Study on robust aerial docking mechanism with deep learning based drogue detection and docking. (1st June 2021)

• Record Type: Journal Article
• Authors: Choi, Andrew Jaeyong; Yang, Hyeon-Ho; Han, Jae-Hung
• Is Part Of: Mechanical systems and signal processing. Volume 154(2021)
• Publication Date: 2021-06-01
• Subjects: Aerial docking -- Probe-and-drogue system -- Bistable mechanism -- Deep learning -- Real-time detection -- YOLOv3; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2020.107579
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A robust aerial docking system with deep learning based computer vision is developed. The modeling of docking system based on a bi - stable mechanism is derived. The performance of developed docking system is experimentally validated. Deep learning based single stage real-time object detector is applied. Point cloud based real-time 3D coordinates extractor is utilized. Abstract: This paper proposes a simple and a robust bistable docking system with a deep learning based real-time drogue detection and tracking system for Unmanned Aircraft Systems (UAS) for mid-air autonomous aerial docking. Secure aerial docking mechanisms between the leader and follower aerial vehicles with effective drogue detection and tracking strategies are fundamental challenges during the air-to-air docking phase of autonomous aerial docking. To confront those issues, this paper not only presents the design of a bistable-based aerial docking mechanism, but also proposes effective deep learning based real-time drogue detection using a convolutional neural network (CNN) and real-time tracking algorithm using a point cloud algorithm. To ensure novelty and robustness for the aerial docking mechanism, a foldable bistable gripper-type mechanism is designed to increase the grasping performance with simplicity and adaptability. The proposed gripper acts as a drogue by itself to grasp a probe which is attached to the follower aerial vehicle. To employ an effective drogue detection method, the deep learning based real-time object detection algorithm, YOLOv3, is used to implement the drogue detection system. The proposed new probe-and-drogue type bistable docking system has the advantages of being simple and robust. The deep learning based real-time drogue detection method increases the detection rate. Moreover, the real-time tracking algorithm with a depth camera system does not require a GPS/INS system and many other sensors to follow the drogue movement in the air.