Drones

22 pages, 4524 KiB

Open AccessArticle

RAEM-SLAM: A Robust Adaptive End-to-End Monocular SLAM Framework for AUVs in Underwater Environments

by Yekai Wu, Yongjie Li, Wenda Luo and Xin Ding

Drones 2025, 9(8), 579; https://doi.org/10.3390/drones9080579 - 15 Aug 2025

Autonomous Underwater Vehicles (AUVs) play a critical role in ocean exploration. However, due to the inherent limitations of most sensors in underwater environments, achieving accurate navigation and localization in complex underwater scenarios remains a significant challenge. While vision-based Simultaneous Localization and Mapping (SLAM) [...] Read more.

Autonomous Underwater Vehicles (AUVs) play a critical role in ocean exploration. However, due to the inherent limitations of most sensors in underwater environments, achieving accurate navigation and localization in complex underwater scenarios remains a significant challenge. While vision-based Simultaneous Localization and Mapping (SLAM) provides a cost-effective alternative for AUV navigation, existing methods are primarily designed for terrestrial applications and struggle to address underwater-specific issues, such as poor illumination, dynamic interference, and sparse features. To tackle these challenges, we propose RAEM-SLAM, a robust adaptive end-to-end monocular SLAM framework for AUVs in underwater environments. Specifically, we propose a Physics-guided Underwater Adaptive Augmentation (PUAA) method that dynamically converts terrestrial scene datasets into physically realistic pseudo-underwater images for the augmentation training of RAEM-SLAM, improving the system’s generalization and adaptability in complex underwater scenes. We also introduce a Residual Semantic–Spatial Attention Module (RSSA), which utilizes a dual-branch attention mechanism to effectively fuse semantic and spatial information. This design enables adaptive enhancement of key feature regions and suppression of noise interference, resulting in more discriminative feature representations. Furthermore, we incorporate a Local–Global Perception Block (LGP), which integrates multi-scale local details with global contextual dependencies to significantly improve AUV pose estimation accuracy in dynamic underwater scenes. Experimental results on real-world underwater datasets demonstrate that RAEM-SLAM outperforms state-of-the-art SLAM approaches in enabling precise and robust navigation for AUVs. Full article

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28 pages, 9030 KiB

Open AccessArticle

UAV Path Planning via Semantic Segmentation of 3D Reality Mesh Models

by Xiaoxinxi Zhang, Zheng Ji, Lingfeng Chen and Yang Lyu

Drones 2025, 9(8), 578; https://doi.org/10.3390/drones9080578 - 14 Aug 2025

Abstract

Traditional unmanned aerial vehicle (UAV) path planning methods for image-based 3D reconstruction often rely solely on geometric information from initial models, resulting in redundant data acquisition in non-architectural areas. This paper proposes a UAV path planning method via semantic segmentation of 3D reality [...] Read more.

Traditional unmanned aerial vehicle (UAV) path planning methods for image-based 3D reconstruction often rely solely on geometric information from initial models, resulting in redundant data acquisition in non-architectural areas. This paper proposes a UAV path planning method via semantic segmentation of 3D reality mesh models to enhance efficiency and accuracy in complex scenarios. The scene is segmented into buildings, vegetation, ground, and water bodies. Lightweight polygonal surfaces are extracted for buildings, while planar segments in non-building regions are fitted and projected into simplified polygonal patches. These photography targets are further decomposed into point, line, and surface primitives. A multi-resolution image acquisition strategy is adopted, featuring high-resolution coverage for buildings and rapid scanning for non-building areas. To ensure flight safety, a Digital Surface Model (DSM)-based shell model is utilized for obstacle avoidance, and sky-view-based Real-Time Kinematic (RTK) signal evaluation is applied to guide viewpoint optimization. Finally, a complete weighted graph is constructed, and ant colony optimization is employed to generate a low-energy-cost flight path. Experimental results demonstrate that, compared with traditional oblique photogrammetry, the proposed method achieves higher reconstruction quality. Compared with the commercial software Metashape, it reduces the number of images by 30.5% and energy consumption by 37.7%, while significantly improving reconstruction results in both architectural and non-architectural areas. Full article

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33 pages, 941 KiB

Open AccessReview

Noise Prediction and Mitigation for UAS and eVTOL Aircraft: A Survey

by Waleed Raza and Richard S. Stansbury

Drones 2025, 9(8), 577; https://doi.org/10.3390/drones9080577 - 14 Aug 2025

Abstract

The integration of small unmanned aircraft systems (sUASs) and electric vertical takeoff and landing (eVTOL) aircraft into urban airspace presents a new challenge in managing environmental noise, which is a critical factor for the public acceptance of urban air mobility (UAM). This survey [...] Read more.

