Abstract
The duration of continuous operation of unmanned aerial vehicles (UAVs) is significantly limited by the resource of their onboard batteries. This article presents a comprehensive system that provides a fully autonomous drone landing and subsequent charging of its battery, designed to eliminate this limitation. The proposed solution includes an intelligent charging station equipped with a protective canopy and a multimodal guidance system, as well as a specially designed quadcopter optimized for close integration with this station. The station's architecture combines a vision system (an annular multi-camera installation with a 360° view and active LED markers), wireless communication channels and a high-performance real-time computing module that offloads the drone's onboard systems by transferring resource-intensive computer vision and control tasks to the station. The system implements a multi-stage landing algorithm: GPS-based global navigation is combined with precise visual guidance using convolutional neural networks and fiducial markers.; Deep reinforcement learning is used to plan the approach trajectory under the canopy; at the final stage, an adaptive PID controller is used for positioning with an accuracy of several centimeters. A magnetic docking mechanism is provided for secure fixation of the drone during charging, and security protocols provide the possibility of emergency interruption of the process if necessary. During the simulation and testing of the prototype, the system successfully performed autonomous landing in various conditions (including at night and in moderate winds), demonstrating high positioning accuracy and stable operation. The results obtained indicate that the developed autonomous landing and charging system can significantly increase the duration of UAV flights, while reducing the need for operator participation.