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Drones
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Advanced Flight Dynamics and Decision-Making for UAV Operations
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Advanced Flight Dynamics and Decision-Making for UAV Operations

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drone Design and Development".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 11222

Special Issue Editors

Dr. Paraskevi Zacharia

E-Mail Website
Guest Editor
Department of Industrial Design and Production Engineering, School of Engineering, University of West Attica, 12241 Egaleo, Greece
Interests: development of industrial robot control strategies in the production line of an industry through intelligent control systems combining artificial intelligence methods, optimization systems with heuristic algorithms and visual serving; robot task scheduling and motion planning considering obstacle avoidance in an industrial environment; production planning for optimizing criteria related to the production line through heuristic algorithms; optimization of robotic path planning for moving in an environment cluttered with obstacles with a basic criterion of cycle time, taking into account the multiplicity of robot configurations; development of intelligent transportation systems using autonomous vehicles and application in industrial environment and city logistics
Special Issues, Collections and Topics in MDPI journals
Dr. Antreas Kantaros

E-Mail Website
Guest Editor
Department of Industrial Design and Production Engineering, University of West Attica, 12244 Athens, Greece
Interests: 3D printing; 3D scanning; non-destructive techniques; 3D CAD design; circular economy; sustainability; material science
Special Issues, Collections and Topics in MDPI journals
Dr. Elias K. Xidias

E-Mail Website
Guest Editor
Department of Product & Systems Design Engineering, University of the Aegean, 84100 Syros, Greece
Interests: path planning; motion planning; collision avoidance; optimization
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Antoni Grau

E-Mail Website
Guest Editor
Automatic Control Department, Technical University of Catalonia, 08034 Barcelona, Spain
Interests: autonomous robotics; artificial intelligence in industry; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Unmanned Aerial Vehicles (UAVs) have rapidly evolved in both civil and military applications, demanding increasingly sophisticated control systems and decision-making frameworks. As UAV missions become more complex—ranging from autonomous surveillance and infrastructure inspection to cooperative multi-agent systems—there is a growing need to enhance their flight dynamics modeling, real-time adaptability, and intelligent autonomy. This Special Issue focuses on cutting-edge advancements in flight dynamics, control algorithms, and decision-making mechanisms that enable UAVs to operate safely, efficiently, and autonomously in dynamic environments.

In particular, the integration of machine learning and data-driven techniques has opened new frontiers in UAV autonomy. From reinforcement learning for adaptive control to neural networks for system identification, and deep learning for perception and decision-making, these approaches are transforming how UAVs learn, respond, and optimize their operations in uncertain and complex scenarios.

Moreover, the development and deployment of UAV platforms increasingly benefit from advancements in digital design and additive manufacturing, allowing rapid prototyping and structural optimization through 3D printing. These technologies facilitate custom-built UAV components designed for specific mission profiles, including lightweight airframes and sensor housings.

The aim of this Special Issue is to bring together state-of-the-art research that pushes the boundaries of UAV flight control and autonomy. Contributions should align with the scope of Drones, emphasizing novel methodologies, rigorous simulations, experimental validations, and practical approaches that contribute to the scientific and technological advancement of UAV systems. We welcome original research articles, comprehensive reviews, and case studies that address theoretical developments or applied solutions.

Potential topics include, but are not limited to:

  • Nonlinear and adaptive flight control
  • Autonomous navigation and trajectory optimization
  • Multi-agent coordination and swarm intelligence
  • AI-enhanced decision-making for UAVs
  • Learning-based flight dynamics modeling
  • Reinforcement learning and imitation learning for UAVs
  • Deep learning for perception, localization, and control
  • Real-time sensing, planning, and environment mapping
  • Design optimization and rapid prototyping of UAVs using 3D printing
  • Integration of additive manufacturing in UAV development workflows

We encourage submissions from academia, industry, and research institutions that explore innovative approaches to the future of UAV operations.

Dr. Paraskevi Zacharia
Dr. Antreas Kantaros
Dr. Elias K. Xidias
Prof. Dr. Antoni Grau
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 250 words) can be sent to the Editorial Office for assessment.

