Drones, Volume 8, Issue 12 (December 2024) – 91 articles

The performance of unmanned ground vehicle (UGV) formation is crucial for large-scale material transport. In a non-communicative environment, visual perception plays a central role in formation control. However, due to unstable lighting conditions, dust, fog, and visual occlusions, developing a high-precision visual formation control technology that does not rely on external markers remains a significant challenge in UGVs. This study developed a new UGV formation controller that relies solely on onboard visual sensors and proposed a teacher–student training method, TSTMIPI, combining the PPO algorithm with imitation learning, which significantly improves the control precision and convergence speed of the vision-based reinforcement learning formation controller. To further enhance formation control stability, we constructed a belief state encoder (BSE) based on convolutional neural networks, which effectively integrates visual perception and proprioceptive information. Simulation results show that the control strategy combining TSTMIPI and BSE not only eliminates the reliance on external markers but also significantly improves control precision under different noise levels and visual occlusion conditions, surpassing existing visual formation control methods in maintaining the desired distance and angular precision. Full article

This study proposes a novel framework for detecting and managing non-point-source (NPS) pollution in agricultural areas using unmanned aerial vehicles (UAVs) and geospatial artificial intelligence (GeoAI). High-resolution UAV imagery, combined with the YOLOv8 instance segmentation model, was employed to accurately detect and classify various NPS sources, such as livestock barns, compost heaps, greenhouses, and mulching films. The spatial information, including the area and volume of detected objects, was analyzed to track temporal changes and evaluate management strategies. The framework integrates remote sensing, deep learning, and geographic information system (GIS) analysis to enhance decision-making processes, providing detailed insight into NPS pollution dynamics over time. This approach not only improves the efficiency of NPS monitoring but also facilitates proactive management by offering precise location and environmental impact data. The results indicate that this framework can significantly improve resource allocation and environmental management practices, particularly in agriculture-dominated regions susceptible to NPS pollution, thereby contributing to the sustainable development of these areas. Full article

The increasing use of unmanned aerial vehicles (UAVs) in the commercial and recreational sectors has led to a heightened demand for effective recovery solutions after a crash, particularly for lightweight drones. This paper presents the development of a long-range and low-cost emergency radio beacon designed specifically for small UAVs. Unlike traditional emergency locator transmitters (ELTs), our proposed beacon addresses the unique needs of UAVs by reducing size, weight, and cost, while maximizing range and power efficiency. The device utilizes a global system for mobile (GSM)-based communication module to transmit location data via short message service (SMS), eliminating the need for specialized receivers and expanding the operational range even in obstacle-rich environments. Additionally, a built-in global navigation satellite system (GNSS) receiver provides precise coordinates, activated only upon impact detection through an accelerometer, thereby saving power during normal operations. Experimental tests confirm the extended range, high precision, and compatibility of the prototype with common mobile networks. Cost-effective and easy to use, this beacon improves UAV recovery efforts by providing reliable localization data to users in real time, thus safeguarding the UAV investment. Full article

Area coverage and target tracking are important applications of UAV swarms. However, attempting to perform both tasks simultaneously can be a challenge, particularly under resource constraints. In such scenarios, UAV swarms must collaborate to cover extensive areas while simultaneously tracking multiple targets. This paper proposes a deep reinforcement learning (DRL)-based, scalable UAV swarm control method for a simultaneous coverage and tracking (SCT) task, called the SCT-DRL algorithm. SCT-DRL simplifies the interaction between UAV swarms into a series of pairwise interactions and aggregates the information of perceived targets in advance, based on which forms the control framework with a variable number of neighboring UAVs and targets. Another highlight of SCT-DRL is using the trajectories of the traditional one-step optimization method to initialize the value network, which encourages the UAVs to select the actions leading to the state with less rest time to task completion to avoid extensive random exploration at the beginning of training. SCT-DRL can be seen as a special improvement of the traditional one-step optimization method, shaped by the samples derived from the latter, and gradually overcomes the inherent myopic issue with the far-sighted value estimation through RL training. Finally, the effectiveness of the proposed method is demonstrated through numerical experiments. Full article

