Overview

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 - 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: https: //github.com/DroneBooks/AutonomousDrones

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