The integration of small unmanned aircraft systems (sUASs) and electric vertical takeoff and landing (eVTOL) aircraft into urban airspace presents a new challenge in managing environmental noise, which is a critical factor for the public acceptance of urban air mobility (UAM). This survey investigates the noise characteristics of UAS and eVTOL platforms, particularly multi-rotor and distributed propulsion configurations, and examines whether the operational benefits of these vehicles outweigh their acoustic footprint in dense urban environments. While eVTOLs are often perceived as quieter than conventional helicopters due to the absence of combustion engines and mechanically simpler drivetrains, their dominant noise sources are aerodynamic in nature. These include blade vortex interactions, rotor loading noise, and broadband noise, which persist regardless of whether propulsion is electric or combustion-based. Recent studies suggest that community perception of drone noise is influenced more by tonal content, frequency, and modulation patterns than by absolute sound pressure levels. This paper presents a comprehensive review of state-of-the-art noise prediction tools, empirical measurement techniques, and mitigation strategies for sUAS operating in UAM scenarios. The discussion provided in this paper assists in vehicle design, certification standards, airspace planning, and regulatory frameworks focused on minimizing noise impact in urban settings. Full article

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17 pages, 4409 KiB

Open AccessArticle

Combined Robust Control for Quadrotor UAV Using Model Predictive Control and Super-Twisting Algorithm

by Shunsuke Komiyama, Kenji Uchiyama and Kai Masuda

Drones 2025, 9(8), 576; https://doi.org/10.3390/drones9080576 - 13 Aug 2025

Abstract

This paper proposes a robust control method of trajectory tracking for quadrotors under disturbance conditions, combining Model Predictive Control (MPC) and the Super-Twisting Algorithm (STA). MPC is a control strategy that solves an optimization problem by predicting the finite time future response from [...] Read more.

This paper proposes a robust control method of trajectory tracking for quadrotors under disturbance conditions, combining Model Predictive Control (MPC) and the Super-Twisting Algorithm (STA). MPC is a control strategy that solves an optimization problem by predicting the finite time future response from the model under control at each time step. However, MPC cannot guarantee control performance under disturbances such as modeling errors and wind gusts because it predicts future states of the control objects using a nominal model. To solve this problem, we propose a composite control method that uses Adaptive Super-Twisting Sliding Mode Disturbance Observer (ASTSMDO), which constrains the system to follow the MPC’s nominal model. The effectiveness of the proposed method is confirmed through numerical simulation. Compared to conventional MPC, the proposed controller achieves superior robustness and trajectory tracking performance under modeling error and wind disturbance. Full article

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24 pages, 4478 KiB

Open AccessArticle

A Hierarchical Decoupling Task Planning Method for Multi-UAV Collaborative Multi-Region Coverage with Task Priority Awareness

by Yiyuan Li, Weiyi Chen, Bing Fu, Zhonghong Wu and Lingjun Hao

Drones 2025, 9(8), 575; https://doi.org/10.3390/drones9080575 - 13 Aug 2025

Abstract

This study proposes a hierarchical framework with task priority perception for mission planning, to enhance multi-UAV coordination in maritime emergency search and rescue. By establishing a hierarchical decoupling optimization mechanism, the complex multi-region coverage problem is decomposed into two stages: task allocation and [...] Read more.

