Loading...
V. Mosherani is an Assistant Professor in the Department of Electronics and Communication Engineering at Hyderabad Institute of Technology and Management, Hyderabad, Telangana. The contribution focuses on deep reinforcement learning and industrial robotics applications.
Deep Reinforcement Learning (DRL) has surfaced as a formidable Artificial Intelligence methodology for creating intelligent, adaptable, and autonomous industrial robotic systems. In contrast to traditional robotic control techniques that depend mostly on established rules and mathematical frameworks, Deep Reinforcement Learning (DRL) allows robots to acquire optimal behaviours by ongoing engagement with their surroundings.The section additionally examines simulation-oriented training, the transfer of simulations to real-world applications, digital twins, edge artificial intelligence, multi-agent reinforcement learning, and secure reinforcement learning. Ultimately, issues pertaining to training efficacy, incentive structuring, safety considerations, computational intricacy, practical implementation, and generalisation are examined alongside prospective research trajectories.
Digital Twin technology has become a crucial element of contemporary intelligent engineering systems by generating dynamic virtual models of tangible assets, processes, and environments. A Digital Twin perpetually amalgamates data from sensors, Internet of Things (IoT) devices, industrial apparatus, communication networks, and computational models to oversee, evaluate, simulate, forecast, and enhance the performance of physical systems. The amalgamation of Artificial Intelligence (AI), Machine Learning (ML), cloud computing, edge computing, big data analytics, and sophisticated simulation methodologies empowers Digital Twins to facilitate astute decision-making across several engineering sectors. This chapter elucidates the essential principles, framework, facilitating technologies, and applications of Digital Twin systems in intelligent engineering.Ultimately, issues concerning data interoperability, cybersecurity, scalability, model precision, synchronisation, computational intricacy, and standardisation are examined, alongside prospective study avenues for intelligent and sustainable smart engineering systems.