Drones

38 pages, 1698 KB

Open AccessArticle

Research on Integrated Decision-Control Cooperative Target Assignment for Cross-Domain Unmanned Systems Based on a Bi-Level Optimization Framework

by Aoyu Zheng, Xiaolong Liang, Zhiyang Zhang, Yuyan Xiao and Jiaqiang Zhang

Drones 2026, 10(3), 193; https://doi.org/10.3390/drones10030193 - 10 Mar 2026

Abstract

Addressing prevalent challenges in current cooperative task assignment methods for cross-domain unmanned swarm, such as the disconnection between decision-making and execution processes, and the inadequate incorporation of platform kinematic constraints, this study introduces an integrated decision-control cooperative task assignment approach based on a [...] Read more.

Addressing prevalent challenges in current cooperative task assignment methods for cross-domain unmanned swarm, such as the disconnection between decision-making and execution processes, and the inadequate incorporation of platform kinematic constraints, this study introduces an integrated decision-control cooperative task assignment approach based on a bi-level optimization framework. The proposed framework formulates a bi-level programming model that tightly couples upper-level task assignment with lower-level optimal control. The upper-level model aims to minimize the maximum task completion time by optimizing the assignment and visitation sequences of diverse target types across heterogeneous unmanned platforms. The lower-level model, given the task sequences from the upper level, addresses a minimum-time optimal control problem based on a comprehensive nonlinear kinematic model. This approach enables precise computation of task execution times, which are subsequently fed back to the decision-making layer, thereby establishing a closed-loop optimization mechanism. To solve this complex model efficiently, the lower-level employs differential flatness transformation to eliminate trigonometric functions in the kinematic equations and discretizes the continuous-time optimal control problem into a nonlinear programming problem via the Radau pseudospectral method. For the upper-level combinatorial optimization, an improved genetic algorithm is developed, integrating hybrid encoding, dual-archive elitism preservation, adaptive crossover and mutation strategies, and periodic local search. Simulation results demonstrate that, compared with traditional Euclidean-distance-based assignment methods, the proposed approach generates kinematically feasible and smooth trajectories while thoroughly accounting for the kinematic constraints of heterogeneous platforms, thereby demonstrating its effectiveness and superiority in improving the comprehensive mission performance of cross-domain unmanned swarms. Full article

(This article belongs to the Special Issue Advances in Perception, Communications, and Control for Drones: 2nd Edition)

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31 pages, 9741 KB

Open AccessArticle

RG-HDP-VD: A Physics-Aware Cooperative Trajectory Planning Framework for Heterogeneous Multi-UAVs

by Dan Han, Zhaoyuan Hua, Xinyu Zhu, Liang Luo, Hao Jiang and Lifang Wang

Drones 2026, 10(3), 192; https://doi.org/10.3390/drones10030192 - 10 Mar 2026

Abstract

This paper presents Regret-Guided Heuristic Decentralized Prioritized Planning with Velocity Decomposition (RG-HDP-VD), a physics-aware cooperative trajectory planning framework for heterogeneous Unmanned Aerial Vehicles (UAVs) relief delivery in post-earthquake, non-convex canyon environments. RG-HDP-VD addresses two prevalent failure modes: energy-inefficient congestion caused by ignoring time-varying [...] Read more.

This paper presents Regret-Guided Heuristic Decentralized Prioritized Planning with Velocity Decomposition (RG-HDP-VD), a physics-aware cooperative trajectory planning framework for heterogeneous Unmanned Aerial Vehicles (UAVs) relief delivery in post-earthquake, non-convex canyon environments. RG-HDP-VD addresses two prevalent failure modes: energy-inefficient congestion caused by ignoring time-varying payload dynamics, and the collapse of feasible sets due to strict arrival windows in fixed-speed planning. We construct a mass-augmented energy topology and use a mass-augmented energy-aware A* search to extract baseline physical metrics—path length, total energy, and unit-distance energy—for each UAV. Regret-Guided (RG) arbitration then quantifies the relative energy cost of waiting versus detouring at conflicts and grants right-of-way to heavy-load, high-cost platforms. These priorities are embedded into Heuristic Decentralized Prioritized Planning (HDP), which maintains a global spatiotemporal occupancy map and serializes planning to eliminate deadlocks. To satisfy tight time windows, Velocity Decomposition (VD) maps 4D temporal constraints into a 3D path-length feasible interval and is realized via an improved VD-TSRRT* sampling-based planner. In high-fidelity simulations, RG-HDP-VD demonstrates superior scalability over conventional methods, maintaining high success rates (up to 100%) in saturated scenarios, while reducing average planning time by ~45% and total system energy by 6.7%. Finally, real-world flight demonstrations using a heterogeneous quadrotor team validate the framework’s practical feasibility and robust hardware execution. Full article

(This article belongs to the Special Issue UAV Path Planning Algorithms for Surveillance and Reconnaissance in Civil Applications)