This study proposes a hierarchical framework with task priority perception for mission planning, to enhance multi-UAV coordination in maritime emergency search and rescue. By establishing a hierarchical decoupling optimization mechanism, the complex multi-region coverage problem is decomposed into two stages: task allocation and path planning. First, a coverage voyage estimation model is constructed based on regional geometric features to provide basic data for subsequent task allocation. Second, an improved multi-objective, multi-population grey wolf optimizer (IM2GWO) is designed to solve the task allocation problem; this integrates adaptive genetic operations and the multi-population coevolutionary mechanism. Finally, a globally optimal coverage path is generated based on the improved dynamic programming (DP). Simulation results indicate that the proposed method effectively reduces total task duration while boosting overall coverage benefits through the aggregation of high-value regions. IM2GWO demonstrates statistically superior performance with respect to the Pareto front distribution index across all test scenarios. Meanwhile, the path planning module based on DP can effectively reduce the overall coverage path cost. Full article

(This article belongs to the Special Issue Advances in Cartography, Mission Planning, Path Search, and Path Following for Drones)

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17 pages, 1182 KiB

Open AccessArticle

Task Allocation Algorithm for Heterogeneous UAV Swarm with Temporal Task Chains

by Haixiao Liu, Zhichao Shao, Quanzhi Zhou, Jianhua Tu and Shuo Zhu

Drones 2025, 9(8), 574; https://doi.org/10.3390/drones9080574 - 13 Aug 2025

Abstract

In disaster relief operations, integrating disaster reconnaissance, material delivery, and effect evaluation into a temporal task chain can significantly reduce emergency response cycles and improve rescue efficiency. However, since multiple types of heterogeneous UAVs need to be coordinated during the rescue temporal task [...] Read more.

In disaster relief operations, integrating disaster reconnaissance, material delivery, and effect evaluation into a temporal task chain can significantly reduce emergency response cycles and improve rescue efficiency. However, since multiple types of heterogeneous UAVs need to be coordinated during the rescue temporal task chains assignment process, this places higher demands on the real-time dynamic decision-making and system fault tolerance of its task assignment algorithm. This study addresses the sequential dependencies among disaster reconnaissance, material delivery, and effect evaluation stages. A task allocation model for heterogeneous UAV swarm targeting temporal task chains is formulated, with objectives to minimize task completion time and energy consumption. A dynamic coalition formation algorithm based on temporary leader election and multi-round negotiation mechanisms is proposed to enhance continuous decision-making capabilities in complex disaster environments. A simulation scenario involving twenty heterogeneous UAVs and seven temporal rescue task chains is constructed. The results show that the proposed algorithm reduces average task completion time by 15.2–23.7% and average fuel consumption by 18.3–26.4% compared with cooperative network protocols and distributed auctions, with up to a 43% reduction in fuel consumption fluctuations. Full article

(This article belongs to the Special Issue The Conceptual Design Methodology for UAV: New Research and New Development)

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19 pages, 4228 KiB

Open AccessArticle

Data-Driven Optimal Bipartite Containment Tracking for Multi-UAV Systems with Compound Uncertainties

by Bowen Chen, Mengji Shi, Zhiqiang Li and Kaiyu Qin

Drones 2025, 9(8), 573; https://doi.org/10.3390/drones9080573 - 13 Aug 2025

Abstract

With the increasing deployment of Unmanned Aerial Vehicle (UAV) swarms in uncertain and dynamically changing environments, optimal cooperative control has become essential for ensuring robust and efficient system coordination. To this end, this paper designs a data-driven optimal bipartite containment tracking control scheme [...] Read more.

With the increasing deployment of Unmanned Aerial Vehicle (UAV) swarms in uncertain and dynamically changing environments, optimal cooperative control has become essential for ensuring robust and efficient system coordination. To this end, this paper designs a data-driven optimal bipartite containment tracking control scheme for multi-UAV systems under compound uncertainties. A novel Dynamic Iteration Regulation Strategy (DIRS) is proposed, which enables real-time adjustment of the learning iteration step according to the task-specific demands. Compared with conventional fixed-step data-driven algorithms, the DIRS provides greater flexibility and computational efficiency, allowing for better trade-offs between the performance and cost. First, the optimal bipartite containment tracking control problem is formulated, and the associated coupled Hamilton–Jacobi–Bellman (HJB) equations are established. Then, a data-driven iterative policy learning algorithm equipped with the DIRS is developed to solve the optimal control law online. The stability and convergence of the proposed control scheme are rigorously analyzed. Furthermore, the control law is approximated via the neural network framework without requiring full knowledge of the model. Finally, numerical simulations are provided to demonstrate the effectiveness and robustness of the proposed DIRS-based optimal containment tracking scheme for multi-UAV systems, which can reduce the number of iterations by 88.27% compared to that for the conventional algorithm. Full article

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24 pages, 4473 KiB

Open AccessArticle

Reliability Analysis of Multi-Rotor Drone Electric Propulsion System Considering Controllability and FDEP

by Nve Xiao, Xianrun Qiao, Xi Chen and Boyang Li

Drones 2025, 9(8), 572; https://doi.org/10.3390/drones9080572 - 13 Aug 2025

Abstract

The electric propulsion system serves as the power source for multi-rotor drones, helping them complete various maneuvering actions. The reliability of this system directly affects whether the drone can successfully complete its mission. The multi-rotor drone propulsion system is a k-out-of-n system with [...] Read more.