While satellite-based remote sensing offers a promising avenue for large-scale LCC estimations, the accuracy of evaluations is often decreased by mixed pixels, attributable to distinct farming practices and diverse soil conditions. To overcome these challenges and to account for maize intercropping with soybeans at different growth stages combined with varying soil backgrounds, a hyperspectral database for maize was set up using a random linear mixed model applied to hyperspectral data recorded by an unmanned aerial vehicle (UAV). Four methods, namely, Euclidean distance, Minkowski distance, Manhattan distance, and Cosine similarity, were used to compare vegetation spectra from Sentinel-2A with the newly constructed database. In a next step, widely used vegetation indices such as NDVI, NAOC, and CAI were tested to find the optimum method for LCC retrieval, validated by field measurements. The results show that the NAOC had the strongest correlation with ground sampling information (R² = 0.83, RMSE = 0.94 μg/cm², and MAE = 0.67 μg/cm²). Additional field measurements sampled at other farming areas were applied to validate the method’s transferability and generalization. Here too, validation results showed a highly precise LCC estimation (R² = 0.93, RMSE = 1.10 μg/cm², and MAE = 1.09 μg/cm²), demonstrating that integrating UAV hyperspectral data with a random linear mixed model significantly improves satellite-based LCC retrievals. Full article

The capability of UAVs for efficient autonomous navigation and obstacle avoidance in complex and unknown environments is critical for applications in agricultural irrigation, disaster relief and logistics. In this paper, we propose the DPRL (Distributed Privileged Reinforcement Learning) navigation algorithm, an end-to-end policy designed to address the challenge of high-speed autonomous UAV navigation under partially observable environmental conditions. Our approach combines deep reinforcement learning with privileged learning to overcome the impact of observation data corruption caused by partial observability. We leverage an asymmetric Actor–Critic architecture to provide the agent with privileged information during training, which enhances the model’s perceptual capabilities. Additionally, we present a multi-agent exploration strategy across diverse environments to accelerate experience collection, which in turn expedites model convergence. We conducted extensive simulations across various scenarios, benchmarking our DPRL algorithm against state-of-the-art navigation algorithms. The results consistently demonstrate the superior performance of our algorithm in terms of flight efficiency, robustness and overall success rate. Full article

Environmental factors and electronic interference often disrupt communication between UAV swarms and ground control centers, requiring UAVs to complete missions autonomously in offline conditions. However, current coordination schemes for UAV swarms heavily depend on ground control, lacking robust mechanisms for offline task allocation and coordination, which compromises efficiency and security in disconnected settings. This limitation is especially critical for complex missions, such as rescue or attack operations, underscoring the need for a solution that ensures both mission continuity and communication security. To address these challenges, this paper proposes an offline task-coordination algorithm based on blockchain smart contracts. This algorithm integrates task allocation, resource scheduling, and coordination strategies directly into smart contracts, allowing UAV swarms to autonomously make decisions and coordinate tasks while offline. Experimental simulations confirm that the proposed algorithm effectively coordinates tasks and maintains communication security in offline states, significantly enhancing the swarm’s autonomous performance in complex, dynamic scenarios. Full article

As a fundamental part of water management, water sampling treatments have recently been integrated into unmanned aerial vehicle (UAV) technologies and offer eco-friendly, cost-effective, and time-saving solutions while reducing the necessity for qualified staff. However, the majority of applications have been conducted with rotary-wing configurations, which lack range and sampling capacity (i.e., payload), leading scientists to search for alternative designs or special configurations to enable more comprehensive water assessments. Hence, in this paper, the conceptual design of a novel long-range and high-capacity WIGE UAV capable of autonomous water sampling is presented in detail. The design process included a vortex lattice solver for aerodynamic investigations, while analytical and empirical methods were used for weight and dimensional estimations. Since the mission involved operation inside maritime traffic, potential obstacle avoidance scenarios were discussed in terms of operational safety, and the aim was for autonomous trajectory tracking performance to be improved by means of a stochastic optimization algorithm. For this purpose, an artificial intelligence-integrated concurrent engineering approach was applied for autonomous control system design and flight altitude determination, simultaneously. During the optimization, the stability and control derivatives of the constituted longitudinal and lateral aircraft dynamic models were predicted via a trained artificial neural network (ANN). The optimization results exhibited an aerodynamic performance enhancement of 3.92%, and a remarkable improvement in trajectory tracking performance for both the fly-over and maneuver obstacle avoidance modes, by 89.9% and 19.66%, respectively. Full article

In modern power systems, drones are increasingly being utilized to monitor the condition of critical power equipment. However, limited computing capacity is a key factor limiting the application of drones. To optimize the computational load on drones, this paper proposes a cloud–edge collaborative intelligence strategy to be applied to insulator identification and defect detection scenarios. Firstly, a low-computation method deployed at the edge is proposed for determing whether insulator strings are present in the captured images. Secondly, an efficient insulator recognition and defect detection method, I-YOLO (Insulator-YOLO), is proposed for cloud deployment. In the neck network, we integrate an I-ECA (Insulator-Enhanced Channel Attention) mechanism based on insulator characteristics to more comprehensively fuse features. In addition, we incorporated the insulator feature cross fusion network (I-FCFN) to enhance the detection of small-sized insulator defects. Experimental results demonstrate that the cloud–edge collaborative intelligence strategy performs exceptionally well in insulator-related tasks. The edge algorithm achieved an accuracy of 97.9% with only 0.7 G FLOPs, meeting the inspection requirements of drones. Meanwhile, the cloud model achieved a $mA{P}_{50}$ of 96.2%, accurately detecting insulators and their defects. Full article

The fault-tolerant time-varying formation (TVF) trajectory tracking control problem is investigated in this paper for uncertain multi-agent systems (MASs) with external disturbances subject to time delays under semi-Markov switching topologies. Firstly, based on the characteristics of actuator faults, a failure distribution model is established, which can better describe the occurrence of the failures in practice. Secondly, switching the network topologies is assumed to follow a semi-Markov stochastic process that depends on the sojourn time. Subsequently, a novel distributed state-feedback control protocol with time-varying delays is proposed to ensure that the MASs can maintain a desired formation configuration. To reduce the impact of disturbances imposed on the system, the $H_{\infty }$ performance index is introduced to enhance the robustness of the controller. Furthermore, by constructing an advanced Lyapunov–Krasovskii (LK) functional and utilizing the reciprocally convex combination theory, the TVF control problem can be transformed into an asymptotic stability issue, achieving the purpose of decoupling and reducing conservatism. Furthermore, sufficient conditions for system stability are obtained through linear matrix inequalities (LMIs). Eventually, the availability and superiority of the theoretical results are validated by three simulation examples. Full article

Unmanned aerial vehicle (UAV) swarms have shown substantial potential to enhance operational efficiency and reduce strike costs, presenting extensive applications in modern urban warfare. However, achieving effective strike performance in complex urban environments remains challenging, particularly when considering three-dimensional obstacles and threat zones simultaneously, which can significantly degrade strike effectiveness. To address this challenge, this paper proposes a target strike strategy using the Electric Eel Foraging Optimization (EEFO) algorithm, a heuristic optimization method designed to ensure precise strikes in complex environments. The problem is formulated with specific constraints, modeling each UAV as an electric eel with random initial positions and velocities. This algorithm simulates the interaction, resting, hunting, and migrating behaviors of electric eels during their foraging process. During the interaction phase, UAVs engage in global exploration through communication and environmental sensing. The resting phase allows UAVs to temporarily hold their positions, preventing premature convergence to local optima. In the hunting phase, the swarm identifies and pursues optimal paths, while in the migration phase the UAVs transition to target areas, avoiding threats and obstacles while seeking safer routes. The algorithm enhances overall optimization capabilities by sharing information among surrounding individuals and promoting group cooperation, effectively planning flight paths and avoiding obstacles for precise strikes. The MATLAB(R2024b) simulation platform is used to compare the performance of five optimization algorithms—SO, SCA, WOA, MFO, and HHO—against the proposed Electric Eel Foraging Optimization (EEFO) algorithm for UAV swarm target strike missions. The experimental results demonstrate that in a sparse undefended environment, EEFO outperforms the other algorithms in terms of trajectory planning efficiency, stability, and minimal trajectory costs while also exhibiting faster convergence rates. In densely defended environments, EEFO not only achieves the optimal target strike trajectory but also shows superior performance in terms of convergence trends and trajectory cost reduction, along with the highest mission completion rate. These results highlight the effectiveness of EEFO in both sparse and complex defended scenarios, making it a promising approach for UAV swarm operations in dynamic urban environments. Full article

As an important branch of path planning, coverage path planning (CPP) is widely used for unmanned aerial vehicles (UAVs) to cover target regions with lower energy consumption. Most current works focus on convex regions, whereas others need pre-decomposition to deal with non-convex or mixed regions. Therefore, it is necessary to pursue a concise and efficient method for the latter. This paper proposes a two-stage method named Shrink-Segment by Dynamic Programming (SSDP), which aims to cover mixed regions with limited energy. First, instead of decomposing and then planning, SSDP formulates an optimal path by shrinking the rings for mixed regions. Second, a dynamic programming (DP)-based approach is used to segment the overall path for UAVs in order to meet energy limits. Experimental results show that the proposed method achieves less path overlap and lower energy consumption compared to state-of-the-art methods. Full article

Electrothermal de-icing systems are a popular approach to protect unmanned aerial vehicles (UAVs) from the performance degradation caused by in-cloud icing. However, their power and energy requirements must be minimized to make these systems viable for small and medium-sized fixed-wing UAVs. Thermal de-icing systems allow intercycle ice accretions and can result in runback icing. Intercycle and runback ice increase the aircraft’s drag, requiring more engine thrust and energy. This study investigates the aerodynamic influence of intercycle and runback ice on a typical UAV wing. Lift and drag coefficients from a wind tunnel campaign and Ansys FENSAP-ICE simulations are compared. Intercycle ice shapes result in a drag increase of approx. 50% for a realistic cruise angle of attack. While dispersed runback ice increases the drag by 30% compared to the clean wing, a spanwise ice ridge can increase the drag by more than 170%. The results highlight that runback ice can significantly influence the drag coefficient. Therefore, it is important to design the de-icing system and its operation sequence to minimize runback ice. Understanding the need to minimize runback ice helps in designing viable de-icing systems for UAVs. Full article

The rapid growth of small Unmanned Aircraft Systems (sUASs) has raised some safety concerns when sUASs enter the national airspace. As sUASs interact with traditional manned aircraft within this airspace, guaranteeing their safe operations has emerged as a top priority for aviation authorities, policymakers, and industry stakeholders. To address this challenge, the Federal Aviation Administration (FAA) has introduced waiver rules, empowering operators to navigate deviations from specific regulations under well-defined circumstances. Additionally, the FAA developed proposed rulemakings to seek input on how to enhance safety and address risks associated with sUAS operations. The primary question is: How do these current waiver rules and rulemakings align with the Safety Management System (SMS), and what changes are needed for better alignment? The main purpose of this paper is to compare the FAA’s sUAS safety requirements, particularly waiver rules and rulemakings, with the SMS’s safety risk management component to identify alignments and gaps between them. A qualitative data analysis was conducted using three FAA waiver trend analyses and five Notice of Proposed Rulemakings (NPRMs) for sUASs. The results revealed that most sUAS waiver rules and rulemakings sufficiently align with the first three components of the SRM framework (system analysis, identify hazards, and analyze safety risk). However, there are significant gaps in the last two components (assess safety risk and control safety risk). The findings of this study make significant contributions to the sUAS safety management literature. They enable both the FAA and sUAS organizations to promote uniform operational protocols, training initiatives, and risk mitigation approaches tailored to sUAS operations. Full article

The application of wireless power transfer (WPT) technology in power replenishment for drones can help to solve problems such as the frequent manual plugging and unplugging of cables. A wireless power replenishment system for drones based on the transmitter design with multiple annular-sector-shaped coils is proposed in this paper, which improves the misalignment tolerance of couplers, enlarges the drone landing area, and reduces the control requirements of drone landing accuracy further. The general analysis model of the proposed transmitter and the numerical calculation method for mutual inductance between energy transceivers are established. Then, the effect of multiple parameters of the proposed transmitter on the variation in mutual inductance is studied. The misalignment tolerance improvement strategy based on the optimization of multiple parameters of the transmitter is investigated. Finally, an experimental prototype of a wireless power replenishment system for drones based on LCC-S compensation topology is designed to validate the theoretical research. Under the same maximum outer radius of 0.20 m and the same mutual inductance fluctuation rate of 5%, compared to single circular transmitter mode, the maximum offset distance of all directions (360 degrees) in the x-y plane is increased from 0.08 m to 0.12 m. As the receiving side position changes, the maximum receiving power and efficiency are 141.07 W and 93.79%, respectively. At the maximum offset position of 0.12 m, the received power and efficiency are still 132.13 W and 91.25%, respectively. Full article

