An Aerial Transport System in Marine GNSS-Denied Environment

Jianjun Sun, Zhenwei Niu, Yihao Dong, Fenglin Zhang, Muhayy Ud Din, Lakmal Seneviratne, Defu Lin, Irfan Hussain*, Shaoming He*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

This paper presents an autonomous aerial system specifically engineered for operation in challenging marine GNSS-denied environments, aimed at transporting small cargo from a target vessel. In these environments, characterized by weakly textured sea surfaces with few feature points, chaotic deck oscillations due to waves, and significant wind gusts, conventional navigation methods often prove inadequate. Leveraging the DJI M300 platform, our system is designed to autonomously navigate and transport cargo while overcoming these environmental challenges. In particular, this paper proposes an anchor-based localization method using ultrawideband and quick-response codes facilities, which decouples the unmanned aerial vehicle's (UAV's) attitude from that of the moving landing platform, thus reducing control oscillations caused by platform movement. Additionally, a motor-driven attachment mechanism for cargo is designed, which enhances the UAV's field of view during descent and ensures a reliable attachment to the cargo upon landing. The system's reliability and effectiveness were progressively enhanced through multiple outdoor experimental iterations and were validated by the successful cargo transport during the Mohamed Bin Zayed International Robotics Challenge 2024 competition. Crucially, the system addresses uncertainties and interferences inherent in maritime transportation missions without prior knowledge of cargo locations on the deck and with strict limitations on intervention throughout the transportation.

Original languageEnglish
JournalJournal of Field Robotics
DOIs
Publication statusAccepted/In press - 2025

Keywords

  • GNSS-denied
  • localization
  • marine transport
  • system integration
  • unmanned aerial vehicle
  • vision-based landing

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