The electric propulsion system serves as the power source for multi-rotor drones, helping them complete various maneuvering actions. The reliability of this system directly affects whether the drone can successfully complete its mission. The multi-rotor drone propulsion system is a k-out-of-n system with functional dependence (FDEP). With the insufficient basis for selecting k-values, the problem of incalculable reliability caused by computational space explosion due to voting gates, and the uncertain impact of functional dependence on system reliability, we propose a reliability evaluation method based on controllability theory and BN (Bayesian network) reconstruction. The drone is dynamically modeled, and a control model is built, and k-values are selected through different failure combination controllability evaluations. We model the system with BN, use functional dependent components as BN node inputs, and reconstruct BN via an adder model to solve the problem of exponential growth in the conditional probability table. This paper analyzes system reliability, safety, and the impact of FDEP on the system, and conducts component importance analysis. The result provides important reference for the reliability, safety assessment, and dynamic maintenance processes of multi-rotor drone. Full article

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11 pages, 742 KiB

Open AccessArticle

Evaluating UAVs for Non-Directional Beacon Calibration: A Cost-Effective Alternative to Manned Flight Inspections

by Andrej Novák and Patrik Veľký

Drones 2025, 9(8), 571; https://doi.org/10.3390/drones9080571 - 13 Aug 2025

Abstract

The increasing demand for efficient aviation navigation system inspections has led to the use of Unmanned Aerial Vehicles (UAVs) as a flexible and cost-effective alternative to traditional manned aircraft. This study emphasizes the operational advantages of UAVs in transforming flight inspections, including Non-Directional [...] Read more.

The increasing demand for efficient aviation navigation system inspections has led to the use of Unmanned Aerial Vehicles (UAVs) as a flexible and cost-effective alternative to traditional manned aircraft. This study emphasizes the operational advantages of UAVs in transforming flight inspections, including Non-Directional Beacon (NDB) calibration. Following the successful performance evaluation of an NDB system in Banská Bystrica, Slovakia, using a manned aircraft, a UAV was deployed on the same flight path to validate its ability to replicate the procedure in terms of trajectory only, without performing any signal measurement. The UAV maintained accurate flight paths and continuous communication throughout the mission. A specialized rotatory system, operating at 868 MHz, enabled real-time tracking and ensured stable communication over long distances. The manned aircraft test revealed a maximum bearing deviation of 13.47° at 3.37 NM and a minimum received signal strength of −90 dBm, which approaches the ICAO threshold for en route navigation (±10°) but remains usable for diagnostic purposes. The UAV flight did not include signal capture but successfully completed the 40 NM profile with a circular error probable (CEP95) of 2.8 m and communication link uptime of 99.8%, confirming that the vehicle can meet procedural trajectory fidelity. These findings support the feasibility of UAV-based NDB inspections and provide the foundation for future test phases with onboard signal monitoring systems. Full article

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22 pages, 3506 KiB

Open AccessArticle

UAV Navigation Using EKF-MonoSLAM Aided by Range-to-Base Measurements

by Rodrigo Munguia, Juan-Carlos Trujillo and Antoni Grau

Drones 2025, 9(8), 570; https://doi.org/10.3390/drones9080570 - 12 Aug 2025

Abstract

This study introduces an innovative refinement to EKF-based monocular SLAM by incorporating attitude, altitude, and range-to-base data to enhance system observability and minimize drift. In particular, by utilizing a single range measurement relative to a fixed reference point, the method enables unmanned aerial [...] Read more.