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17 pages, 8254 KB

Open AccessArticle

QoS-Aware Downlink Paging Control for UAV-Assisted 5G-Advanced Networks with On-Demand Coverage

by Conghao Li, Haizhi Yu, Weidong Gao, Dengyan Wang, Shouhui Lai, Xu Zhao, Hongzhi Zhang and Gengshuo Liu

Drones 2026, 10(3), 191; https://doi.org/10.3390/drones10030191 - 10 Mar 2026

Abstract

To meet the energy-saving requirements of user equipment (UE) operating in Radio Resource Control idle/inactive states (RRC_IDLE/RRC_INACTIVE) in the 3rd-Generation Partnership Project (3GPP) 5G-Advanced (5G-A) networks, the New Radio (NR) downlink paging procedure relies on periodic monitoring and frequent synchronization signal block (SSB) [...] Read more.

To meet the energy-saving requirements of user equipment (UE) operating in Radio Resource Control idle/inactive states (RRC_IDLE/RRC_INACTIVE) in the 3rd-Generation Partnership Project (3GPP) 5G-Advanced (5G-A) networks, the New Radio (NR) downlink paging procedure relies on periodic monitoring and frequent synchronization signal block (SSB) measurements, which wastes energy when no paging arrivals occur. Meanwhile, heterogeneous Quality of Service (QoS) constraints make it difficult for fixed-parameter Idle Discontinuous Reception and Paging Early Indication mechanisms (IDRX/PEI) to balance energy, delay, and reliability. This paper develops a UAV-assisted 5G-A paging control framework that maps services into multiple QoS classes and models QoS violation risk and system energy consumption under unified accounting, including UE monitoring/reception energy and unmanned aerial vehicle (UAV) forwarding energy. We then propose a QoS-aware risk-driven paging strategy: an offline Long Short-Term Memory (LSTM) predictor is trained to estimate the time-to-next-arrival (TTNA) of paging events and produce a bounded urgency/risk signal to initialize class-dependent thresholds, while online triggering and QoS-feedback-based threshold adaptation regulate the empirical violation rate toward target constraints under varying loads, enabling a controllable energy–delay trade-off. A simulation-based evaluation is conducted to compare the proposed method with representative baselines (Enhanced Paging Monitoring (EPM), Split Paging Occasion (SPOP), and Predicted Paging Early Indication (PPEI)) and to examine the impact of SSB overhead and UAV relaying on the energy–delay–reliability trade-offs. Full article

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27 pages, 12591 KB

Open AccessArticle

Audio–Visual Fusion Sim2Real Platform for Anti-UAV Detection and Tracking

by Xiaohong Nian, Haolun Liu and Xunhua Dai

Drones 2026, 10(3), 190; https://doi.org/10.3390/drones10030190 - 10 Mar 2026

Abstract

To address the escalating security challenges posed by unauthorized Unmanned Aerial Vehicles, this paper presents a Sim2real physics-informed audio–visual fusion simulation platform designed to enhance Counter-Unmanned Aerial Vehicle detection and tracking performance. The proposed method integrates two complementary sensing pipelines: a physics-based acoustic [...] Read more.

To address the escalating security challenges posed by unauthorized Unmanned Aerial Vehicles, this paper presents a Sim2real physics-informed audio–visual fusion simulation platform designed to enhance Counter-Unmanned Aerial Vehicle detection and tracking performance. The proposed method integrates two complementary sensing pipelines: a physics-based acoustic localization system utilizing Time Difference of Arrival principles and a deep learning-driven visual detection framework. To ensure robust surveillance against non-cooperative targets, these pipelines are not only fused through strict spatiotemporal synchronization but also mutually reinforce each other—acoustic data guides visual attention in low-visibility scenarios typical of adversarial intrusions, while visual detections refine acoustic parameter estimation. Building upon prior work in multi-modal perception, we extend the framework to dynamic environments characterized by configurable visual obstructions, including smoke and fog, which frequently compromise conventional optical anti-drone systems. Experiments demonstrate that the fusion system progressively adapts to degraded visual conditions, extending tracking continuity from approximately 50% coverage under vision-only operation to near-continuous target awareness, with a moderate trade-off in average angular precision when acoustic-only segments are included. Physical validation with quadrotor Unmanned Aerial Vehicles confirms the platform’s capability to bridge simulation-to-reality gaps. Our results highlight the system’s robustness against sensor degradation and its potential to accelerate the development of resilient multisensor Counter-Unmanned Aerial Vehicle systems while reducing dependency on costly field testing. Full article

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64 pages, 9863 KB

Open AccessReview

Drone-Enabled Practices in Modern Warehouse Management: A Comprehensive Review

by Eknath Pore, Bhumeshwar K. Patle, Sandeep Thorat and Brijesh Patel

Drones 2026, 10(3), 189; https://doi.org/10.3390/drones10030189 - 9 Mar 2026

Abstract

The advent of drone technology has led to groundbreaking advancements across various industries, including warehousing operations. In recent years, warehouse drones have garnered significant attention due to their potential to revolutionize traditional inventory management and order fulfillment processes. This paper presents a comprehensive [...] Read more.

