Recent research:
The collection presents a cohesive exploration of intelligent automation, education technology, and autonomous robotics aimed at enhancing industrial and learning environments. The work on Smith Predictor-based control addresses the challenge of large time delays in temperature plants, demonstrating how anticipatory control can improve response times and stability when implemented on embedded hardware. In the robotics/Industry 4.0 project, an omnidirectional line-following robot using AI-driven vision and deep learning demonstrates real-time obstacle detection and evasive navigation with substantial effectiveness, illustrating how digital twins, IoT, and AI can transform manufacturing workflows in emerging markets. The education technology study integrates immersive virtual reality (VR) to support English language learning, validating that VR tools can be accepted by experts and students as engaging complements to traditional pedagogy, thereby increasing motivation and perceived usefulness. Taken together, these studies highlight methodological pluralism—control theory, autonomous robotic perception, and immersive learning technologies—and emphasize impact through practical deployment, experimental validation, and positive user acceptance in both industrial and educational settings.