Abstract

The rapid advancement of wireless communication technology has significantly influenced industrial automation, particularly in collaborative robotics (cobots). With the expected commercial deployment of 6G by 2030, this technology offers ultra-high-speed data transmission, low latency, and deep integration of Artificial Intelligence (AI). This study investigates the impact of 6G on enhancing the performance and efficiency of cobots in industrial settings, addressing challenges related to real-time coordination, network latency, and economic implications. The results indicate that cobots powered by 6G achieve a 25-30% increase in throughput, a latency reduction to 0.5 - 0.8 ms, and a 15-20% improvement in energy efficiency compared to 5G-based cobots. These enhancements facilitate more precise and adaptive human-robot interactions, improving operational flexibility and productivity across various sectors. However, challenges such as infrastructure readiness, cybersecurity risks, and high implementation costs remain key concerns. This study provides a comprehensive analysis of the synergy between 6G and collaborative robotics, offering valuable insights for industries preparing for next-generation automation.

References

Ahmed, J., Razzaque, M. A., Rahman, M. M., Alqahtani, S. A., & Hassan, M. M. (2022). A Stackelberg Game-Based Dynamic Resource Allocation in Edge Federated 5G Network. IEEE Access, 10, 10460–10471. https://doi.org/10.1109/ACCESS.2022.3144960

Ajayan, A. P., Dash, S. P., & Ramkumar, B. (2021). Performance Analysis of an IRS-Aided Wireless Communication System With Spatially Correlated Channels. IEEE Wireless Communications Letters, 11(3), 563–567. https://doi.org/10.1109/LWC.2021.3136210

Akalin, N., Loutfi, A., Schmitz, A., & Distante, C. (2021). Reinforcement Learning Approaches in Social Robotics. Sensors, 21(4), 1292. https://doi.org/10.3390/s21041292

Almusaed, A., Yitmen, I., & Almssad, A. (2023). Reviewing and Integrating AEC Practices into Industry 6.0: Strategies for Smart and Sustainable Future-Built Environments. Sustainability, 15(18), 13464. https://doi.org/10.3390/su151813464

An, J., Xu, C., Gan, L., & Hanzo, L. (2021). Low-Complexity Channel Estimation and Passive Beamforming for RIS-Assisted MIMO Systems Relying on Discrete Phase Shifts. IEEE Transactions on Communications, 70(2), 1245–1260. https://doi.org/10.1109/TCOMM.2021.3127924

Andronie, M., Lăzăroiu, G., Iatagan, M., Hurloiu, I., Ștefănescu, R., Dijmărescu, A., & Dijmărescu, I. (2023). Big Data Management Algorithms, Deep Learning-Based Object Detection Technologies, and Geospatial Simulation and Sensor Fusion Tools in the Internet of Robotic Things. ISPRS International Journal of Geo-Information, 12(2), 35. https://doi.org/10.3390/ijgi12020035

Andronie, M., Lăzăroiu, G., Karabolevski, O. L., Ștefănescu, R., Hurloiu, I., Dijmărescu, A., & Dijmărescu, I. (2022). Remote Big Data Management Tools, Sensing and Computing Technologies, and Visual Perception and Environment Mapping Algorithms in the Internet of Robotic Things. Electronics, 12(1), 22. https://doi.org/10.3390/electronics12010022

Attaran, M. (2023). The impact of 5G on the evolution of intelligent automation and industry digitization. Journal of Ambient Intelligence and Humanized Computing, 14, 5977–5993. https://doi.org/10.1007/s12652-020-02521-x

Borboni, A., Reddy, K. V. V., Elamvazuthi, I., AL-Quraishi, M. S., Natarajan, E., & Azhar Ali, S. S. (2023). The Expanding Role of Artificial Intelligence in Collaborative Robots for Industrial Applications: A Systematic Review of Recent Works. Machines, 11(1), 111. https://doi.org/10.3390/machines11010111

Chataut, R., Nankya, M., & Akl, R. (2024). 6G Networks and the AI Revolution—Exploring Technologies, Applications, and Emerging Challenges. Sensors, 24(6), 1888. https://doi.org/10.3390/s24061888

