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 standard 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.

APPENDICES

• APPENDIX A1: GitHub Guide - Project structure, branches and workflow.
• APPENDIX A2: C++ for Robotics - Advanced concepts, optimization and ROS2 libraries.

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.