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. Drones is an international peer-reviewed open access monthly 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 2600 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

  • UAV flight dynamics
  • autonomous control
  • decision-making
  • machine learning
  • reinforcement learning
  • neural networks
  • UAV simulation
  • swarm UAVs
  • 3D Printing
  • UAV design
  • additive manufacturing

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

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Research

26 pages, 2269 KB  
Article
Mission-Driven UAV Path Selection: Post Hoc Cost Evaluation of Deterministic and Sampling Approaches
by James R. Kelly and Umair B. Chaudhry
Drones 2026, 10(2), 152; https://doi.org/10.3390/drones10020152 - 22 Feb 2026
Viewed by 510
Abstract
Unmanned Aerial Vehicles (UAVs) are increasingly deployed in hazardous and dynamic environments, where path planning requires balancing competing objectives beyond simple distance minimisation. Classical planners such as Dijkstra, A*, and RRT* generate paths efficiently but often overlook mission-specific trade-offs involving energy use, risk [...] Read more.
Unmanned Aerial Vehicles (UAVs) are increasingly deployed in hazardous and dynamic environments, where path planning requires balancing competing objectives beyond simple distance minimisation. Classical planners such as Dijkstra, A*, and RRT* generate paths efficiently but often overlook mission-specific trade-offs involving energy use, risk avoidance, and reward maximisation. This work proposes a unified evaluation framework that integrates grid-based (Dijkstra, A*, weighted A*) and sampling-based (RRT, CARRT*) planners within parameterised environments embedding a range of functions into penalty and reward zones. A global cost function, J=αL+βE+γP−δR, is applied post hoc to decouple path generation from mission prioritisation, enabling rapid reassessment under changing objectives such as low-fuel, high-safety, or speed-priority scenarios. Experiments conducted on an Apple M2 CPU, repeated three times per configuration to ensure statistical robustness, demonstrate that CARRT* achieves the lowest mission costs and highest efficiency for fuel- and time-sensitive missions, while deterministic grid-based planners perform better in safety- and reward-oriented contexts in four environments. These findings indicate that optimal UAV path planning depends not only on algorithmic efficiency but also on aligning planner choice with mission priorities. The framework provides a reproducible methodology for benchmarking and deploying mission-aware path planning strategies in research and operational settings. Full article
(This article belongs to the Special Issue Advanced Flight Dynamics and Decision-Making for UAV Operations)
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26 pages, 7208 KB  
Article
Investigation of a Vertically Offset Rear-Rotor Quadrotor Configuration for Aerodynamic Interference Mitigation
by He Zhu, Xinyu Yi, Hong Nie, Xiaohui Wei, Qijun Zhao and Yin Yin
Drones 2026, 10(2), 92; https://doi.org/10.3390/drones10020092 - 28 Jan 2026
Viewed by 745
Abstract
The deployment of multi-rotor drones in applications such as package delivery and urban air mobility is increasingly prevalent. Aerodynamic interference between rotors in traditional quadrotor drones impairs performance, and vertical offset is a promising solution to mitigate this interference. This study systematically investigates [...] Read more.
The deployment of multi-rotor drones in applications such as package delivery and urban air mobility is increasingly prevalent. Aerodynamic interference between rotors in traditional quadrotor drones impairs performance, and vertical offset is a promising solution to mitigate this interference. This study systematically investigates the aerodynamic characteristics of a quadrotor drone with a vertically offset rear-rotor configuration through computational fluid dynamics (CFD) simulations. By varying the vertical spacing ratio between the front and rear rotors (H/R), quantitative analyses were conducted of key performance metrics, including rotor thrust and power loading, with explanations provided from the perspective of the flow field structure. Furthermore, the underlying physical mechanisms influencing the observed performance variations are explored. The results indicate that, under the operating conditions investigated in this study, which include a single rotor RPM, a 10° inflow angle, and a specific forward-flight speed, the vertically offset configuration demonstrates superior aerodynamic performance at H/R = 1. At this spacing ratio, the rear rotor disk avoids most of the downwash-induced velocity generated by the front rotor, allowing partial recovery of the effective angle of attack. Consequently, vortex-propeller interaction (PVI) is significantly weakened, turbulent kinetic energy (TKE) levels in the interference region are reduced, and premature flow separation on the rear rotor blades is suppressed. These combined effects enhance overall aerodynamic efficiency. This study clarifies the role of vertical rotor spacing in regulating aerodynamic interference in multi-rotor drones, offering valuable insights for the aerodynamic design of compact rotorcraft. Full article
