Agile UAV Landing on a Moving Maritime Vessel via MPC-Based Trajectory Generation

Ondřej Procházka, Filip Novák, Tomáš Báča, Parakh M. Gupta, Robert Penicka, Martin Saska

Keywords: unmanned aerial vehicle, unmanned surface vehicle, model predictive control, autonomous landing, maritime robotics, multi-robot systems

TL;DR: The proposed MPC-FAA method enables UAV landing on a rocking maritime vessel by synchronizing its attitude with the deck just before touchdown.

Abstract: Autonomous landing of an Unmanned Aerial Vehicle (UAV) on a maritime vessel in harsh sea conditions is a critical capability for enabling persistent UAV operations at sea. We present a Model Predictive Controller (MPC)-based trajectory generation method that enables a multirotor UAV to land precisely and rapidly on the deck of a moving Autonomous Surface Vessel (USV), even when the deck is continuously tilting due to waves. The key novelty is a dynamic penalization scheme, which we named Forcing Attitude Alignment (FAA), that exponentially increases the weight on attitude-tracking as the UAV approaches the deck, driving attitude synchronization between the UAV and the USV upon touchdown. The MPC exploits full-state predictions of the USV, including position, orientation, and their first derivatives, and a three-phase state machine governs distinct flight behaviours from navigation through landing. Simulations across sea states up to Rough sea (wave height 4 m) demonstrate a 100 % success rate in most scenarios, twice the touchdown precision, and 50 % faster landing than the studied state-of-the-art methods. Real-world experiments on a moving USV corroborate these results with all computation performed onboard the UAV in real time.

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Submission Number: 3