The advent of drone technology has led to groundbreaking advancements across various industries, including warehousing operations. In recent years, warehouse drones have garnered significant attention due to their potential to revolutionize traditional inventory management and order fulfillment processes. This paper presents a comprehensive review that synthesizes findings from more than 120 research papers on drone-enabled practices in warehouses. The review systematically considers multiple parameters, including drone function (inventory counting, mapping, surveillance, inspection, and intralogistics support), robot platforms used (UAV, UAV-AGV), deployment architecture (single and multi-drone system), validation approach (real-time and simulation), technology and methodology used (modern electronic devices, AI, and IOT), and environmental context (dynamic and static). Furthermore, the paper explores the diverse applications of warehouse drones in inventory management, maintenance and inspection, picking and packaging, goods transportation, security and surveillance, and warehouse layout optimization. The review highlights that most studies still rely on single-UAV systems tested mainly in simulations, with only a few real-time demonstrations of fully autonomous performance inside real warehouses. Although multi-drone approaches are emerging to improve scalability, they continue to struggle with coordination and safety. Research remains largely focused on static environments, with dynamic warehouse conditions receiving far less attention despite their practical importance. The findings of the review are presented with the tabulated results and a comparative table to provide a better understanding of the review work, which helps to identify the existing literature gap. The review presents its findings through clear tables and comparisons, making it easier to understand existing studies and pinpoint the gaps in the current literature. Full article

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26 pages, 23794 KB

Open AccessArticle

A Novel Hierarchical Topology-Metric Road Graph (HTMRG) Construction for UGV Navigation

by Shuai Zhou, Xiaosu Xu, Tao Zhang and Nuo Li

Drones 2026, 10(3), 188; https://doi.org/10.3390/drones10030188 - 9 Mar 2026

Abstract

Autonomous navigation in complex environments requires efficient and reliable road-network representations for fast path planning. However, traditional grid and skeleton-based approaches often suffer from high computational cost and limited path quality. This paper proposes a Hierarchical Topology-Metric Road Graph (HTMRG) framework for autonomous [...] Read more.

Autonomous navigation in complex environments requires efficient and reliable road-network representations for fast path planning. However, traditional grid and skeleton-based approaches often suffer from high computational cost and limited path quality. This paper proposes a Hierarchical Topology-Metric Road Graph (HTMRG) framework for autonomous navigation of unmanned ground vehicles (UGVs). The method automatically constructs a hierarchical road graph from grid maps by identifying key intersection structures and generating smooth corridor and intersection connections. In addition, a dedicated start–goal insertion strategy is developed to enable efficient graph-based path planning in previously unexplored scenarios. Extensive simulations and real-world experiments demonstrate that the proposed method can automatically construct hierarchical road graphs and generate smooth, high-quality paths with improved planning efficiency and robustness. The HTMRG framework has also been successfully integrated into a UGV system, validating its effectiveness and practicality in real-world navigation scenarios. Full article

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23 pages, 15039 KB

Open AccessArticle

Impact of Atmospheric Turbulence on Data Quality During BVLOS UAV Missions in Antarctic Conditions

by Anna Zmarz and Mirosław Rodzewicz

Drones 2026, 10(3), 187; https://doi.org/10.3390/drones10030187 - 9 Mar 2026

Abstract

This article presents an analysis of the impact of atmospheric turbulence on the quality of images obtained during photogrammetric missions in Antarctica using a fixed-wing UAV operating in BVLOS mode. Image quality was evaluated primarily by the degree of blurring, which served as [...] Read more.

This article presents an analysis of the impact of atmospheric turbulence on the quality of images obtained during photogrammetric missions in Antarctica using a fixed-wing UAV operating in BVLOS mode. Image quality was evaluated primarily by the degree of blurring, which served as the main assessment criterion. In the Antarctic region, turbulence is a frequent phenomenon and can occur even under very light wind conditions, which formed the basis of this study. Autopilot log data were used to conduct a series of analyses, resulting in maps of areas where turbulence symptoms were recorded. In parallel, the quality of images captured during the mission was examined, producing a map of blurring levels assessed on a five-point scale. The study shows that UAV image blurring is mainly caused by sudden camera movements, mechanical vibrations from the propulsion system, and atmospheric turbulence that disrupts flight stability and overloads image stabilization. Additional factors such as low-light conditions, fog, haze, precipitation, glare, and moving shadows further reduce image clarity. Full article

(This article belongs to the Section Drones in Ecology)

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Graphical abstract

25 pages, 30697 KB

Open AccessArticle

A Collaborative Navigation Algorithm for Unmanned Aerial Vehicles Based on Joint Cognition and Risk Perception

by Chenkang Huang, Ruixuan Wei, Benqi Jiang, Pengfei Wei and Qirui Zhang

Drones 2026, 10(3), 186; https://doi.org/10.3390/drones10030186 - 9 Mar 2026

Abstract

Addressing the challenges of cooperative navigation for unmanned aerial vehicles (UAVs) in dynamic unknown environments, this paper proposes a collaborative method based on Joint Cognition and Risk Perception (JCRP). The method employs a sequential cooperative framework, where a pioneer UAV constructs a transferable [...] Read more.