(This article belongs to the Special Issue Advanced Flight Dynamics and Decision-Making for UAV Operations)
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30 pages, 4879 KB  
Article
Physical Modeling and Data-Driven Hybrid Control for Quadrotor-Robotic-Arm Cable-Suspended Payload Systems
by Lu Lu, Qihua Xiao, Shikang Zhou, Xinhai Wang and Yunhe Meng
Drones 2026, 10(1), 51; https://doi.org/10.3390/drones10010051 - 10 Jan 2026
Cited by 1 | Viewed by 816
Abstract
This work investigates a quadrotor equipped with dual-stage robotic arms and a cable-suspended payload, developing a unified methodology for modeling and control. A 10-DOF Lagrangian model captures vehicle-arm-payload coupling through structured mass matrices. A hierarchical control architecture combines SO(3)-based attitude regulation with cooperative [...] Read more.
This work investigates a quadrotor equipped with dual-stage robotic arms and a cable-suspended payload, developing a unified methodology for modeling and control. A 10-DOF Lagrangian model captures vehicle-arm-payload coupling through structured mass matrices. A hierarchical control architecture combines SO(3)-based attitude regulation with cooperative swing compensation via partial feedback linearization, exploiting coupling matrices to distribute control between platform and arm actuators. Model accuracy is enhanced through physics-informed system identification, achieving improved prediction correlation with bounded corrections. Lyapunov analysis establishes semi-global practical stability with explicit robustness bounds. High-fidelity simulations in MuJoCo demonstrate a 40–70% swing reduction compared to PD control across multiple scenarios, with low computational overhead at kHz-level control rates, making it suitable for embedded implementation. The framework provides a theoretical foundation and implementation guidelines for cooperative aerial manipulation systems. Full article
(This article belongs to the Special Issue Advanced Flight Dynamics and Decision-Making for UAV Operations)
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18 pages, 1756 KB  
Article
Delay-Aware UAV Swarm Formation Control via Imitation Learning from ARD-PF Expert Policies
by Rodolfo Vera-Amaro, Alberto Luviano-Juárez and Mario E. Rivero-Ángeles
Drones 2026, 10(1), 34; https://doi.org/10.3390/drones10010034 - 6 Jan 2026
Cited by 1 | Viewed by 1193
Abstract
This paper studies delay-aware formation control for (unmanned aerial vehicle) UAV swarms operating under realistic air-to-air communication latency. An attractive–repulsive distance-based potential-field (ARD-PF) controller is used as an expert to generate demonstrations for imitation learning in multi-UAV cooperative systems. By augmenting the training [...] Read more.
This paper studies delay-aware formation control for (unmanned aerial vehicle) UAV swarms operating under realistic air-to-air communication latency. An attractive–repulsive distance-based potential-field (ARD-PF) controller is used as an expert to generate demonstrations for imitation learning in multi-UAV cooperative systems. By augmenting the training data with communication delay, the learned policy implicitly compensates for outdated neighbor information and improves swarm coordination during autonomous flight. Extensive simulations across different swarm sizes, formation spacings, and delay levels show that delay-robust imitation learning significantly enlarges the probabilistic stability region compared with classical ARD-PF control and non-robust learning baselines. Formation control performance is evaluated using internal geometric error, global offset, and multi-run stability metrics. In addition, a predictive delay–stability model is introduced, linking the maximum admissible communication delay to swarm size and inter-agent spacing, with low fitting error against simulated stability boundaries. The results provide quantitative insights for designing communication-aware UAV swarm systems under latency constraints. Full article
