The integration of automation and voice control in drone systems has received significant attention in recent research, driven by the need for more intuitive and efficient human-machine interaction [4, 1]. This project focuses on developing a voice command system for the Tello drone, utilizing speech recognition and deep learning models to translate voice commands into precise drone actions. The primary challenge addressed by this project is the accurate and efficient translation of voice commands into specific drone operations. This is particularly crucial in scenarios where traditional control interfaces are impractical or where operators require hands-free operation [10, 5]. To address this challenge, we developed and evaluated three distinct pipelines. The first pipeline uses a traditional Speech-to-Text (STT) model followed by a Large Language Model (LLM) for command interpretation [11]. The second pipeline involves a direct mapping model that predicts drone commands from audio inputs without intermediate text conversion. The third pipeline employs a Siamese neural network to generalize new commands by comparing audio inputs to pre-trained examples [8]. Each pipeline was designed to balance performance, flexibility, and ease of maintenance.

With the rapid development of drone technology, the application of multi-drones is becoming increasingly widespread in various fields. However, the task planning technology for multi-drones still faces challenges such as the complexity of remote operation and the convenience of human-machine interaction. To address these issues, this paper proposes a prompt-driven task planning method for multi-drones based on large language models. By introducing the Prompt technique, appropriate prompt information is provided for the multi-drone system.

The paper describes of the Ryze Tello drone to move autonomously using a basic vision system. The drone's position is determined by identifying AprilTags' position relative to the drone's built-in camera. The accuracy of the drone's position readings and distance calculations was tested under controlled conditions, and errors were analysed. The study showed a decrease in absolute error with decreasing drone distance from the marker, a little change in the relative error for large distances, and a sharp decrease in the relative error for small distances. The method is satisfactory for determining the drone's position relative to a marker.