Keeping it Local, Tiny and Real: Automated Report Generation on Edge Computing Devices for Mechatronic-Based Cognitive Systems

Recent advancements in deep learning are significantly transforming mechatronic systems and robotics by enhancing their ability to interact effectively with dynamic environments. This progress is particularly crucial for applications such as autonomous driving and service robotics, where the evaluation of large volumes of diverse data is necessary. The integration of automated report generation on edge computing devices supports these mechatronic-based cognitive systems by enabling localized, real-time data processing. Such localized processing reduces reliance on centralized data centers, potentially improving response times and system reliability. These developments underscore the growing importance of edge computing in managing complex, data-intensive tasks within critical robotic applications. As deep learning continues to evolve, its role in advancing autonomous and service robotics through enhanced cognitive capabilities becomes increasingly evident. This trend aligns with broader movements in artificial intelligence research focused on deploying intelligent systems closer to the data source.