Addressing the challenges of cooperative navigation for unmanned aerial vehicles (UAVs) in dynamic unknown environments, this paper proposes a collaborative method based on Joint Cognition and Risk Perception (JCRP). The method employs a sequential cooperative framework, where a pioneer UAV constructs a transferable environmental map, while successor UAVs integrate this prior knowledge with real-time perceptions to form a joint cognitive representation. A dynamic trust mechanism quantitatively evaluates cognitive reliability, enabling risk-aware path planning that balances safety and efficiency. Simulations and physical experiments demonstrate that JCRP reduces the path length of follower UAVs by approximately 41.39% and improves the safe decision ratio by 10.9 percentage points over baseline methods. These results validate the method’s robustness in complex scenarios, such as maze-like environments, highlighting its potential for applications in search-and-rescue. Full article

(This article belongs to the Section Artificial Intelligence in Drones (AID))

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24 pages, 4915 KB

Open AccessArticle

Semantic-Guided Matching of Heterogeneous UAV Imagery and Mobile LiDAR Data Using Deep Learning and Graph Neural Networks

by Tee-Ann Teo, Hao Yu and Pei-Cheng Chen

Drones 2026, 10(3), 185; https://doi.org/10.3390/drones10030185 - 8 Mar 2026

Abstract

The integration of heterogeneous geospatial data, specifically low-cost unmanned aerial vehicle (UAV) imagery and mobile light detection and ranging (LiDAR) system point clouds, presents a significant challenge due to the significant radiometric and structural discrepancies between the two modalities. This study proposes a [...] Read more.

The integration of heterogeneous geospatial data, specifically low-cost unmanned aerial vehicle (UAV) imagery and mobile light detection and ranging (LiDAR) system point clouds, presents a significant challenge due to the significant radiometric and structural discrepancies between the two modalities. This study proposes a novel air-to-ground semantic feature matching framework to achieve precise geometric registration between these data sources by effectively incorporating semantic-constraint deep learning-based matching. The methodology transformed the cross-sensor alignment challenge into a robust two-dimensional image matching problem. This was achieved by first using YOLOv11 for semantic segmentation of common road markings in both the UAV orthoimage and the converted LiDAR intensity image to generate highly consistent feature references. Subsequently, the SuperPoint detector and a graph neural network matcher, SuperGlue, were applied to these semantic images to establish reliable geomatics information correspondence points. Experimental results confirmed that this semantic-guided strategy consistently outperformed traditional feature-based matching (i.e., scale-invariant feature transform + fast library for approximate nearest neighbors), particularly by converting the noisy LiDAR intensity image into a stabilized semantic representation. The explicit application of semantic constraints further proved effective in eliminating false matches between geometrically similar but semantically distinct objects. The final object-specific analysis demonstrated that features with clear, complex geometric structures (e.g., pedestrian crossings and directional arrows) provide the most robust matching control. In summary, the proposed framework successfully leverages semantic context to overcome cross-sensor heterogeneity, offering an automated and precise solution for the geometric alignment of mobile LiDAR data. Full article

(This article belongs to the Special Issue When Deep Learning Meets Geometry for Air-to-Ground Perception on Drones: 2nd Edition)

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23 pages, 1824 KB

Open AccessArticle

Multi-Agent Deep Reinforcement Learning for Coding-Aware and Energy-Balanced Routing in Dynamic Drone Networks

by Yuhao Wu, Xiulin Qiu, Bo Song, Yaqi Ke, Lei Xu and Yuwang Yang

Drones 2026, 10(3), 184; https://doi.org/10.3390/drones10030184 - 8 Mar 2026

Abstract

By incorporating opportunistic coding, network throughput is enhanced, resulting in improved overall performance. However, applying this paradigm to Flying Ad-hoc Networks (FANETS) faces significant challenges due to the highly dynamic topology caused by the high-velocity mobility of UAVs, alongside the NP-hard complexity of [...] Read more.