(This article belongs to the Special Issue Advanced Flight Dynamics and Decision-Making for UAV Operations)
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30 pages, 8648 KB  
Article
Research on Dynamic Center-of-Mass Reconfiguration for Enhancement of UAV Performances Based on Simulations and Experiment
by Anas Ahmed, Guangjin Tong and Jing Xu
Drones 2025, 9(12), 854; https://doi.org/10.3390/drones9120854 - 12 Dec 2025
Viewed by 1907
Abstract
The stability of unmanned aerial vehicles (UAVs) during propulsion failure remains a critical safety challenge. This study presents a center-of-mass (CoM) correction device, a compact, under-slung, and dual-axis prismatic stage, which can reposition a dedicated shifting mass within the UAV frame [...] Read more.
The stability of unmanned aerial vehicles (UAVs) during propulsion failure remains a critical safety challenge. This study presents a center-of-mass (CoM) correction device, a compact, under-slung, and dual-axis prismatic stage, which can reposition a dedicated shifting mass within the UAV frame to generate stabilizing gravitational torques by the closed-loop feedback from the inertial measurement unit (IMU). Two major experiments were conducted to evaluate the feasibility of the system. In a controlled roll test with varying payloads, the device produced a corrective torque up to 1.2375 N·m, reducing maximum roll deviations from nearly 90° without the device to less than 5° with it. In a dynamic free-fall simulation, the baseline UAV exhibited rapid tumbling and inverted impacts, whereas with the CoM system activated, the UAV maintained a near-level attitude to achieve the upright recovery and greatly reduced structural stress prior to ground contact. The CoM device, as a fail-safe stabilizer, can also enhance maneuverability by increasing control authority, enable a faster speed response and more efficient in-air braking without reliance on the rotor thrust, and achieve comprehensive energy saving, at about 7% of the total power budget. In summary, the roll stabilization and free-fall results show that the CoM device can work as a practical pathway toward the safer, more agile, and energy-efficient UAV platforms for civil, industrial, and defense applications. Full article
(This article belongs to the Special Issue Advanced Flight Dynamics and Decision-Making for UAV Operations)
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31 pages, 3140 KB  
Article
A 3D-Printed, Open-Source, Low-Cost Drone Platform for Mechatronics and STEM Education in an Academic Context
by Avraam Chatzopoulos, Antreas Kantaros, Paraskevi Zacharia, Theodore Ganetsos and Michail Papoutsidakis
Drones 2025, 9(11), 797; https://doi.org/10.3390/drones9110797 - 17 Nov 2025
Cited by 4 | Viewed by 4957
Abstract
This study presents the design and implementation of a low-cost, open-source, 3D-printed drone platform for university-level STEM education in mechatronics, robotics, control theory, and artificial intelligence. The platform addresses key limitations of existing educational drones, such as high cost, the proprietary nature of [...] Read more.
This study presents the design and implementation of a low-cost, open-source, 3D-printed drone platform for university-level STEM education in mechatronics, robotics, control theory, and artificial intelligence. The platform addresses key limitations of existing educational drones, such as high cost, the proprietary nature of systems, and limited customizability, by integrating accessible materials, Arduino-compatible microcontrollers, and modular design principles, with all design files and instructional materials openly available. This work introduces technical improvements, including enhanced safety features and greater modularity, alongside pedagogical advancements such as structured lesson plans, a workflow bridging simulation, and hardware implementation. Educational impact was evaluated through a case study in a postgraduate course with 39 students participating in project-based activities involving 3D modeling, electronics integration, programming, and flight testing. Data collected via a Technology Acceptance Model-based survey and researcher observations showed high student engagement and satisfaction, with average scores of 4.49/5 for overall experience, 4.31/5 for perceived usefulness, and 4.38/5 for intention to use the drone in future activities. These results suggest the platform is a practical and innovative teaching tool for academic settings. Future work will extend its educational evaluation and application across broader contexts. Full article
(This article belongs to the Special Issue Advanced Flight Dynamics and Decision-Making for UAV Operations)
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