Autonomous deck landing of a vertical take-off and landing unmanned aerial vehicle based on the tau theory. (January 2023)

• Record Type: Journal Article
• Authors: Wang, Biao; Lin, Haiwei; Tang, Chaoying; Xu, Guili
• Is Part Of: Transactions of the Institute of Measurement and Control. Volume 45:Number 2(2023)
• Page Start: 233
• Page End: 248
• Publication Date: 2023-01
• Subjects: VTOL UAV -- shipboard landing -- optical flow -- tau theory -- unity; Automatic control -- Periodicals; Measuring instruments -- Periodicals; Commande automatique -- Périodiques; Mesure -- Instruments -- Périodiques; 681.2
• DOI: 10.1177/01423312221104424
• Languages: English
• ISSNs: 0142-3312

Abstract

The research on autonomous landing of vertical take-off and landing (VTOL) unmanned aerial vehicles (UAVs) is well established. However, the research on autonomous deck landing using visual methods is relatively not so mature and many of them require the support of ground infrastructures. In order to reduce such dependencies, a ship landing guidance strategy based on on-board vision is studied. Considering the characteristics of the ship landing issue, we propose a three-phase landing scheme and a decision-making method to ensure landing safety is also studied. For improving the traditional two-dimensional (2D) optical-flow method, a three-dimensional (3D) velocity vector estimation method using image spherical optical flow is studied. Furthermore, a guidance law based on the tau theory is employed by only using the visual information of the line of sight to the target. In this phase, a trajectory-tracking controller is applied to generate the velocity commands of the UAV. Finally, the whole algorithm is validated by simulation in different wave conditions developed with Unity3D. Compared with traditional trajectory planning methods, our method does not require complex optimization iterations and can meet both the real-time and accuracy requirements of deck landing. The average tracking error of our method maintains in 0.2 m. Moreover, the whole algorithm runs efficiently at around 30 fps on a Raspberry-Pi 3B+ microcomputer which meets the real-time requirements.