Automated Aerial Docking System Using Onboard Vision-Based Deep Learning

This paper proposes an automated aerial docking system for unmanned aerial vehicles. The proposed automated aerial docking system consists of the following two subsystems: 1) docking mechanical system and 2) vision-based deep learning detection/tracking system. A fundamental challenge during the midair docking phase is integration between the leader and follower vehicles with a robust target detection/tracking strategy. This paper presents not only the design of a probe-and-drogue-type aerial docking system, but it also presents the development of an onboard machine learning system for aerial target detection and tracking, which is limited from 0.1 to 3.5 m. The aerial docking system is designed based on a bistable mechanism to increase the robustness of integration and separation between two vehicles. The prototype of the proposed docking mechanism is built by a 3D printer. To employ effective drogue detection in the air, a deep convolutional neural network-based single-stage detector algorithm, YOLOv4-tiny, is applied. Furthermore, to track the moving drogue in the air simultaneously, a point cloud-based tracking algorithm with an RGB-D camera system is developed. The developed deep learning-based detection/tracking system is implemented to be operated in the Arm architecture-based onboard machine learning computer. For performance validation of the proposed automated aerial docking system, a ground test using two robot arms and an indoor flight test using quadcopter drone and an unmanned ground vehicle were conducted.