Found 29 results
Open Access
Review
09 July 2026Green Unmanned Aerial Vehicles: A Review of Hydrogen Fuel Cell and Solar-Powered Drones
Unmanned aerial vehicles (UAVs) are increasingly used in applications such as agriculture, logistics, mapping, surveillance, and environmental monitoring. However, the limited battery endurance continues to restrict mission duration and operational range. This review examines two sustainable propulsion alternatives, hydrogen fuel cells and solar-powered systems, based on findings reported in the literature. Evidence from peer-reviewed studies, experimental demonstrations, and industrial reports published between 2009 and 2024 is considered. Key parameters, including endurance, payload capacity, and operational altitude, are compared, along with practical aspects such as hydrogen storage, thermal management, and energy control systems. The available data suggest that hydrogen fuel cell (HFC) drones are better suited for low to mid-altitude missions requiring higher payload and rapid refueling. Solar-powered drones are more effective for long-endurance and high-altitude applications under favorable solar conditions. Future developments are expected to focus on hybrid propulsion systems, improved materials, and more efficient energy management strategies.
Open Access
Article
16 June 2026Modeling and Simulation of Express Delivery Using Quadrotor UAVs
In this study, the trajectory planning and control problem for quadrotor unmanned aerial vehicles (UAVs) in logistics-oriented delivery scenarios. A smooth trajectory generation method based on spline curves is proposed to ensure continuous, stable, and feasible flight paths for quadrotor UAVs under dynamic constraints. The proposed method focuses on general UAV path planning rather than a specific express delivery optimization problem. A simulation platform is developed to evaluate the effectiveness of the trajectory planning and control framework, where a PID-based controller is implemented for trajectory tracking and attitude stabilization. The performance of the proposed method is validated through two representative emergency delivery scenarios, demonstrating accurate path tracking and stable flight behavior under environmental disturbances. The results indicate that the proposed framework provides a reliable simulation tool for UAV trajectory planning and control analysis, contributing to general UAV motion planning research rather than a specific delivery optimization formulation.
Open Access
Article
12 May 2026Detecting and Mapping Stoebe vulgaris Post Herbicide Control, Using UAV Imagery and Machine Learning
Stoebe vulgaris is a declared indigenous bush encroacher species in South Africa. It has invaded over 11 million ha of grasslands. It is commonly called bankrupt bush due to its ability to outcompete other indigenous forb and grass species, decreasing grazing capacity, biodiversity, and ecosystem functioning, eventually leading to financial ruin for farmers. Landowners are legally required to control the plant. A herbicide trial was set up in a severely encroached camp at Dundee Research Station in KwaZulu-Natal, South Africa, to test the effectiveness of metsulfuron-methyl (50 g active ingredient ha−1) in controlling S. vulgaris. Applying metsulfuron-methyl provided a significant long-term reduction in S. vulgaris cover over six years. However, effective monitoring and management strategies depend on knowledge of the spatial distribution and expansion patterns of invasive species. We evaluated the ability of UAV-based imagery and machine learning, using Picterra, to detect and map S. vulgaris, while determining the optimal parameters to maximise detection accuracy. The best season for image acquisition was late summer when vegetation was at peak growth and maturity, providing the best spectral distinction between species, under light overcast and mild wind conditions. We recommend careful consideration of the flight orientation to the solar angle. We achieved 92% detector accuracy, with multispectral imagery enhancing the discrimination of similarly coloured plants. Plants smaller than 10 cm were not detected by the model. Our approach, using high-resolution drone imagery and AI, is capable of individual plant detection suited to a farm scale. This opens the way for using advances in drone technology for targeted, spot-application of herbicide.
Open Access
Article
08 April 2026Dogfight Simulation of Autonomous Swarm UAVs Based on Multi-Agent Deep Reinforcement Learning
The operational utility of Unmanned Aerial Vehicles (UAVs) has evolved from passive surveillance to active engagement in disputed environments, where autonomous control must operate under highly dynamic and adversarial conditions. Hand-crafted heuristics often exhibit limited robustness when facing stochastic opponent behavior and non-stationary interactions. To address these challenges, we propose a Multi-Agent Deep Reinforcement Learning (MADRL) framework implemented in a Unity 6–based, physics-driven simulation that models flight dynamics and weapon kinematics. Agents are trained using Proximal Policy Optimization (PPO) with a composite reward function designed to encourage cooperative behaviors (e.g., coordinated target engagement) while enforcing safety constraints such as collision avoidance. In empirical evaluations, the learned policies achieve an 85% win rate against a heuristic baseline under the tested scenarios, exhibiting coordinated maneuvers and adaptive engagement strategies. These results indicate that multi-agent learning with decentralized execution can reduce operator workload and improve swarm effectiveness and survivability in conflict zone.