By incorporating opportunistic coding, network throughput is enhanced, resulting in improved overall performance. However, applying this paradigm to Flying Ad-hoc Networks (FANETS) faces significant challenges due to the highly dynamic topology caused by the high-velocity mobility of UAVs, alongside the NP-hard complexity of identifying optimal coding opportunities in rapidly evolving aerial network architectures. To address these challenges, this paper proposes a novel coding-aware routing protocol based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG). We formulate the routing problem as a multi-agent continuous decision-making process, employing the MADDPG algorithm to optimize routing policies in real-time through decentralized execution and centralized training. To maximize network utility, we design a comprehensive reward function that integrates coding benefits, throughput, energy distribution, and end-to-end delay, ensuring a balance between throughput maximization and the energy sustainability of individual UAV nodes. Simulation results demonstrate that the proposed protocol significantly outperforms state-of-the-art coding-aware routing protocols in terms of throughput, Packet Delivery Ratio (PDR), and transmission delay, exhibiting superior robustness in highly dynamic FANET scenarios. Notably, at a network density of 20 UAVs, MARL-CAR outperforms COPE, DCAR, TSCAR, and RLCAR in terms of coding ratio by 32.23%, 18.93%, 20.35%, and 5.5%, respectively. This research provides a scalable and intelligent networking solution for the next generation of autonomous UAV swarms and collaborative aerial missions. Full article

(This article belongs to the Section Drone Communications)

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19 pages, 3307 KB

Open AccessArticle

Towards Autonomous Powerline Inspection: A Real-Time UAV-Edge Computing Framework for Early Identification of Fire-Related Hazards

by Shuangfeng Wei, Yuhang Cai, Kaifang Dong, Chuanyao Liu, Fan Yu and Shaobo Zhong

Drones 2026, 10(3), 183; https://doi.org/10.3390/drones10030183 - 6 Mar 2026

Abstract

Transmission lines traversing forested areas pose significant fire risks, necessitating timely and efficient inspection mechanisms. Traditional manual patrols and cloud-based UAV inspections suffer from high latency, bandwidth dependence, and delayed response times. To address these challenges, this study proposes an integrated, real-time UAV-edge [...] Read more.

Transmission lines traversing forested areas pose significant fire risks, necessitating timely and efficient inspection mechanisms. Traditional manual patrols and cloud-based UAV inspections suffer from high latency, bandwidth dependence, and delayed response times. To address these challenges, this study proposes an integrated, real-time UAV-edge computing system for the early identification of fire risks and structural hazards along transmission corridors. The system integrates a DJI M300 RTK UAV with a Manifold 2-G edge computing unit (based on NVIDIA Jetson TX2), deploying a lightweight, TensorRT-optimized YOLOv8 model. By leveraging FP16 precision quantization and operator fusion, the system achieves a real-time inference speed of 32 FPS on the embedded platform. Furthermore, a custom Payload SDK integration ensures automated image acquisition and closed-loop data transmission via a dual-mode (4G/5G + Wi-Fi) communication link. Field experiments demonstrate that the system significantly reduces data transmission latency while maintaining high detection accuracy (mAP > 94%), providing a robust and replicable solution for intelligent power grid maintenance in resource-constrained environments. Full article

(This article belongs to the Special Issue Unmanned Aerial Vehicles for Enhanced Emergency Response)

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36 pages, 3837 KB

Open AccessArticle

A Theoretical Framework for Event-Driven Correction in UAV Swarm Situational Awareness: Mechanism Design with Evidence-Theoretic Foundations

by Haotian Yu, Xin Guan and Lang Ruan

Drones 2026, 10(3), 182; https://doi.org/10.3390/drones10030182 - 6 Mar 2026

Abstract

The effectiveness of unmanned aerial vehicle (UAV) swarms in complex and dynamic environments relies heavily on real-time and consistent situational awareness throughout the network. Effective event-driven correction mechanisms must meet two essential requirements: they must robustly handle uncertainties inherent in challenging situations and [...] Read more.

The effectiveness of unmanned aerial vehicle (UAV) swarms in complex and dynamic environments relies heavily on real-time and consistent situational awareness throughout the network. Effective event-driven correction mechanisms must meet two essential requirements: they must robustly handle uncertainties inherent in challenging situations and ensure strict commutativity between weighting and fusion operations to allow for distributed implementation. To tackle the critical issue of uncertain information processing, this work adopts Dempster–Shafer evidence theory because of its advantages in representing and managing epistemic uncertainty. However, the traditional discounting operation in evidence theory does not satisfy commutativity with the combination rule, which poses a significant barrier to distributed implementation. To address this limitation, we introduce a novel evidence weakening operation that is rigorously proven to be commutative with Dempster’s combination rule. This theoretical advancement enables the design of a distributed protocol that supports efficient propagation and parallel computation of corrections. Simulation results demonstrate that the proposed protocol achieves a zero correction error rate, along with approximately 40% reduction in latency and 35% savings in communication overhead compared to conventional serial discounting methods, while maintaining sublinear scalability. This approach provides a feasible solution for robust and efficient information fusion in dynamic multi-agent systems. Full article

