Advanced Drone Navigation and Landing with Multi-Algorithm Integration for Parrot MiniDrone

This paper presents a comprehensive navigation algorithm for autonomous line following and precision landing using a Parrot MiniDrone. The system leverages multiple parallel-running image processing algorithms, each contributing to different aspects of the drone’s navigation. The Dynamic Crown Pursuit Algorithm (DCPA) generates target points to guide the drone along a predefined line, while the Slope-Based Speed Control Algorithm optimizes speed during turns and Off-Track Algorithm corrects deviations to maintain the drone’s position. Additionally, an End Marker Detection algorithm enables precise landing within a designated area. These algorithms are integrated into a Stateflow model that governs the drone’s behavior across various flight stages, including takeoff, line following, alignment, turning, and landing. The landing process is executed in three stages to ensure accuracy and speed. The proposed system was tested in a simulated environment, demonstrating its effectiveness in maintaining accurate line following and executing precise landings. To further validate the system’s overall performance, a comparative analysis with existing methods was conducted, revealing the superior speed and precision of the proposed approach. This makes it well-suited for autonomous drone missions that require high precision and speed in navigation and landing, with potential applications in fields such as surveillance, inspection, and delivery services.