This study introduces an innovative refinement to EKF-based monocular SLAM by incorporating attitude, altitude, and range-to-base data to enhance system observability and minimize drift. In particular, by utilizing a single range measurement relative to a fixed reference point, the method enables unmanned aerial vehicles (UAVs) to mitigate error accumulation, preserve map consistency, and operate reliably in environments without GPS. This integration facilitates sustained autonomous navigation with estimation error remaining bounded over extended trajectories. Theoretical validation is provided through a nonlinear observability analysis, highlighting the general benefits of integrating range data into the SLAM framework. The system’s performance is evaluated through both virtual experiments and real-world flight data. The real-data experiments confirm the practical relevance of the approach and its ability to improve estimation accuracy in realistic scenarios. Full article

(This article belongs to the Section Drone Design and Development)

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20 pages, 27363 KiB

Open AccessArticle

A Heterogeneous Time-Series Soft Actor–Critic Method for Quadruped Locomotion

by Zhaoxu Wang, Zhuoying Chen and Huiping Li

Drones 2025, 9(8), 569; https://doi.org/10.3390/drones9080569 - 12 Aug 2025

Abstract

The locomotion control of unmanned quadruped robots has been one of the greatest challenges in robotics. Deep reinforcement learning has made great achievements in robot control. However, extracting effective features from historical information to improve locomotion agility is still an open and challenging [...] Read more.

The locomotion control of unmanned quadruped robots has been one of the greatest challenges in robotics. Deep reinforcement learning has made great achievements in robot control. However, extracting effective features from historical information to improve locomotion agility is still an open and challenging problem. In this paper, a heterogeneous time-series soft actor–critic (HTS-SAC) method is proposed to enable better policy learning from historical data. Firstly, four mutual information decision conditions are developed for feature selection, which can analyze the correlation between input states and motion performance, obtaining the importance of temporal features of different lengths. Then, according to the results of feature optimization, a novel heterogeneous time-series neural network and the HTS-SAC locomotion control method are designed. Finally, the effectiveness of the proposed method is validated on different terrains using a Laikago quadruped robot simulation model. Full article

(This article belongs to the Special Issue Advances in Deep Learning for Drones and Its Applications: 2nd Edition)

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19 pages, 1476 KiB

Open AccessArticle

Network Design and Content Deployment Optimization for Cache-Enabled Multi-UAV Socially Aware Networks

by Yikun Zou, Gang Wang, Guanyi Chen, Jinlong Wang, Siyuan Yu, Chenxu Wang and Zhiquan Zhou

Drones 2025, 9(8), 568; https://doi.org/10.3390/drones9080568 - 12 Aug 2025

Abstract

Unmanned aerial vehicles (UAVs) with high mobility and self-organization capabilities can establish highly connected networks to cache popular content for edge users, which improves network stability and significantly reduces access time. However, an uneven distribution of demand and storage capacity may reduce the [...] Read more.

Unmanned aerial vehicles (UAVs) with high mobility and self-organization capabilities can establish highly connected networks to cache popular content for edge users, which improves network stability and significantly reduces access time. However, an uneven distribution of demand and storage capacity may reduce the utilization of the storage capacity of UAVs without a proper UAV coordination mechanism. This work proposes a multi-UAV-enabled caching socially aware network (SAN) where UAVs can switch roles by adjusting the social attributes, effectively enhancing data interaction within the UAVs. The proposed network breaks down communication barriers at the UAV layer and integrates the collective storage resources by incorporating social awareness mechanisms to mitigate these imbalances. Furthermore, we formulate a multi-objective optimization problem (MOOP) with the objectives of maximizing both the diversity of cached content and the total request probability (RP) of the network, while employing a multi-objective particle swarm optimization (MOPSO) algorithm with a mutation strategy to approximate the Pareto front. Finally, the impact of key parameters on the Pareto front is analyzed under various scenarios. Simulation results validate the benefits of leveraging social attributes for resource allocation and demonstrate the effectiveness and convergence of the proposed algorithm for the multi-UAV caching strategy. Full article

(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV: 2nd Edition)

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32 pages, 3055 KiB

Open AccessArticle

Research on Scheduling Return Communication Tasks for UAV Swarms in Disaster Relief Scenarios

by Zhangquan Tang, Yuanyuan Jiao, Xiao Wang, Xiaogang Pan and Jiawu Peng

Drones 2025, 9(8), 567; https://doi.org/10.3390/drones9080567 - 12 Aug 2025

Abstract

This study investigates the scheduling problem of return communication tasks for unmanned aerial vehicle (UAV) swarms, where disaster relief environmental global positioning is hampered. To characterize the utility of these tasks and optimize scheduling decisions, we developed a time window-constrained scheduling model that [...] Read more.

