Proactive Guidance for Accurate UAV Landing on a Dynamic Platform: A Visual–Inertial Approach

Ching Wei Chang, Li Yu Lo, Hiu Ching Cheung, Yurong Feng, An Shik Yang, Chih Yung Wen, Weifeng Zhou

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

This work aimed to develop an autonomous system for unmanned aerial vehicles (UAVs) to land on moving platforms such as an automobile or a marine vessel, providing a promising solution for a long-endurance flight operation, a large mission coverage range, and a convenient recharging ground station. Unlike most state-of-the-art UAV landing frameworks that rely on UAV onboard computers and sensors, the proposed system fully depends on the computation unit situated on the ground vehicle/marine vessel to serve as a landing guidance system. Such a novel configuration can therefore lighten the burden of the UAV, and the computation power of the ground vehicle/marine vessel can be enhanced. In particular, we exploit a sensor fusion-based algorithm for the guidance system to perform UAV localization, whilst a control method based upon trajectory optimization is integrated. Indoor and outdoor experiments are conducted, and the results show that precise autonomous landing on a 43 cm × 43 cm platform can be performed.

Original languageEnglish
Article number404
JournalSensors
Volume22
Issue number1
DOIs
Publication statusPublished - 5 Jan 2022

Keywords

  • Autonomous landing
  • Deep learning
  • Kalman filter
  • Object tracking
  • Optimal trajectory
  • Sensor fusion
  • UAV
  • VTOL

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

Cite this