You have mastered the fundamentals (Volume 1). Now it is time to take it to the next level: intelligent autonomy.

This book teaches you how to integrate robotics, computer vision, and artificial intelligence into real drones. Perfect for engineers looking to specialise in autonomous perception.

Chapter 1 - ROS2 and Robotic Architecture
- The de facto operating system in professional robotics.
- Nodes, topics, messages - decentralised architecture.
- Ardupilot + ROS2 integration (MAVLink bridge).
- Nav2: 3D autonomous navigation stack.
- Simulation with Gazebo + SITL.

Chapter 2 - Computer Vision and Object Detection
- OpenCV: real-time image processing.
- YOLOv8: ultra-fast detection (45 FPS on GPU).
- Classical methods vs. Deep Learning.
- Integration with ROS2 (image publishers/subscribers).
- Real-world use cases: detection of people, vehicles, points of interest.

Chapter 3 - AI in Drones
- Edge Computing: processing on the drone, not in the cloud.
- Jetson line (Nano Orin): selection based on latency and budget.
- Latency < 100ms: mandatory for autonomous flight.
- TensorRT: 2-3x acceleration of NN models.
- Complete architecture: Jetson + ROS2 + Ardupilot + vision.

Key Features:
- 246 pages of applied content.
- Ready-to-use Python/C++ code.
- 20+ graphs and flow diagrams.
- Compatible with hardware: Jetson Nano, Orin NX, RTX.
- Preparation for research/commercial drones.

Prerequisites:
- Familiarity with Python (Appendix A2).
- Drone concepts (Volume 1).
- Ubuntu 22.04 recommended.

Who is it for?
- Engineers specialising in robotic autonomy.
- Researchers in computer vision.
- AI drone startups.
- Makers who want "intelligent" drones.

From object detection to autonomous decision-making, you will learn the complete stack of intelligent drones.

All examples, practices, exercises and exams are solved and available on GitHub:

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