This study investigates the scheduling problem of return communication tasks for unmanned aerial vehicle (UAV) swarms, where disaster relief environmental global positioning is hampered. To characterize the utility of these tasks and optimize scheduling decisions, we developed a time window-constrained scheduling model that operates under constraints, including communication base station time windows, battery levels, and task uniqueness. To solve the above model, we propose an enhanced algorithm through integrating Dueling Deep Q-Network (Dueling DQN) into adaptive large neighborhood search (ALNS), referred to as Dueling DQN-ALNS. The Dueling DQN component develops a method to update strategy weights, while the action space defines the destruction and selection strategies for the ALNS scheduling solution across different time windows. Meanwhile, we design a two-stage algorithm framework consisting of centralized offline training and decentralized online scheduling. Compared to traditionally optimized search algorithms, the proposed algorithm could continuously and dynamically interact with the environment to acquire state information about the scheduling solution. The solution ability of Dueling DQN is 3.75% higher than that of the Ant Colony Optimization (ACO) algorithm, 5.9% higher than that of the basic ALNS algorithm, and 9.37% higher than that of the differential evolution algorithm (DE). This verified its efficiency and advantages in the scheduling problem of return communication tasks for UAVs. Full article

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27 pages, 15885 KiB

Open AccessArticle

Model-Free UAV Navigation in Unknown Complex Environments Using Vision-Based Reinforcement Learning

by Hao Wu, Wei Wang, Tong Wang and Satoshi Suzuki

Drones 2025, 9(8), 566; https://doi.org/10.3390/drones9080566 - 12 Aug 2025

Abstract

Autonomous UAV navigation in unknown and complex environments remains a core challenge, especially under limited sensing and computing resources. While most methods rely on modular pipelines involving mapping, planning, and control, they often suffer from poor real-time performance, limited adaptability, and high dependency [...] Read more.

Autonomous UAV navigation in unknown and complex environments remains a core challenge, especially under limited sensing and computing resources. While most methods rely on modular pipelines involving mapping, planning, and control, they often suffer from poor real-time performance, limited adaptability, and high dependency on accurate environment models. Moreover, many deep-learning-based solutions either use RGB images prone to visual noise or optimize only a single objective. In contrast, this paper proposes a unified, model-free vision-based DRL framework that directly maps onboard depth images and UAV state information to continuous navigation commands through a single convolutional policy network. This end-to-end architecture eliminates the need for explicit mapping and modular coordination, significantly improving responsiveness and robustness. A novel multi-objective reward function is designed to jointly optimize path efficiency, safety, and energy consumption, enabling adaptive flight behavior in unknown complex environments. The trained policy demonstrates generalization in diverse simulated scenarios and transfers effectively to real-world UAV flights. Experiments show that our approach achieves stable navigation and low latency. Full article

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19 pages, 2982 KiB

Open AccessArticle

Immersion and Invariance Adaptive Control for Unmanned Helicopter Under Maneuvering Flight

by Xu Zhou, Yousong Xu, Siliang Du and Qijun Zhao

Drones 2025, 9(8), 565; https://doi.org/10.3390/drones9080565 - 12 Aug 2025

Abstract

An asymptotic stability velocity tracking controller is designed to enable the autonomous maneuvering flight of unmanned helicopters. Firstly, taking the UH-60A without pilots as the research object, a high-efficient rotor aerodynamic modeling is developed, which incorporates a free-wake vortex method with the flap [...] Read more.

