Autonomous Drones II: Robotic, Computer Vision, AI

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: