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.
This paper proposes a vision-based collision avoidance system for unmanned aerial vehicles (UAVs). A method to detect and avoid approaching objects is necessary for UAVs since they are inherently vulnerable to external impacts. To resolve common issues with motion detection on a moving platform, computer vision algorithms such as optical flow and homography transform are utilized. The robustness of these algorithms is improved by employing characteristics of differential images. The proposed method is implemented in a camera-equipped onboard computer and then mounted onto a UAV as a collision avoidance system. It performs evasive maneuvers to avoid various objects thrown in its flight path, demonstrating its functionality and robustness.