(This article belongs to the Section Artificial Intelligence in Drones (AID))

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15 pages, 1851 KB

Open AccessArticle

First Attempts to Control Forest Pests Using Multi-Rotor Unmanned Aerial Spraying Systems (UASSs) in Forest Ecosystems

by Marius Paraschiv, Andrei Buzatu, Cosmin Paraschivoiu and Dănuț Chira

Drones 2026, 10(3), 181; https://doi.org/10.3390/drones10030181 - 6 Mar 2026

Abstract

Large-scale forest pest management has traditionally relied on aerial spraying; however, increasing regulatory restrictions and environmental concerns have limited its application in many regions. Unmanned Aerial Spraying System (UASS) platforms for aerial spraying have developed intensively in the last decade for pesticide application [...] Read more.

Large-scale forest pest management has traditionally relied on aerial spraying; however, increasing regulatory restrictions and environmental concerns have limited its application in many regions. Unmanned Aerial Spraying System (UASS) platforms for aerial spraying have developed intensively in the last decade for pesticide application in agricultural crops but remain scarcely explored within the forestry sector. This study evaluates the feasibility of UASS-based spraying platforms for forest pest control. We tested a multi-rotor agricultural UASS in two different forest conditions: broadleaf and conifer stands. Both biological and synthetic insecticides were sprayed against two contrasting forest pests, Lymantria dispar and Adelges laricis. Defoliation and infestation intensity were used to assess treatment efficacy post-application. Results indicated differences in operational productivity between forest stand types, with higher treatment efficacy observed for L. dispar. Despite the correct dosage delivered by the UASS, the target organism showed a limited biological response in the conifer pest. In conclusion the use of UASSs in forest ecosystems is conditioned by forest-specific factors; however, these technologies show potential to be aligned with interventions targeting early-stage pest outbreaks. Full article

(This article belongs to the Special Issue The Role of UAVs in Modern Agriculture: Precision Spraying and Crop Health Analysis)

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25 pages, 1793 KB

Open AccessArticle

Computing Efficiency Optimization for UAV-Enabled Integrated Sensing, Computing, and Communication: A Memory-Based Deep Reinforcement Learning Approach

by Honghao Qi and Muqing Wu

Drones 2026, 10(3), 180; https://doi.org/10.3390/drones10030180 - 6 Mar 2026

Abstract

Unmanned aerial vehicles (UAVs) have emerged as a promising platform for supporting integrated sensing, computing, and communication (ISCC) functionality in Internet of Things (IoT) applications. This paper investigates a UAV-enabled ISCC network, where the UAV performs radar sensing and onboard edge computing with [...] Read more.

Unmanned aerial vehicles (UAVs) have emerged as a promising platform for supporting integrated sensing, computing, and communication (ISCC) functionality in Internet of Things (IoT) applications. This paper investigates a UAV-enabled ISCC network, where the UAV performs radar sensing and onboard edge computing with the computational assistance of ground access points (APs). Given the limited onboard energy, ensuring energy-efficient operation of UAVs is crucial to support the long-term sustainability of network performance. In this paper, we define computing efficiency as the ratio between the total number of successfully processed computational bits and the overall UAV energy consumption, under the constraint of a required sensing threshold. To maximize this performance metric, this paper jointly optimizes the beamforming vector, the CPU frequency, and the trajectory of the UAV. This optimization problem is modeled as a Markov decision process (MDP) and solved using a deep reinforcement learning (DRL) approach based on a memory mechanism. Specifically, a long short-term memory (LSTM) and twin delayed deep deterministic policy gradient (TD3)-based trajectory design and resource allocation (LTTDRA) algorithm is proposed. LSTM units are integrated into the actor and critic to effectively capture the temporal correlations in dynamic environments, thereby enhancing policy stability and accelerating algorithm convergence. The reward function is meticulously designed to alleviate sparse-penalty effects and learn high-performance strategies in complex environments with multiple constraints. Extensive simulations are conducted under various settings and network scenarios, and the results consistently indicate that the proposed approach substantially outperforms the baseline schemes. Full article

(This article belongs to the Special Issue Advances in UAV Networks Towards 6G)

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23 pages, 7885 KB

Open AccessArticle

UAV Cooperative Search Method Based on Asynchronous Collaborative Hybrid Architecture Under Urban Communication Constraints

by Xiefang Lin, Tingting Bai, Xiqiang Liu and Yong Liu

Drones 2026, 10(3), 179; https://doi.org/10.3390/drones10030179 - 5 Mar 2026

Abstract

Cooperative searches by unmanned aerial vehicles (UAVs) have wide applications in urban environments. However, the dense obstacles and limited communication networks in urban settings often lead to repeated searches and inefficient information sharing among UAVs. To address these challenges, this article proposes a [...] Read more.