Fragapane, G., de Koster, R., Sgarbossa, F., & Strandhagen, J. O. (2021). Planning and control of autonomous mobile robots for intralogistics: Literature review and research agenda. European Journal of Operational Research, 294(2), 405–426. https://doi.org/10.1016/j.ejor.2021.01.019

Hafi, H., Brik, B., Frangoudis, P. A., Ksentini, A., & Bagaa, M. (2024). Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges, and Future Directions. IEEE Access, 12, 9890–9930. https://doi.org/10.1109/ACCESS.2024.3351600

Hassan, M. A., Zardari, S., Farooq, M. U., Alansari, M. M., & Nagro, S. A. (2024). Systematic Analysis of Risks in Industry 5.0 Architecture. Applied Sciences, 14(4), 1466. https://doi.org/10.3390/app14041466

Hua, J., Zeng, L., Li, G., & Ju, Z. (2021). Learning for a Robot: Deep Reinforcement Learning, Imitation Learning, Transfer Learning. Sensors, 21(4), 1278. https://doi.org/10.3390/s21041278

Jahid, A., Alsharif, M. H., & Hall, T. J. (2023). The convergence of blockchain, IoT and 6G: Potential, opportunities, challenges and research roadmap. Journal of Network and Computer Applications, 217, 103677. https://doi.org/10.1016/j.jnca.2023.103677

Khaloopour, L., Su, Y., Raskob, F., Meuser, T., Bless, R., Janzen, L., Abedi, K., Andjelkovic, M., Chaari, H., Chakraborty, P., Kreutzer, M., Hollick, M., Strufe, T., Franchi, N., & Jamali, V. (2024). Resilience-by-Design in 6G Networks: Literature Review and Novel Enabling Concepts. IEEE Access, 12, 155666–155695. https://doi.org/10.1109/ACCESS.2024.3480275

Letaief, K. B., Shi, Y., Lu, J., & Lu, J. (2022). Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications. IEEE Journal on Selected Areas in Communications, 40(1), 5–36. https://doi.org/10.1109/JSAC.2021.3126076

Liu, Y., Yang, Y., Li, H., & Zhong, K. (2022). Digital Economy Development, Industrial Structure Upgrading and Green Total Factor Productivity: Empirical Evidence from China’s Cities. International Journal of Environmental Research and Public Health, 19(4), 2414. https://doi.org/10.3390/ijerph19042414

Merluzzi, M., Borsos, T., Rajatheva, N., Benczur, A. A., Farhadi, H., Yassine, T., Mueck, M. D., Barmpounakis, S., Strinati, E. C., Dampahalage, D., Demestichas, P., Ducange, P., Filippou, M. C., Baltar, L. G., Haraldson, J., Karacay, L., Korpi, D., Lamprousi, V., Marcelloni, F., … Uusitalo, M. A. (2023). The Hexa-X Project Vision on Artificial Intelligence and Machine Learning-Driven Communication and Computation Co-Design for 6G. IEEE Access, 11, 65620–65648. https://doi.org/10.1109/ACCESS.2023.3287939

Mourtzis, D., Angelopoulos, J., & Panopoulos, N. (2021). Smart Manufacturing and Tactile Internet Based on 5G in Industry 4.0: Challenges, Applications and New Trends. Electronics, 10(24), 3175. https://doi.org/10.3390/electronics10243175

Mourtzis, D., Angelopoulos, J., & Panopoulos, N. (2022). A Literature Review of the Challenges and Opportunities of the Transition from Industry 4.0 to Society 5.0. Energies, 15(17), 6276. https://doi.org/10.3390/en15176276

Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., Niyato, D., Dobre, O., & Poor, H. V. (2021). 6G Internet of Things: A Comprehensive Survey. IEEE Internet of Things Journal, 9(1), 359–383. https://doi.org/10.1109/JIOT.2021.3103320

Nguyen, V. L., Lin, P. C., Cheng, B. C., Hwang, R. H., & Lin, Y. D. (2021). Security and Privacy for 6G: A Survey on Prospective Technologies and Challenges. IEEE Communications Surveys and Tutorials, 23(4), 2384–2428. https://doi.org/10.1109/COMST.2021.3108618

