Development of mid-air autonomous aerial docking system using onboard machine learning computations

Unmanned aerial systems (UAS) with embedded machine learning applications are applied in various fields for autonomous aerial refueling (AAR), concept of parent-child UAV system, drone swarm, teaming of manned aircraft and UAV, package delivery, etc. The fundamental challenge of an air-to-air docking phase is securing between a leader and a follower aerial vehicles with effective target detection strategy. This paper proposes an autonomous docking system for unmanned aerial vehicle (UAV) system that detects, tracks, and docks to a drogue. The proposed system is operated on an onboard machine learning computer platform. This paper presents not only the design of a probe-and-drogue type of docking system based on bi-stable mechanism, but also the development of an onboard machine learning system for a simple and a robust mid-air docking. ARM-based computer, Jetson Xavier NX module, is used as a companion computer to perform a real-time detection and an autonomous control for the aerial vehicle. To employ an effective drogue detection, a deep learning convolutional neural network (CNN) based real-time object detection algorithm, YOLOv4 tiny, is applied. Furthermore, a point-cloud based tracking algorithm with a RGB-D camera system is developed to track the drogue movement in the air. Before conducting an outfield docking test, a performance of the proposed docking system is validated.