Cooperative searches by unmanned aerial vehicles (UAVs) have wide applications in urban environments. However, the dense obstacles and limited communication networks in urban settings often lead to repeated searches and inefficient information sharing among UAVs. To address these challenges, this article proposes a novel cooperative strategy named the Asynchronous Collaborative Hybrid Architecture (ACHA), which is tailored for urban flight. Specifically, a digital pheromone mechanism is devised to create artificial potential field to guide UAVs’ search efficiently within local communication constraints. Moreover, UAVs switch between the dual decision mode, namely Chain-Following Mode (CFM) and Tree Expansion Mode (TEM) based on the urban environmental topology. When UAVs arrive at a bifurcation node, the TEM is activated, asynchronously triggering the Collaborative-aware Pruning Search Tree (CPT) algorithm to generate subsequent paths, after which they switch back to CFM. Theoretically, it is demonstrated that the collaborative-aware pruning scheme can avoid the “cooperative benefit trap”, where there is a significant divergence between the actual and predicted cooperative benefits. The simulation results confirm that the proposed method outperforms existing approaches in terms of cooperative search accuracy, collision risk and convergence speed in complex urban search scenarios. Full article

(This article belongs to the Special Issue UAV Swarm Intelligent Control and Decision-Making)

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53 pages, 2913 KB

Open AccessArticle

SORA 2.5-Guided BVLOS UAS for Wildlife Conservation in Kenya: Reducing Friction Between Safety and Field Operations

by Guy Maalouf, Thomas Stuart Richardson, David Roy Guerin, Matthew Watson, Ulrik Pagh Schultz Lundquist, Blair R. Costelloe, Elzbieta Pastucha, Saadia Afridi, Edouard George Alain Rolland, Kilian Meier, Jes Hundevadt Jepsen, Thomas van der Sterren, Lucie Laporte-Devylder, Camille Rondeau Saint-Jean, Constanza Andrea Molina Catricheo, Vandita Shukla, Elena Iannino, Jenna Kline, Dat Nguyen Ngoc, William Njoroge and Kjeld Jensen Show full author list Hide full author list

Drones 2026, 10(3), 178; https://doi.org/10.3390/drones10030178 - 5 Mar 2026

Abstract

Safe Beyond Visual Line of Sight (BVLOS) operations are increasingly required for wildlife monitoring and conservation, yet existing regulatory frameworks are rarely tailored to protected areas characterised by low population density and limited infrastructure. This paper presents a field-based use case illustrating how [...] Read more.

Safe Beyond Visual Line of Sight (BVLOS) operations are increasingly required for wildlife monitoring and conservation, yet existing regulatory frameworks are rarely tailored to protected areas characterised by low population density and limited infrastructure. This paper presents a field-based use case illustrating how the Specific Operations Risk Assessment (SORA) methodology can be applied to conservation-oriented BVLOS missions under Kenyan airspace conditions, including coordination within military-controlled airspace. We evaluate three population-density estimation approaches (qualitative, bottom-up, and top-down) against available ground truth, and compare tabulated and analytical SORA methods for deriving the Ground Risk Class. The work illustrates how SORA 2.5 structures ground and air risk reasoning in a conservation context, while retrospective review identifies limitations in containment, Operational Safety Objectives, and tactical mitigation performance requirements. Field trials involved five concurrent teams and 30 personnel conducting over 260 flights and more than 60 h of UAS activity across the Ol Pejeta Conservancy, providing insights into multi-team coordination under field conditions. Field implementation revealed areas of misalignment between prescribed safety requirements and operational realities, prompting iterative adaptation of workflows and procedures. Observed outcomes included reductions in team size (25–50%) and procedural steps (18%), derived from retrospective comparison of field procedures. A lightweight Uncrewed Traffic Management prototype was also trialled, revealing practical limitations in conservancy environments. Finally, we present a ten-step framework for developing field-ready safety procedures to support risk-informed decision-making in non-standard operational contexts. The findings provide empirically grounded guidance on applying SORA principles to conservation UAS missions, without proposing a new risk framework or generalised operational model. Full article

(This article belongs to the Special Issue UAVs for Nature Conservation Tasks in Complex Environments)

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24 pages, 4793 KB

Open AccessArticle

Safe Swarm Navigation in Constrained Environments: A Dynamic Tube-Based Distributed MPC Approach

by Xunhua Dai, Yuting Liao and Yong Chen

Drones 2026, 10(3), 177; https://doi.org/10.3390/drones10030177 - 5 Mar 2026

Abstract

Navigating large-scale Unmanned Aerial Vehicle (UAV) swarms through restricted airspaces requires a delicate balance between rigorous safety guarantees and high mission throughput. To address the limitations of rigid tube structures and over-constrained optimization problems, we present a Dynamic Tube-based Distributed Model Predictive Control [...] Read more.