Forests are crucial for biodiversity, climate regulation, and hydrological cycles, requiring sustainable management due to threats like deforestation and climate change. Traditional forest monitoring methods are labor-intensive and limited, whereas UAV LiDAR offers detailed three-dimensional data on forest structure and extensive coverage. This study primarily assesses individual tree segmentation algorithms in two forest ecosystems with different levels of complexity using high-density LiDAR data captured by the Zenmuse L1 sensor on a DJI Matrice 300RTK platform. The processing methodology for LiDAR data includes preliminary preprocessing steps to create Digital Elevation Models, Digital Surface Models, and Canopy Height Models. A comprehensive evaluation of the most effective techniques for classifying ground points in the LiDAR point cloud and deriving accurate models was performed, concluding that the Triangular Irregular Network method is a suitable choice. Subsequently, the segmentation step is applied to enable the analysis of forests at the individual tree level. Segmentation is crucial for monitoring forest health, estimating biomass, and understanding species composition and diversity. However, the selection of the most appropriate segmentation technique remains a hot research topic with a lack of consensus on the optimal approach and metrics to be employed. Therefore, after the review of the state of the art, a comparative assessment of four common segmentation algorithms (Dalponte2016, Silva2016, Watershed, and Li2012) was conducted. Results demonstrated that the Li2012 algorithm, applied to the normalized 3D point cloud, achieved the best performance with an F1-score of 91% and an IoU of 83%. Full article

In order to improve the network performance of multi-unmanned ground vehicle (UGV) systems in urban environments, this article proposes a novel online autonomous motion-control method for the relay UAV. The problem is solved by jointly considering unknown RF channel parameters, unknown multi-agent mobility, the impact of the environments on channel characteristics, and the unavailable angle-of-arrival (AoA) information of the received signal, making the solution of the problem more practical and comprehensive. The method mainly consists of two parts: wireless channel parameter estimation and optimal relay position search. Considering that in practical applications, the radio frequency (RF) channel parameters in complex urban environments are difficult to obtain in advance and are constantly changing, an estimation algorithm based on Gaussian process learning is proposed for online evaluation of the wireless channel parameters near the current position of the UAV; for the optimal relay position search problem, in order to improve the real-time performance of the method, a line search algorithm and a general gradient-based algorithm are proposed, which are used for point-to-point communication and multi-node communication scenarios, respectively, reducing the two-dimensional search to a one-dimensional search, and the stability proof and convergence conditions of the algorithm are given. Comparative experiments and simulation results under different scenarios show that the proposed motion-control method can drive the UAV to reach or track the optimal relay position and improve the network performance, while demonstrating that it is beneficial to consider the impact of the environments on the channel characteristics. Full article

As Urban Air Mobility (UAM) moves toward implementation, managing high-density, high-volume flights in urban airspaces becomes increasingly critical. In such environments, the design of vertiport airspace structures plays a key role in determining how many UAVs can operate safely and efficiently within a specific airspace. Existing studies have not fully explored the complex interdependencies between airspace structure parameters and fleet operation capacity, particularly regarding how various structural components and their configurations affect UAV fleet performance. This paper addresses these gaps by proposing a multi-layered funnel-shaped airspace structure for vertiports, along with an adjustable parameter model to assess factors affecting landing capacity. The proposed design includes the assembly layer, upper layer, lower layer, and approach point, forming the basis for fleet operations, divided into three phases: arrival, approach, and landing. By modeling fleet operations with various constraints and time-based algorithms, simulations have been conducted to analyze the impact of changing airspace structure parametric dimensions on UAV fleet operation capacity. The results reveal that fleet capacity is closely influenced by two limitations: the distance traveled in each phase and the availability of holding points at each layer. These findings provide valuable insights and contribute to future airspace design efforts for UAM vertiports. Full article

As Unmanned Aerial Vehicles (UAVs) are becoming crucial in modern warfare, research on autonomous path planning is becoming increasingly important. The conflicting nature of the optimization objectives characterizes path planning as a multi-objective optimization problem. Current research has predominantly focused on developing new optimization algorithms. Although being able to find the mathematical optimum is important, one also needs to ensure this optimum aligns with the decision-maker’s (DM’s) most preferred solution (MPS). In particular, to align these, one needs to handle the DM’s preferences on the relative importance of each optimization objective. This paper provides a comprehensive overview of all preference handling techniques employed in the military UAV path planning literature over the last two decades. It shows that most of the literature handles preferences by the overly simplistic method of scalarization via weighted sum. Additionally, the current literature neglects to evaluate the performance (e.g., cognitive validity and modeling accuracy) of the chosen preference handling technique. To aid future researchers handle preferences, we discuss each employed preference handling technique, their implications, advantages, and disadvantages in detail. Finally, we identify several directions for future research, mainly related to aligning the mathematical optimum to the MPS. Full article