Open Access
Article
11 March 2026Cost-Aware UAV Photogrammetric Mission Design: Experimental Trade-Offs Between Overlap, Geometry, and Mapping Quality
Unmanned Aerial Vehicle (UAV) photogrammetry enables high-resolution mapping and 3D reconstruction, yet operational and processing costs often scale rapidly with conservative mission designs (e.g., high overlap and redundant geometries). This paper presents an experimentally validated, cost-aware network-design study that quantifies cost–quality trade-offs in urban UAV photogrammetry. Five mission strategies—reduced sidelap with increased endlap, cross-flight compensation, partial high-overlap calibration, multi-altitude acquisition, and oblique cross-flight integration—are evaluated using a controlled experimental campaign over two urban test areas (2 × 20 ha), comprising 98 test blocks with overlaps ranging from 60% to 95%, sidelap from 20% to 80%, image counts from 70 to 2961, 7 check points, 15–17 ground control points, and GSD values between 2.6 cm and 4.6 cm, including nadir, oblique, cross-flight, and multi-altitude imagery. Each configuration is assessed using three indicators: (i) cost (flight and processing cost proxies), (ii) completeness, quantified by the number of reconstructed tie points, and (iii) accuracy, defined as a combined image–ground error at check points. Results show that cost reductions of over 50% in both flight and processing proxies can be achieved under the tested conditions while maintaining checkpoint accuracy comparable to a high-overlap reference configuration, provided that reduced overlap is compensated by stronger network geometry (e.g., cross-flight and/or oblique views). The analysis highlights product-dependent recommendations: vector map (MAP) generation can remain reliable even with very low sidelap (down to approximately 20%) when supported by adequate longitudinal overlap, whereas ortho-image mosaic (OIM) production requires at least moderate overlap in both directions (typically ≥60% endlap and sidelap) to ensure radiometric and geometric consistency. In contrast, dense 3D mesh reconstruction demands substantially stronger network geometry, including cross-flight and oblique imagery in addition to nadir views, with overlap levels exceeding 60% and preferably approaching 80%. These findings provide practical mission-planning guidelines that support efficient autonomous and semi-autonomous UAV mapping workflows.
Open Access
Article
05 March 2026Light-Guided Autonomous Drone Navigation for Indoor GPS-Denied Environments
Autonomous drones operating in indoor environments cannot rely on the global positioning system (GPS) signals for precise navigation due to severe signal attenuation and multipath interference in GPS-denied spaces. This paper presents a novel Li-Fi-based optical positioning, and combined with high-sensitivity photodiode sensor arrays, to enable robust drone guidance in challenging indoor environments where conventional radio-frequency localization fails. The proposed system uses strategically distributed ceiling-mounted Light Emitting Diode (LED) luminaires across the operational space, each transmitting unique identification codes through high-frequency light modulation at rates imperceptible to human vision, thereby maintaining dual functionality for simultaneous illumination and positioning. Unlike existing VLC positioning studies that focus on static receivers, our system integrates real-time optical localization directly into the UAV control loop at 120 Hz, achieving closed-loop autonomous navigation without GPS or RF assistance. The system demonstrates sub-decimetric positioning accuracy (<8 cm), low latency (4.2 ms), and operates successfully on resource constrained micro-UAV platforms (250 g quadcopter with STM32 microcontroller. OpenELAB Technology Ltd., Garching bei München, Germany). Experimental validation includes complex 3D trajectory tracking, multi-room scalability analysis, and quantitative comparison with existing localization technologies, confirming the viability of Li-Fi guided autonomous flight for practical indoor application.
Open Access
Article
04 March 2026Impact of Urban Topography and Infrastructure on Air Pollution Dispersion Using UAV-Based AQI Systems
Urban air quality reflects the combined effects of topography, built form, and emission sources, producing pronounced spatial and temporal variability in pollutant dispersion. This study investigates how urban morphological features-building density, green-space distribution, and transportation corridors-shape these dispersion patterns by deploying unmanned aerial vehicles (UAVs) equipped with Air Quality Index (AQI) sensors. Multi-altitude, high-resolution drone transects were conducted across contrasting urban settings to capture fine-scale pollutant distributions and their dynamics. The measurements reveal localized hotspots and zones of limited dispersion that align with variations in building layout, vegetation presence, and traffic intensity. Compared with fixed-site monitors, the UAV approach resolves vertical and horizontal gradients that are otherwise missed, providing complementary evidence of three-dimensional micro-scale heterogeneity. Taken together, the results indicate that decisions on urban design and infrastructure placement materially influence air-quality outcomes. These findings support the integration of UAV-based observations with conventional monitoring networks to inform targeted mitigation measures, exposure-aware mobility planning, and evidence-based strategies for public health and urban sustainability.
Open Access
Article
28 February 2026Euclid sUAV Handling Qualities Evaluation Through Flight Simulation, Using Cooper-Harper Handing Qualities Rating Scale
This article briefly presents the design steps, from the conceptual design up to flight simulation of the Euclid 3D printed small Unmanned Aerial Vehicle (sUAV). The use of valid tools and proper methodology implementation is essential throughout this entire path to render the aircraft’s kinematics properly in the flight simulator. The primary object of study in this article is the Euclid sUAV handling qualities evaluation through flight simulation, using Cooper-Harper Handing Qualities Rating Scale. A novel methodology consisting of eighteen flight tests is presented, each one evaluating a certain flight procedure. For each procedure, performing instructions are provided. This methodology can be used either as is, or modified, to evaluate the handling qualities of similar sUAV’s. Furthermore, a full video of the procedure is given for validation and replication purposes. The results from the application of the 18-step procedure for the Euclid sUAV, indicated that all scores fluctuated in the (1–3) score region. These score region is translated as satisfactory handling qualities, without improvement needed to the system, according to Cooper-Harper Handing Qualities Rating Scale.
Open Access
Article
25 February 2026Ecological Application of UAVs for Monitoring and Eliminating Oil Product Spills on the Sea Surface
The objective of marine ecological safety necessitates the development of comprehensive, integrated strategies for oil spill management, encompassing advanced monitoring and effective remediation. This paper introduces and validates a novel integrated methodology and conceptual framework for autonomous marine environmental safety. The core of this framework lies in the merging of AI-assisted monitoring capabilities with a multi-agent Unmanned Aerial Vehicle (UAV) system for targeted dispersant delivery. UAV systems, within this methodology, function as a cost-effective and readily deployable operational platform. The study details the primary development stages of the methodology-driven system and presents empirical results from in-situ field trials. The framework leverages artificial intelligence (AI) tools developed and validated for slick monitoring, which execute primary segmentation for spill detection and subsequent secondary segmentation to categorize the slick into thickness uniformity maps. Datasets of actual marine oil slick imagery were compiled to facilitate robust deep learning of the underlying neural network architectures. The study explores scientific feasibility, specifically employing Laser-Induced Fluorescence (LIF) spectroscopy to classify oil product grades and assess the ecological impact of various remediation agents on local phytoplankton communities. This integrated method for spill response is underpinned by successful field validation results. The full methodology was tested during actual oil spill incidents in the waters of Peter the Great Bay from 2019 to 2024. The article presents experimental validation of a new concept and methodology of integrated environmental safety of marine areas by a multi-agent UAV system in the event of oil product spills.
Open Access
Article
04 February 2026Analysis of Sensor Locations in Drone Aided Environmental Monitoring System Using Computational Fluid Dynamics (CFD) Studies
Recent advancements in unmanned aerial vehicle (UAV) technology have enabled flexible, high-resolution monitoring of atmospheric CO2, particularly in complex or otherwise inaccessible environments. This study employs Computational Fluid Dynamics (CFD) to investigate the downwash flow field of a quadcopter UAV in hover condition with the objective of identifying low-disturbance regions suitable for accurate atmospheric sensor placement. A quadcopter model was simulated using the SST k-ω turbulence model. Simulations were performed at rotor speeds ranging from 1000 to 6000 rpm. Results show that the strongest downwash and turbulence occur directly beneath the rotors, while airflow above the central fuselage and regions laterally distant from the rotors remain significantly calmer. The findings strongly recommend placing gas sensors either above the drone body or sufficiently far horizontally from the rotor plane to minimize measurement errors caused by propeller-induced flow.