An asymptotic stability velocity tracking controller is designed to enable the autonomous maneuvering flight of unmanned helicopters. Firstly, taking the UH-60A without pilots as the research object, a high-efficient rotor aerodynamic modeling is developed, which incorporates a free-wake vortex method with the flap response of blades. The consummate flight dynamic model is complemented by wind tunnel-validated fuselage/tail rotor load regressions. Secondly, a linear state–space equation is derived via the small perturbation linearization method based on the flight dynamic model within the body coordinate system. A decoupled model is formulated based on the linear state–space equation by employing the implicit model approach. Subsequently, a system of ordinary differential equations is constructed, which is related to the deviation between actual velocity and its expected value, along with higher-order derivatives of this discrepancy. The I&I (immersion and invariance) theory is then employed to facilitate the design of a non-cascade control loop. Finally, the response of desired velocity in longitudinal channel is simulated with step signal to compare the control effect with a PID (proportional–integral–derivative) controller. By adjusting the coefficients, the response progress of the PID controller is similar to the effect of adaptive controller with I&I theory. However, there is no obvious overshoot in the process with I&I adaptive controller, and the average response amplitude accounts for 16.69% of the random white noise, which is 7.38% of the oscillation level under the PID controller. The parameter tuning complexity when employing I&I theory is significantly lower than that of the PID controller, which is evaluated by mathematical derivations and simulations. Meanwhile, the sidestep and pirouette maneuvers are simulated and analyzed to examine the controller in accordance with the performance criteria outlined in the ADS-33E-REF standards. The simulation results demonstrate that the speed expectation-oriented asymptotic stability control can achieve a fast response. Both sidestep and pirouette maneuvers can satisfy the desired performance requirements stipulated by ADS-33E-REF. Full article

(This article belongs to the Topic Advances in Design, Manufacturing, and Dynamics of Complex Systems)

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26 pages, 5933 KiB

Open AccessFeature PaperArticle

Optimizing Data Distribution Service Discovery for Swarm Unmanned Aerial Vehicles Through Preloading and Network Awareness

by HyeonGyu Lee, Doyoon Kim and SungTae Moon

Drones 2025, 9(8), 564; https://doi.org/10.3390/drones9080564 - 11 Aug 2025

Abstract

Collaborative unmanned aerial vehicle (UAV) swarm operations using the open-source PX4–ROS2 system have been extensively studied for reconnaissance and autonomous missions. PX4–ROS2 utilizes data distribution service (DDS) middleware to ensure network flexibility and support scalable operations. DDS enables decentralized information exchange through its [...] Read more.

Collaborative unmanned aerial vehicle (UAV) swarm operations using the open-source PX4–ROS2 system have been extensively studied for reconnaissance and autonomous missions. PX4–ROS2 utilizes data distribution service (DDS) middleware to ensure network flexibility and support scalable operations. DDS enables decentralized information exchange through its discovery protocol. However, in dense swarm environments, the default initialization process of this protocol generates considerable communication overhead, which hinders reliable peer detection among UAVs. This study introduces an optimized DDS discovery scheme incorporating two key strategies: a preloading method that embeds known participant data before deployment, and a dynamic network awareness approach that regulates discovery behavior based on real-time connectivity. Integrated into PX4–ROS2, the proposed scheme was assessed through both simulations and real-world testing. Results demonstrate that the optimized discovery process reduced peak packet traffic by over 90% during the initial exchange phase, thereby facilitating more stable and scalable swarm operations in wireless environments. Full article

(This article belongs to the Section Drone Communications)

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17 pages, 2749 KiB

Open AccessFeature PaperArticle

Real-Time Wind Estimation for Fixed-Wing UAVs

by Yifan Fu, Weigang An, Xingtao Su and Bifeng Song

Drones 2025, 9(8), 563; https://doi.org/10.3390/drones9080563 - 11 Aug 2025

Abstract

Wind estimation plays a crucial role in atmospheric boundary layer research and aviation flight safety. Fixed-wing UAVs enable rapid and flexible detection across extensive boundary layer regions. Traditional meteorological fixed-wing UAVs require either additional wind measurement sensors or sustained turning maneuvers for wind [...] Read more.

Wind estimation plays a crucial role in atmospheric boundary layer research and aviation flight safety. Fixed-wing UAVs enable rapid and flexible detection across extensive boundary layer regions. Traditional meteorological fixed-wing UAVs require either additional wind measurement sensors or sustained turning maneuvers for wind estimation, increasing operational costs while inevitably reducing mission duration and coverage per flight. This paper proposes a real-time wind estimation method based on an Unscented Kalman Filter (UKF) without aerodynamic sensors. The approach utilizes only standard UAV avionics—GNSS, pitot tube, and Inertial Measurement Unit (IMU)—to estimate wind fields. To validate accuracy, the method was integrated into a meteorological UAV equipped with a wind vane sensor, followed by multiple flight tests. Comparison with wind vane measurements shows real-time wind speed errors below 1 m/s and wind direction errors within 20° (0.349 rad). Results demonstrate the algorithm’s effectiveness for real-time atmospheric boundary layer wind estimation using conventional fixed-wing UAVs. Full article

