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Unmanned Aerial Vehicles (UAVs) and Autonomous Marine Vehicles (AMVs): Emerging...
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Unmanned Aerial Vehicles (UAVs) and Autonomous Marine Vehicles (AMVs): Emerging Technologies and Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 80

Special Issue Editors

Prof. Dr. Meiqin Liu

E-Mail Website
Guest Editor
College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Interests: theory and applications of artificial intelligence; perception and processing of underwater information; multi-sensor information fusion
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shanling Dong

E-Mail Website
Guest Editor
College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Interests: networked control systems; intelligent control; information fusion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the ongoing development of communication technologies, sensor technologies, and artificial intelligence, unmanned systems such as unmanned aerial vehicles (UAVs) and autonomous marine vehicles (AMVs) have come to be widely used in both military and civil applications. They have, consequently, received great attention from researchers, and much effort has been put into their development. Accordingly, the objective of this Special Issue is to present the new advances in UAV and AMV systems.

The scope of this Special Issue covers, but is not limited to, the following topics:
Modeling, path planning, localization, formation control, coordination control, fault diagnosis, security control, performance optimization, hardware design, tracking control, obstacle avoidance, vision-based navigation, reinforcement learning, dynamic simulation, experiments, and applications for both aerial and marine autonomous vehicles.

Prof. Dr. Meiqin Liu
Prof. Dr. Shanling Dong
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • path planning
  • localization
  • fault diagnosis
  • optimization
  • hardware design
  • navigation
  • security
  • control
  • intelligent learning
  • analysis and applications

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Published Papers (1 paper)

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Research

31 pages, 5821 KB  
Article
Trajectory Tracking Control Method via Simulation for Quadrotor UAVs Based on Hierarchical Decision Dual-Threshold Adaptive Switching
by Fei Peng, Qiang Gao, Hongqiang Lu, Zhonghong Bu, Bobo Jia, Ganchao Liu and Zhong Tao
Appl. Sci. 2025, 15(20), 11217; https://doi.org/10.3390/app152011217 - 20 Oct 2025
Abstract
In complex 3D maneuvering tasks (e.g., post-disaster rescue, urban operations, and infrastructure inspection), the trajectories that quadrotors need to track are often complex—containing both gentle flight phases and highly maneuverable trajectory segments. Under such trajectory tracking tasks with the composite characteristics of “gentle-high [...] Read more.
In complex 3D maneuvering tasks (e.g., post-disaster rescue, urban operations, and infrastructure inspection), the trajectories that quadrotors need to track are often complex—containing both gentle flight phases and highly maneuverable trajectory segments. Under such trajectory tracking tasks with the composite characteristics of “gentle-high maneuvering”, quadrotors face challenges of limited onboard computing resources and short endurance, requiring a balance between trajectory tracking accuracy, computational efficiency, and energy consumption. To address this problem, this paper proposes a lightweight trajectory tracking control method based on hierarchical decision-making and dual-threshold adaptive switching. Inspired by the biological “prediction–reflection” mechanism, this method designs a dual-threshold collaborative early warning switching architecture of “prediction layer–confirmation layer”: The prediction layer dynamically assesses potential risks based on trajectory curvature and jerk, while the confirmation layer confirms in real time the stability risks through an attitude-angular velocity composite index. Only when both exceed the thresholds, it switches from low-energy-consuming Euler angle control to high-precision geometric control. Simulation experiments show that in four typical trajectories (straight-line rapid turn, high-speed S-shaped, anti-interference composite, and narrow space figure-eight), compared with pure geometric control, this method reduces position error by 19.5%, decreases energy consumption by 45.9%, and shortens CPU time by 28%. This study not only optimizes device performance by improving trajectory tracking accuracy while reducing onboard computational load, but also reduces energy consumption to extend UAV endurance, and simultaneously enhances anti-disturbance capability, thereby improving its operational capability to respond to emergencies in complex environments. Overall, this study provides a feasible solution for the efficient and safe flight of resource-constrained onboard platforms in multi-scenario complex environments in the future and has broad application and expansion potential. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAVs) and Autonomous Marine Vehicles (AMVs): Emerging Technologies and Applications)
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