Modular Framework for Autonomous Waypoint Following and Landing Based on Behavior Trees
Miguel Gil-Castilla, Raúl Tapia, Iván Maza, Anı́bal Ollero · 2024
Autonomous navigation and landing are critical capabilities for modern unmanned aerial vehicles (UAVs), particularly in complex and dynamic environments. In this paper, we propose a modular framework that employs behavior trees to achieve reliable waypoint following and landing. Behavior trees offer a flexible and easily extensible method of managing autonomous behaviors, enabling the integration of various algorithms and sensors. Our framework is designed to enhance the robustness and flexibility of UAV navigation and landing procedures. Experimental results from Software In The Loop (SITL) simulation tests demonstrate the system's robustness and adaptability, showcasing its potential for a wide range of UAV applications. This work contributes to the advancement of autonomous UAV technology by providing a scalable and efficient solution for mission-critical operations.