ArticleScience progress
Dynamic guidance control for UAV landing on autonomous surface vessel.
Article in Science progress. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study proposes an optimization strategy for a tail-sitter unmanned aerial vehicle (UAV) landing on an autonomous surface vessel (ASV), aiming for minimal risk, shortest time, and lowest energy consumption. Sliding mode control ensures system stability under nonlinear and dynamic conditions. A two-player game model between the UAV and ASV is established, achieving Nash equilibrium to minimize risk and energy. Pareto optimal theory provides a trade-off among multiple objectives, while Karush-Kuhn-Tucker conditions verify the optimality of the solution. An Actor-Critic network generates and evaluates landing strategies through adaptive online learning based on temporal difference errors. Results show that the UAV can dynamically adjust to ship motion, achieving precise, energy-efficient, and low-risk landings. This approach offers practical technical support and theoretical foundations for cooperative operations between UAVs and unmanned ships in diverse environments.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.