Modeling the Effect of External Actions on the Process of Automated Landing of a Quadcopter UAV on a Moving Platform Using Technical Vision

Abstract

This article describes a series of experiments in the Gazebo simulation environment aimed at studying the effect of external weather conditions on the automatic landing of an unmanned aerial vehicle (UAV) on a moving platform using computer vision and a previously developed control system based on PID and polynomial controllers. As part of the research, methods for modeling external weather conditions are developed and landing tests are carried out simulating weather conditions such as wind, light conditions, fog, and precipitation, as well as their combinations. All the experiments show a successful platform landing. During the experiments, the landing time and its accuracy are measured. The graphical and statistical analysis of the obtained results reveals the effect of light conditions, precipitation, and wind on the UAV landing time, and the introduction of wind in the simulation under any other external conditions leads to the most significant increase in the landing time. At the same time, the study fails to identify the systemic negative effect of external conditions on the accuracy of the landing. The results obtained provide valuable information for further improvement of autonomous automatic landing systems for UAVs without the use of satellite navigation systems.