Browsing by Author "Singh, K. Ranjith"

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    AI-Powered collaborative robotics for next-gen industrial automation
    (IEEE, 2026-02-20) Ramya, M; Subburam, S.; Patel, Pushplata; Seetaram, J.; Singh, K. Ranjith; Ali, Guma; Salami, Zayd Ajzan
    The runaway development of Industry 4.0 has brought very interconnected, intelligent, and automated production systems in place, but conventional robotics based on rules cannot react to dynamic production conditions, unpredictable working processes, and real-time disruptions, which tend to result in lower efficiency, time delays, and bottlenecks in the work process. Among common challenges faced by factories are inefficiencies in task allocation, the absence of collaborative coordination among robots, poor predictive maintenance, and vulnerabilities in data exchange during interactions. To address those issues, this research proposes AI-Powered Multi-Agent Collaborative Robotic System (AI-MACROS), a highly sophisticated industrial automation system integrating swarm intelligence, reinforcement learning, digital twin simulation, and blockchain-based security. AI-MACROS allows teams of robotic agents to work together, learn dynamically based on environmental interactions, optimize task distribution and deal with unexpected events in the real world. Through digital twins, it provides virtual replicas of robots and work processes, with which to monitor the system in real time, preemptive maintenance and simulate scenarios without disrupting the existing processes. Also, blockchain protocols guarantee integrity and safety of communication and data across the industrial network. Compared to a typical rulebased robotics, one may observe that AI-MACROS is executed by the high performance score (i.e. higher efficiency, accuracy, recall, and F1-scores), and the coordination and energy efficiency is additionally simplified with the help of particle swarm optimization, genetic algorithms and deep reinforcement learning. The proposed solution addresses the disadvantages of the traditional automation, making the operations more resilient in addition to improving the security and scale, not mentioning that it is deemed as not only a potential game changer but also as a next-generation smart factory solution. In a clever synergy, predictive analytics and safe decentralized information processing, AI-MACROS creates high-performance prototype of adaptable, fruitful and credible automation of production under realistic production scenarios.