## 1 Introduction  
Drones have become essential for multiple fields, including disaster response, industrial inspection, and autonomous delivery systems [1, 2, 3]. Their versatility has led to widespread adoption in both commercial and research applications, but traditional drone operation requires precise manual control, often demanding significant training and experience [4]. This challenge is particularly evident in cluttered or dynamic environments where human pilots must react quickly to avoid obstacles [5]. To enhance accessibility and usability, researchers have explored alternative control methods such as gesture-based interfaces, brain-computer interaction, and voice-controlled navigation [6, 7]. While these technologies have been studied independently, integrating them with other features into a seamless, user-friendly system remains an ongoing challenge. Gesture-based control offers a natural alternative to conventional remote control systems. It leverages computer vision techniques to interpret human hand movements [8]. Specifically, Google's MediaPipe (Version 0.10.21) Hands software library can detect 21 landmarks of the human hand from static images and track these landmarks in videos, utilizing a machine learning model [9]. These landmark positions distinguish between different gestures [10]. Prior research has demonstrated the effectiveness of gesture-based interfaces in robotic control, virtual reality, assistive technology, and human-robot interaction [11; 12; 13]. However, their application in drone piloting--particularly when combined with autonomous safety mechanisms--remains underexplored. Some studies have attempted to integrate gesture recognition with drone control, but they often suffer from high latency, gesture misclassification with differing hand sizes, lighting issues, or limited adaptability in dynamic environments [14]. Autonomous obstacle avoidance, another key component of drone navigation, has been successfully implemented using infrared time-of-flight sensors, LiDAR, and deep-learning-based object detection [15; 16]. These systems enable drones to detect and avoid obstacles, significantly reducing the risk of collisions in unknown environments [17]. However, many autonomous systems override user control entirely when avoiding obstacles, reducing pilot interaction and flexibility [18]. This raises important questions about how to balance human input with autonomous safety mechanisms to provide a smooth and responsive user experience. This study proposes a multifunctional drone control system that integrates hand gesture recognition, autonomous obstacle avoidance, and 3D mapping using Bitcraze's Crazyflie drone to create an intuitive and beginner-friendly piloting experience [19]. The system ensures safe and interactive flight, allowing users to control drones with hand gestures. At the same time, an autonomous safety feature prevents collisions by detecting obstacles within a safety threshold \(\rho_{th}\) and retreating the drone by a fixed avoidance distance, \(\rho_{avoid}\). A 3D mapping system visualizes the flight path and detected obstacles, and an AI-Deck streams live video, enabling navigation beyond direct line-of-sight. Unlike prior research, which often focuses on one aspect of drone control, this system bridges the gap between human interaction and autonomous safety, allowing for greater user engagement while maintaining obstacle avoidance [20; 21]. By integrating gesture-based control with autonomous safety mechanisms and 3D mapping, this research advances human-drone interaction in a novel and practical way. Unlike many existing systems that treat gesture recognition and obstacle avoidance as separate functionalities, this work combines them to create a cohesive, intuitive user experience. This multifaceted integration directly addresses challenges in drone accessibility, offering a scalable and user-friendly solution for applications in education, training, and real-world navigation.