(This article belongs to the Topic Advances in Design, Manufacturing, and Dynamics of Complex Systems)

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22 pages, 1722 KiB

Open AccessArticle

Finite-Time Adaptive Reinforcement Learning Control for a Class of Morphing Unmanned Aircraft with Mismatched Disturbances and Coupled Uncertainties

by Wei Ren, Yingjie Wei, Cong Wang and Zheng Wang

Drones 2025, 9(8), 562; https://doi.org/10.3390/drones9080562 - 11 Aug 2025

Abstract

This paper proposes a finite-time adaptive reinforcement learning (RL) control law for a class of morphing unmanned aircraft with mismatched disturbances and coupled uncertainties. To handle the mismatched disturbances, an adaptive upper-bound estimator as well as the parameter adaptive laws have been proposed. [...] Read more.

This paper proposes a finite-time adaptive reinforcement learning (RL) control law for a class of morphing unmanned aircraft with mismatched disturbances and coupled uncertainties. To handle the mismatched disturbances, an adaptive upper-bound estimator as well as the parameter adaptive laws have been proposed. Aiming at the coupled uncertainties, an RL-based online uncertainty estimator and a corresponding finite-time compensation control law have been developed. To deal with the non-affine characteristics, an auxiliary integral system has been introduced. By systematically integrating the aforementioned adaptive upper-bound estimators, finite-time control law, and the auxiliary signals, a novel RL-based adaptive finite-time control framework is constructed for morphing unmanned aircraft. Simulation results reveal the finite-time convergence and the advantages of the proposed method. Full article

(This article belongs to the Section Drone Design and Development)

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19 pages, 15866 KiB

Open AccessArticle

Layout and Rotation Effect on Aerodynamic Performance of Multi-Rotor Ducted Propellers

by Zeyu Li, Jianghao Wu, Pengyu Zhang, Lin Wang, Long Chen, Zhengping Zou and Haiying Lin

Drones 2025, 9(8), 561; https://doi.org/10.3390/drones9080561 - 11 Aug 2025

Abstract

Multi-rotor ducted propellers, which integrate the high-efficiency characteristics of ducted propellers with the layout flexibility and safety advantages of distributed propulsion, are extensively utilized in the propulsion systems of low-altitude transport systems and large-scale unmanned aerial vehicles. This study numerically investigates the effects [...] Read more.

Multi-rotor ducted propellers, which integrate the high-efficiency characteristics of ducted propellers with the layout flexibility and safety advantages of distributed propulsion, are extensively utilized in the propulsion systems of low-altitude transport systems and large-scale unmanned aerial vehicles. This study numerically investigates the effects of spanwise distance, streamwise distance, rotational consistency, and rotational phase gap on the unsteady aerodynamic characteristics of multi-rotor ducted propellers under hovering conditions. A parameterized numerical computation model and an Aligned Rank Transform Analysis of Variance (ART-ANOVA) method suitable for small datasets exhibiting regular patterns were developed. Initially, numerical simulations investigated the aerodynamic performance of multi-rotor ducted propeller models with varying layout parameters. The aerodynamic coefficients of the propellers monotonically decrease as the layout spacing increases; however, the change trends differ. Aerodynamic interference reduces the airflow velocity and influences the distribution of high-pressure zones, consequently impacting thrust and efficiency. Subsequently, this paper examined the coupled effects of two rotational characteristics. The relationship between propeller aerodynamic performance and rotational phase gap exhibits distinct trigonometric function characteristics. The presence of the duct mitigates the mutual interference between blades, thereby altering the amplitude and phase of these characteristics. Finally, an ART-ANOVA method was employed to quantify the main and interaction effects, revealing that rotational consistency has a dominant influence on all aspects of aerodynamic performance. Insights into aerodynamic performance are crucial for advancing low-altitude transport systems that utilize ducted propeller propulsion systems. Full article

(This article belongs to the Special Issue Advanced Autonomous Mobility Toward Low-Altitude Economy and Three-Dimensional Transportation Systems)

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