Introductions

Real-time systems play a pivotal role in today’s industries, robotics, autonomous driving, and numerous other applications. The processing of real-time control based on sensor data, as well as the handling of complex data such as video and point clouds, has become an increasingly critical requirement. While traditional real-time operating systems and dedicated hardware remain essential, they often face limitations when it comes to applying modern technologies like deep learning, which demands high computational power.

Xenomai stands out as one of the most powerful real-time frameworks designed to address these demands, providing low latency and fast responsiveness that make it an excellent choice for handling complex sensor data processing and control systems. What makes Xenomai unique is its ability to run a real-time kernel in parallel with the Linux kernel, allowing users to leverage the advantages of a real-time operating system while retaining the flexibility of Linux.

This book aims to provide a comprehensive guide on building real-time systems with Xenomai. In an environment where traditional filter-based techniques often struggle with parameter tuning and divergence issues, Xenomai offers a reliable alternative for real-time control systems. Additionally, we will explore how to apply modern data processing techniques, including deep learning, in real-time systems.

From the basics of Xenomai to its practical applications, this book delivers a structured guide to maximizing real-time performance. I invite you to explore how Xenomai can break through the limitations of real-time systems and discover its full potential.