Navigating large-scale Unmanned Aerial Vehicle (UAV) swarms through restricted airspaces requires a delicate balance between rigorous safety guarantees and high mission throughput. To address the limitations of rigid tube structures and over-constrained optimization problems, we present a Dynamic Tube-based Distributed Model Predictive Control (DMPC) approach. Unlike traditional methods, our framework introduces an elastic tube reconstruction mechanism that adaptively shifts boundaries to accommodate dynamic obstacles while maintaining a connected navigable tube. By integrating a predictive risk-triggered constraint activation policy, the proposed controller avoids the curse of over-conservativeness, ensuring recursive feasibility in high-density scenarios. Quantitative results indicate that our method reduces redundant computations while maximizing workspace utilization, providing a scalable solution for future Urban Air Mobility infrastructures (UAM). Full article

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17 pages, 1647 KB

Open AccessArticle

Field-Validated Drone-Based Precision Control of the Invasive Apple Snail (Pomacea canaliculata) in Rice Paddy Fields: Chemical Reduction and Yield Preservation

by Senlin Guan, Kimiyasu Takahashi, Shuichi Watanabe, Koichiro Fukami, Hiroyuki Obanawa and Keita Ono

Drones 2026, 10(3), 176; https://doi.org/10.3390/drones10030176 - 5 Mar 2026

Abstract

Apple snail infestation poses a persistent threat to rice production in open-field environments, where long-term coexistence with this species is unavoidable. This study presents a drone-based precision control approach that integrates high-resolution micro-topographic mapping with site-specific pesticide application. A lightweight mapping unmanned aerial [...] Read more.

Apple snail infestation poses a persistent threat to rice production in open-field environments, where long-term coexistence with this species is unavoidable. This study presents a drone-based precision control approach that integrates high-resolution micro-topographic mapping with site-specific pesticide application. A lightweight mapping unmanned aerial vehicle was deployed to produce centimeter-level microtopographic data across paddy fields, facilitating the identification of deep-water areas preferred by apple snails. From these elevation-derived water risk patterns, prescription maps were generated to guide downstream management decisions, and agricultural drones equipped for granular application subsequently performed targeted pesticide delivery only in these high-risk areas. Over 2 years of field experiments, the proposed method achieved rice yields comparable to those under conventional management while reducing pesticide use by 44.1–63.0%, with lower estimated crop damage in regions with high apple snail occurrence. Designed with robustness and scalability in mind, the system demonstrated considerable potential for practical implementation in general farming households and broader applications in precision pest management. Full article

(This article belongs to the Special Issue Drones in Pest Management and Plant Disease Detecting: State of the Art, Achievements and Perspectives)

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20 pages, 17849 KB

Open AccessArticle

UAV–UGV Collaborative Localization in GNSS-Denied Large-Scale Environments: An Anchor-Free VIO–UWB Fusion with Adaptive Weighting and Outlier Suppression

by Haoyuan Xu, Gaopeng Zhao and Yuming Bo

Drones 2026, 10(3), 175; https://doi.org/10.3390/drones10030175 - 4 Mar 2026

Abstract

In GNSS-denied large-scale outdoor environments, UAVs and UGVs that rely solely on visual–inertial odometry (VIO) suffer from accumulated global drift as the trajectory grows. Meanwhile, inter-platform ultra-wideband (UWB) ranging exhibits unknown, time-varying noise under NLOS/multipath, rendering naïve weighting unreliable. This paper presents an [...] Read more.

In GNSS-denied large-scale outdoor environments, UAVs and UGVs that rely solely on visual–inertial odometry (VIO) suffer from accumulated global drift as the trajectory grows. Meanwhile, inter-platform ultra-wideband (UWB) ranging exhibits unknown, time-varying noise under NLOS/multipath, rendering naïve weighting unreliable. This paper presents an anchor-free collaborative localization framework for UAV–UGV teams that fuses pairwise UWB ranges (including UAV–UAV, UAV–UGV, and UGV–UGV) with onboard VIO in a factor-graph backend via a two-stage robust scheme. First, we bound VIO drift using per-agent state covariance and reject UWB outliers with a Mahalanobis gate, preventing early-stage bias when VIO is still accurate. Then, during global optimization, we adaptively estimate the Fisher information of UWB factors from measurement–state residuals, enabling online self-tuning of measurement confidence under time-varying SNR. Real-world experiments with three UAVs and two UGVs over multi-level rooftops and forest–open areas (~1.6 km²) show that, compared to an outlier-only variant, the proposed method further reduces localization RMSE by about 24.6% and maximum error by about 31.2% for both UAVs and UGVs, maintaining strong performance during long trajectories dominated by VIO drift and NLOS ranges. The approach requires no fixed anchors or GNSS and is applicable to UAV–UGV teams for disaster response, cooperative mapping/inspection, and bandwidth-limited operations. Full article

(This article belongs to the Section Artificial Intelligence in Drones (AID))

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