Legged climbing unmanned ground vehicles (LC-UGVs) possess obstacle avoidance and wall transition capabilities, allowing them to move in 3D environments. Existing navigation methods for legged UGVs are only suitable for ground locomotion rather than 3D space. Although some wall transition methods have been proposed, they are specific to certain legged structures and have not been integrated into the navigation framework in full 3D environments. The planning of collision-free and accessible paths for legged climbing UGVs with any configuration in a 3D environment remains an open problem. This paper proposes a map representation suitable for the navigation planning of LC-UGVs in 3D space, named the Multi-Level Elevation Map (MLEM). Based on this map representation, we propose a universal hierarchical planning architecture. A global planner is applied to rapidly find cross-plane topological paths, and then a local planner and a motion generator based on motion primitives produces accessible paths and continuous motion trajectories. The hierarchical planning architecture equips the LC-UGVs with the ability to transition between different walls, thereby allowing them to navigate through challenging 3D environments. Full article

This study proposes a hierarchical communication framework for drone swarms designed to enhance security and operational efficiency. Leveraging elliptic curve cryptography and space quanta concepts, the model ensures continuous authentication and risk assessment of participating entities. Experimental results demonstrate the framework’s effectiveness in mitigating security risks, achieving reliable communication even in adverse conditions. Key findings include significant improvement in threat detection accuracy and reduced computational overhead, validating the model’s applicability for real-world drone swarm operations. These contributions establish a robust foundation for secure and resilient drone coordination. Full article

The auction mechanism stands as a pivotal distributed solution approach for addressing the task allocation problem in unmanned aerial vehicle (UAV) swarms, with its rapid solution capability well-suited to meet the real-time requirements of aerial mission planning for UAV swarms. Building upon the auction mechanism, this paper proposes a distributed task allocation method for multi-UAV grounded in swarm benefits optimization. The method introduces individual benefit variation to quantify the effect of a task on the benefit of a single UAV, thereby enabling direct optimization of swarm benefit through these individual benefit variations. Within the formulated individual benefit calculation, both the spatial distance between tasks and UAVs and the initial task value along with its temporal decay are taken into account, ensuring a thorough and accurate assessment. Additionally, the method incorporates real-time updates of individual benefits for each UAV, reflecting the dynamic state of task benefit fluctuations within the swarm. Monte Carlo simulation experiments demonstrate that, for a swarm size of 16 UAVs and 80 tasks, the proposed method achieves an average swarm benefit improvement of approximately 2% and 4% over the Consensus-Based Bundle Algorithm (CBBA) and Performance Impact (PI) methods, respectively, thus validating its effectiveness. Full article

Air target intention recognition (ATIR) is critical for unmanned systems in modern air defense operations. Through the analysis of typical air defense combat scenarios, first, the paper defines the intention space and intention parameters of air units based on military experience and domain knowledge. Then, a rule-based agent for unmanned systems for online intention recognition is proposed, with no training, no tagging, and no big data support, which is not only for intention recognition and parameter prediction, but also for formation identification of air targets. The most critical point of the agent is the introduction and application of a thermal distribution grid graph (TDGG) and virtual grid dictionary (VGD), where the former is used to identify the formation information of air targets, and the latter is used to optimize the storage space and simplify the access process for the large-scale and real-time combat information. Finally, to have a performance evaluation and application analysis for the algorithm, we carried out a data instance analysis of ATIR for unmanned systems and an air defense warfare simulation experiment based on a Wargame platform; the comparative experiments with the classical k-means, FCNIRM, and the sector-based forward search method verified the effectiveness and feasibility of the proposed agent, which characterizes it as a promising tool or baseline model for the battlefield situational awareness tasks of unmanned systems. Full article

