Human-Robot Collaboration in Industrial Automation: Challenges and Future Prospects
Abstract
The rise of Industry 4.0 has led to increased collaboration between humans and robots in manufacturing and industrial automation. This paper investigates the role of AI-driven collaborative robots (cobots) in optimizing production efficiency and workplace safety. Through case studies in China’s leading manufacturing hubs, such as Guangzhou and Suzhou, the study explores how cobots enhance productivity, reduce human error, and create safer working environments. The research also addresses challenges such as human-robot interaction, ethical considerations, and workforce adaptation. The findings offer insights into the future of intelligent automation and its implications for global industries.
References
Antiya, D. (2024). DevOps for Compliance: Building Automated Compliance Pipelines for Cloud Security. Xoffencer international book publication house.
Sudhakar, V. M. (2020). Optimizing Supply Chain Management in Oil and Gas with Machine Learning: A Data-Driven Approach for Cost Reduction and Efficiency.
Mohammed, C. S. A. (2019). Exploring the Features and Scope of SAP S/4HANA for Financial Products Subledger Management. Australian Journal of Cross-Disciplinary Innovation, 1(1).
Mohammed, C. (2021). Revolutionizing Financial Operations: A Comprehensive Study on the Impact of SAP and Kyriba Integration. International Journal of Sustainable Development in Computing Science, 3(2), 1-19.
Daka, M. K., Zhong, J., & Antiya, D. S. (2024, July). Revolutionizing Multiplayer Gaming: A Deep Dive into VisionXO, a 3D Multiplayer Tic-Tac-Toe Game. In World Congress in Computer Science, Computer Engineering & Applied Computing (pp. 242-246). Cham: Springer Nature Switzerland.
Gami, S. J., Shah, K., Katru, C. R., & Nagarajan, S. K. S. (2024). Interactive Data Quality Dashboard: Integrating Real-Time Monitoring with Predictive Analytics for Proactive Data Management.
Sudhakar, V. M. (2022). Advancements in Automl: Designing Scalable Solutions for Enterprise Data Science Platforms.
Mahida, A. (2023). Enhancing Observability in Distributed Systems-A Comprehensive Review. Journal Of Mathematical & Computer Applications. Src/Jmca-166. Doi: Doi. Org/10.47363/Jmca/2023 (2), 135, 2-4.
Mahida, A. (2023). Explainable Generative Models in FinCrime. J Artif Intell Mach Learn & Data Sci, 1(2), 205-208.
Mahida, A. (2024). Integrating Observability with DevOps Practices in Financial Services Technologies: A Study on Enhancing Software Development and Operational Resilience. International Journal of Advanced Computer Science & Applications, 15(7).
Mahida, A. (2022). Comprehensive Review on Optimizing Resource Allocation in Cloud Computing for Cost Efficiency. Journal of Artificial Intelligence & Cloud Computing. SRC/JAICC-249. DOI: doi. org/10.47363/JAICC/2022 (1), 232, 2-4.
Mahida, A. (2023). Machine Learning for Predictive Observability-A Study Paper. Journal of Artificial Intelligence & Cloud Computing. SRC/JAICC-252. DOI: doi. org/10.47363/JAICC/2023 (2), 235, 2-3.
Mahida, A. (2021). A Review on Continuous Integration and Continuous Deployment (CI/CD) for Machine Learning. International journal of science and research, 10(3), 1967-1970.
Mahida, A. (2024). Secure Data Outsourcing Techniques for Cloud Storage. International Journal of Science and Research (IJSR), 13 (4), 181-184.
Mahida, A. (2024). A comprehensive review on generative models for anomaly detection in financial data. Journal of Anomaly Detection Research, 12(2), 45-59.
Mahida, A., Chintale, P., & Deshmukh, H. (2024). Enhancing Fraud Detection in Real Time using DataOps on Elastic Platforms.
Dutta, P. K., Bhardwaj, A. K., & Mahida, A. (2024). Navigating the Complexities of Agile Transformations in Large Organizations. In Quantum Computing and Supply Chain Management: A New Era of Optimization (pp. 315-330). IGI Global.
Mahida, A. (2023). An Automated Disaster Recovery Strategies for Fintech Infrastructure. Journal of Engineering and Applied Sciences Technology. SRC/JEAST-342. DOI: doi. org/10.47363/JEAST/2023 (5), 236, 2-4.
Mahida, A. (2024, December). Impact of Observability on Enhancing Customer Experience in Digital Payment Platforms. In 2024 Eighth International Conference on Parallel, Distributed and Grid Computing (PDGC) (pp. 121-125). IEEE.
Guttha, P. R. (2024). Advancements in Commercial Technology: A Data Technology Engineering Perspective. Australian Journal of Cross-Disciplinary Innovation, 6(6).
Guttha, P. R. (2024). Optimizing Business Growth with Salesforce Sales Cloud: Architecture, Development, and Scalable Delivery. Australian Journal of Cross-Disciplinary Innovation, 6(6).
Guttha, P. R. (2023). Architecting Scalable Business Applications: Design, Development, and Delivery Strategies. Australian Journal of Cross-Disciplinary Innovation, 5(5).
