Enhancing Drone Navigation and Control: Gesture-Based Piloting, Obstacle Avoidance, and 3D Trajectory Mapping

Autonomous drone navigation presents challenges for users unfamiliar with manual flight controls, increasing the risk of collisions. This research addresses this issue by developing a multifunctional drone control system that integrates hand-gesture recognition, obstacle avoidance, and 3D mapping to improve accessibility and safety. The system employs Google’s MediaPipe Hands software library, which uses machine learning to track 21 key landmarks of the user’s hand, allowing for gesture-based drone control. Each recognized gesture is mapped to a flight command, eliminating the need for a traditional controller. The obstacle avoidance system, utilizing the Flow Deck V2 and Multi-Ranger Deck, detects objects within 0.35 meters and autonomously moves the drone 0.2 meters away to prevent collisions. A mapping system continuously logs the drone’s flight path and detects obstacles, enabling 3D visualization of drone’s trajectory after the drone landing. Also, an AI-Deck streams live video, enabling navigation beyond the user’s direct line of sight. Experimental validation with the Crazyflie drone demonstrates seamless integration of these systems, providing a beginner-friendly experience where users can fly drones safely without prior expertise. This research enhances human-drone interaction, making drone technology more accessible for education, training, and intuitive navigation.