Peruzzini, M., Prati, E., & Pelicciari, M. (2024). A framework to design smart manufacturing systems for Industry 5.0 based on the human-automation symbiosis. International Journal of Computer Integrated Manufacturing, 37(10–11), 1426–1443. https://doi.org/10.1080/0951192X.2023.2257634

Rahman, M. M., Khatun, F., Jahan, I., Devnath, R., & Bhuiyan, M. A. A. (2024). Cobotics: The Evolving Roles and Prospects of Next-Generation Collaborative Robots in Industry 5.0. Journal of Robotics, 2024(1), 1–22. https://doi.org/10.1155/2024/2918089

Ramly, A. M., Abdullah, N. F., & Nordin, R. (2021). Cross-Layer Design and Performance Analysis for Ultra-Reliable Factory of the Future Based on 5G Mobile Networks. IEEE Access, 9, 68161–68175. https://doi.org/10.1109/ACCESS.2021.3078165

Riedelbauch, D., Hollerich, N., & Henrich, D. (2023). Benchmarking Teamwork of Humans and Cobots - An Overview of Metrics, Strategies, and Tasks. IEEE Access, 11, 43648–43674. https://doi.org/10.1109/ACCESS.2023.3271602

Roda-Sanchez, L., Olivares, T., Garrido-Hidalgo, C., De La Vara, J. L., & Fernandez-Caballero, A. (2021). Human-robot interaction in Industry 4.0 based on an Internet of Things real-time gesture control system. Integrated Computer-Aided Engineering, 28(2), 159–175. https://doi.org/10.3233/ICA-200637

Sefati, S. S., Haq, A. U., Nidhi, Craciunescu, R., Halunga, S., Mihovska, A., & Fratu, O. (2024). A Comprehensive Survey on Resource Management in 6G Network Based on Internet of Things. IEEE Access, 12, 113741–113784. https://doi.org/10.1109/ACCESS.2024.3444313

Selvaggio, M., Cognetti, M., Nikolaidis, S., Ivaldi, S., & Siciliano, B. (2021). Autonomy in physical human-robot interaction: A brief survey. IEEE Robotics and Automation Letters, 6(4), 7989–7996. https://doi.org/10.1109/LRA.2021.3100603

Sharma, M., Tomar, A., & Hazra, A. (2024). Edge Computing for Industry 5.0: Fundamental, Applications, and Research Challenges. IEEE Internet of Things Journal, 11(11), 19070–19093. https://doi.org/10.1109/JIOT.2024.3359297

Uusitalo, M. A., Rugeland, P., Boldi, M. R., Strinati, E. C., Demestichas, P., Ericson, M., Fettweis, G. P., Filippou, M. C., Gati, A., Hamon, M. H., Hoffmann, M., Latva-Aho, M., Parssinen, A., Richerzhagen, B., Schotten, H., Svensson, T., Wikstrom, G., Wymeersch, H., Ziegler, V., & Zou, Y. (2021). 6G Vision, Value, Use Cases and Technologies from European 6G Flagship Project Hexa-X. IEEE Access, 9, 160004–160020. https://doi.org/10.1109/ACCESS.2021.3130030

Yadav, M., Agarwal, U., Rishiwal, V., Tanwar, S., Kumar, S., Alqahtani, F., & Tolba, A. (2023). Exploring Synergy of Blockchain and 6G Network for Industrial Automation. IEEE Access, 11, 137163–137187. https://doi.org/10.1109/ACCESS.2023.3338861

Yazdinejad, A., Dehghantanha, A., Karimipour, H., Srivastava, G., & Parizi, R. M. (2024). A Robust Privacy-Preserving Federated Learning Model Against Model Poisoning Attacks. IEEE Transactions on Information Forensics and Security, 19, 6693–6708. https://doi.org/10.1109/TIFS.2024.3420126

Zhang, W., Zhao, S., Wan, X., & Yao, Y. (2021). Study on the effect of digital economy on high-quality economic development in China. PLoS ONE, 16(9), e0257365. https://doi.org/10.1371/journal.pone.0257365

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Published

2025-05-31