The study of unmanned aerial vehicle (UAV) coverage path planning is of great significance for ensuring maritime situational awareness and monitoring. In response to the problem of maritime multi-region coverage surveillance in complex obstacle environments, this paper proposes a global path planning method capable of simultaneously addressing the multiple traveling salesman problem, coverage path planning problem, and obstacle avoidance problem. Firstly, a multiple traveling salesmen problem–coverage path planning (MTSP-CPP) model with the objective of minimizing the maximum task completion time is constructed. Secondly, a method for calculating obstacle-avoidance path costs based on the Voronoi diagram is proposed, laying the foundation for obtaining the optimal access order. Thirdly, an improved discrete grey wolf optimizer (IDGWO) algorithm integrated with variable neighborhood search (VNS) operations is proposed to perform task assignment for multiple UAVs and achieve workload balancing. Finally, based on dynamic programming, the coverage path points of the area are solved precisely to generate the globally coverage path. Through simulation experiments with scenarios of varying scales, the effectiveness and superiority of the proposed method are validated. The experimental results demonstrate that this method can effectively solve MTSP-CPP in complex obstacle environments. Full article

In emergency rescue scenarios, drones can be equipped with different payloads as needed to aid in tasks such as disaster reconnaissance, situational awareness, communication support, and material assistance. However, rescue missions typically face challenges such as limited reconnaissance boundaries, heterogeneous communication networks, complex data fusion, high task latency, and limited equipment endurance. To address these issues, an unmanned emergency support system tailored for emergency rescue scenarios is designed. This system leverages 5G edge computing technology to provide high-speed and flexible network access along with elastic computing power support, reducing the complexity of data fusion across heterogeneous networks. It supports the control and data transmission of drones through the separation of the control plane and the data plane. Furthermore, by applying the Tammer decomposition method to break down the system optimization problem, the Global Learning Seagull Algorithm for Gaussian Mapping (GLSOAG) is proposed to jointly optimize the system’s energy consumption and latency. Through simulation experiments, the GLSOAG demonstrates significant advantages over the Seagull Optimization Algorithm (SOA), Particle Swarm Optimization (PSO), and Beetle Antennae Search Algorithm (BAS) in terms of convergence speed, optimization accuracy, and stability. The system optimization approach effectively reduces the system’s energy consumption and latency costs. Overall, our work alleviates the pain points faced in rescue scenarios to some extent. Full article

The advancement of drone technology has underscored the critical need for adaptability and enhanced functionality in unmanned aerial vehicles (UAVs). Morphing quadrotors, capable of dynamically altering their structure during flight, offer a promising solution to extend and optimize the operational capabilities of conventional drones. This paper presents a comprehensive review of current advancements in morphing quadrotor research, focusing on morphing concept, actuation mechanisms and flight control strategies. We examine various active morphing approaches, including the integration of smart materials and advanced actuators that facilitate real-time structural adjustments to meet diverse mission requirements. Key design considerations—such as structural integrity, weight distribution, and control algorithms—are meticulously analyzed to assess their impact on the performance and reliability of morphing quadrotors. Despite their significant potential, morphing quadrotors face challenges related to increased design complexity, higher energy consumption, and the integration of sophisticated control systems. The discussion on challenges and opportunities highlights the necessity for ongoing advancements in morphing quadrotor technologies, particularly in addressing adaptive control problems associated with highly nonlinear and dynamic morphing aircraft systems, and in the potential integration with smart materials. By synthesizing the latest research and outlining prospective directions, this paper aims to serve as a valuable reference for researchers and practitioners dedicated to advancing the field of morphing quadrotor technologies. Full article

Electric short takeoff and landing (eSTOL) aircraft utilize the slipstream generated by distributed propellers to significantly increase the effective lift coefficient and reduce the takeoff and landing distances. By utilizing the blown lift, eSTOL UAVs can achieve similar takeoff and landing site requirements as electric vertical takeoff and landing (eVTOL) UAVs, while having lower takeoff and landing energy consumption and thrust requirements. This research proposes a high-peak-power distributed electric propulsion (DEP) system model and overload design method for eSTOL UAVs to further improve the power and thrust of the propulsion system. The model considers motor temperature factors with the throttle input, which is solved through three-round iterative calculations. The experimental and simulation results indicate that the maximum error of the high-peak-power propulsion unit model without considering temperature is 19.52%, and the maximum error when considering temperature is 1.2%. The propulsion unit ground test indicates that the main factors affecting peak power are the duration of peak power and the temperature limit of the motor. Finally, the effectiveness of the propulsion system model is verified through ground tests and UAV flight tests. Full article

