Autonomous Landing Scheme of VTOL UAV on Moving Ship Using Deep Learning Technique Embedded in Companion Computer

T. Trong, Manh Vu Van, Quan-Tran Hai, B. N. Thai
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引用次数: 1

Abstract

We propose an autonomous landing scheme for Vertical Take-off and Landing Unmanned Aerial Vehicle (VTOL UAV) on a moving ship at sea and this scheme is embedded in a hardware platform - Companion Computer. This mission requires determining the ship’s location, speed, and trajectory, which are significant challenges in the marine environment. This research applies a non-contact method, it is combined deep-learning and visual servoing techniques for real-time measuring of the parameters just mentioned above and tightly coupling with modern navigation logic to ensure the UAV follows a fast and optimal landing trajectory. No prior information about a moving ship’s location and landing pad is needed during the entire VTOL UAV’s landing process. The method aims to improve the performance of the landing. The proposed technique has been evaluated in a hardware in the loop simulation system using Jetson Nano and X-Plane.
基于伴机深度学习技术的垂直起降无人机在移动船舶上的自主降落方案
提出了一种垂直起降无人机(VTOL UAV)在海上移动船舶上的自主降落方案,并将该方案嵌入到硬件平台——同伴计算机中。这项任务需要确定船舶的位置、速度和轨迹,这在海洋环境中是一个重大挑战。本研究采用非接触式方法,结合深度学习和视觉伺服技术对上述参数进行实时测量,并与现代导航逻辑紧密耦合,确保无人机遵循快速、最优的着陆轨迹。在整个垂直起降(VTOL)无人机的着陆过程中,不需要关于移动舰船位置和着陆垫的先验信息。该方法旨在提高着陆性能。该技术已在Jetson Nano和X-Plane硬件在环仿真系统中进行了评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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