Efficient and effective path planning can significantly enhance the task execution capabilities of UAVs in complex environments. This paper proposes an improved sampling-based path planning algorithm, Bi-APF-RRT*, which integrates an Artificial Potential Field (APF) method with a newly introduced repulsive coefficient and incorporates dynamic step size adjustments. To further improve path planning performance, the algorithm introduces strategies such as dynamic goal biasing, target switching, and region-based adaptive sampling probability. The improved Bi-APF-RRT* algorithm effectively controls sampling direction and spatial distribution during the path search process, avoiding local optima and significantly improving the success rate and quality of path planning. To validate the performance of the algorithm, this paper conducts a comparative analysis of Bi-APF-RRT* against traditional RRT* in multiple simulation experiments. Quantitative results demonstrate that Bi-APF-RRT* achieves a 59.6% reduction in average computational time (from 5.97 s to 2.41 s), a 20.6% shorter path length (from 691.56 to 549.21), and a lower average path angle (reduced from 33.28° to 29.53°), while maintaining a 100% success rate compared to 95% for RRT*. Additionally, Bi-APF-RRT* reduces the average number of nodes in the search tree by 45.8% (from 381.17 to 206.5), showcasing stronger obstacle avoidance capabilities, faster convergence, and smoother path generation in complex 3D environments. The results highlight the algorithm’s robust adaptability and reliability in UAV path planning. Full article

The limited coverage of terrestrial base stations and the limited transmission distance and onboard resources of satellite communications make it difficult to ensure the quality of communication services for marine users by relying only on satellites and terrestrial base stations. In contrast, UAVs, as flexible mobile communication nodes, have the capacity for dynamic deployment and real-time adjustment. They can effectively make up for the communication blind spots of traditional satellites and ground base stations in the marine environment, especially in the vast and unpredictable marine environment. Considering the mobility of maritime users, one can effectively reduce the communication delay and optimize the deployment scheme of UAVs by predicting their sailing trajectories in advance, thus enhancing the communication service quality. Therefore, this paper proposes a communication coverage model based on mobile user route prediction and a UAV dynamic deployment algorithm (RUDD). It aims to optimize the coverage efficiency of the maritime communication network, minimize the communication delay, and effectively reduce the energy consumption of UAVs. In this algorithm, the RUDD algorithm employs a modified Long Short-Term Memory (LSTM) network to predict the maritime user’s trajectory, utilizing its strengths in processing time-series data to provide accurate predictions. The prediction results are then used to guide the Proximal Policy Optimization (PPO) algorithm for the dynamic deployment of UAVs. The PPO algorithm can optimize the deployment strategy in dynamic environments, improve communication coverage, and reduce energy consumption. Simulation results show that the proposed algorithm can complement the existing satellite and terrestrial networks well in terms of coverage, with a communication coverage rate of more than 95%, which significantly improves the communication quality of marine users in areas far from land and beyond the reach of traditional networks, and enhances network reliability and user experience. Full article

The accurate estimation of flow angles is crucial for enhancing flight performance and aircraft safety. Flow angles of fixed-wing small unmanned aerial vehicles (SUAVs) are more vulnerable due to their low airspeed. Current flow angle measurement devices have not been widely implemented in SUAVs due to their substantial cost and size constraints. Moreover, there are no general estimation methods suitable for SUAVs based on their rudimentary sensor suite. This study presents a generalized optimization-assisted filter estimation (OAFE) method for estimating the relative velocity and flow angles of fixed-wing SUAVs based on a standard sensor suite. This OAFE method mainly consists of a cubature Kalman filter and an optimizer. The filter serves as the main loop with which to generate flow angles in real time by fusing the acceleration, angular rate, attitude, and airspeed. Without flow angle measurements, the optimizer generates approximate aerodynamic derivatives, which serve as pseudo-measurements with which to refine the performance of the filter. The results demonstrate that the estimated angle of attack and side slip angle displayed root mean square errors of around 0.11° and 0.24° in the simulation. The feasibility was also verified in field tests. The OAFE method does not require flow angle measurements, the prior acquisition of aerodynamic parameters, or model training, making it suitable for quick deployment on different SUAVs. Full article