Search Results (45)

In urban environments, unmanned aerial vehicles (UAVs) can significantly enhance the performance of wireless powered communication networks (WPCNs), enabling reliable communication and efficient energy transfer for urban Internet of Things (IoTs) nodes. However, the complex urban landscape characterized by dense building structures and node distributions severely hampers the efficiency of wireless power transmission. To address this challenge, this paper presents a novel framework for urban WPCN systems assisted by UAVs equipped with reconfigurable intelligent surfaces (UAV-RISs). The framework adopts time division multiple access (TDMA) technology to coordinate the transmission process of information and energy. Considering two TDMA methods, the paper jointly optimizes the flight trajectory of the UAV, the energy harvesting scheduling of ground nodes, and the phase shift matrix of the RIS with the goal of improving the energy efficiency of the system. Furthermore, deep reinforcement learning (DRL) is introduced to effectively solve the formulated optimization problem. Simulation results demonstrate that the proposed optimized scheme outperforms benchmark schemes in terms of average throughput and energy efficiency. Experimental data also reveal the applicability of different TDMA strategies: dynamic TDMA exhibits superior performance in achieving higher average throughput at ground nodes in urban scenarios, while traditional TDMA is more advantageous for total energy harvesting efficiency. These findings provide critical theoretical insights and practical guidelines for UAV trajectory design and communication network optimization in urban environments. Full article

This paper presents an analysis of the flight endurance of solar-powered unmanned aerial vehicles (UAVs). Flight endurance is usually only analyzed under the operating conditions for the location where the UAV was constructed. The fact that these conditions change in a different environment of its operation has been missed. This can be disastrous for those looking to operate such a system under different geographical conditions. This work provides critical insights into the design and operation of solar-powered UAVs for various latitudes, highlighting strategies to maximize their performance and energy efficiency. This work analyzes the endurance of small UAVs designed for practical applications such as shoreline monitoring, agricultural pest detection, and search and rescue operations. The study uses TRNSYS 18 software to employ solar radiation in the power system performance at different latitudes. The results show that flight endurance is highly dependent on solar irradiance. This study confirms that the differences between low latitudes in summer and high latitudes in winter are significant, and this parameter cannot be ignored in terms of planning the use of such vehicles. The findings emphasize the importance of optimizing the balance between UAV mass, solar energy harvesting, and endurance. While the addition of battery mass can enhance endurance, the structural reinforcements required for increased weight may impose practical limitations. The scientific contribution of this work may be useful for both future designers and stakeholders in the operation of such unmanned systems. Full article

The future Internet of Things (IoT) will consist of energy harvesting devices and Unmanned Aerial Vehicles (UAVs) to support applications in remote areas. However, as UAVs communicate with IoT devices using broadcast channels, information leakage emerges as a critical security threat. This paper considers the problem of maximizing the minimum secrecy rate in an energy harvesting IoT network supported by two UAVs, where one acts as a server to collect data from devices, and the other is an eavesdropper to intercept data transmission. It presents a novel Mixed-Integer Nonlinear Program (MINLP), which we then linearize into a Mixed-Integer Linear Program (MILP) problem. It also proposes a heuristic solution called Fly Nearest Location (FNL). Both solutions determine (i) the UAV server’s flight routing, flight time, and computation time, as well as (ii) the energy usage and operation mode of IoT devices. Our results show that FNL achieves on average 78.15% of MILP’s performance. Full article

This paper investigates the energy transfer and data collection problem of multiple unmanned aerial vehicle (UAV)-assisted wireless-powered Internet of Things (IoT) networks. To ensure information freshness for IoT devices and reduce UAVs’ energy consumption, we minimize the average Age of Information (AoI) of IoT devices by jointly optimizing the energy harvesting (EH) and data collection time for IoT devices, the selection of data collection points (DCPs), DCP-IoT associations, and task assignment, flight speed, and trajectories of UAVs, subject to the limited endurance of UAVs. As this problem is nonconvex, we propose a novel DCP association and trajectory-planning scheme that seeks age-optimal solutions through an iterative three-step process. First, we calculate the EH and data collection time for IoT devices using Karush–Kuhn–Tucker (KKT) conditions. Then, we introduce an optimal hovering time allocation-based affinity propagation (OHTAP) clustering algorithm to determine optimal DCP locations and establish DCP-IoT associations. Finally, we develop two algorithms to optimize UAVs’ trajectories: an improved partheno-genetic algorithm with enhancement mechanisms (EIPGA) and a hybrid algorithm that combines improved MinMax k-means clustering with EIPGA. Numerical results confirm that our scheme consistently outperforms benchmark schemes in AoI performance and solution stability across diverse scenarios. Full article

Small air-launched unmanned aerial vehicles (UAVs) face challenges in range and endurance due to their compact size and lightweight design. To address these issues, this paper introduces a multi-phase wind energy harvesting trajectory planning method designed to optimize the onboard electrical energy consumption during rendezvous and formation flight of air-launched fixed-wing swarms. This method strategically manages gravitational potential energy from air-launch deployments and harvests wind energy that aligns with the UAV’s flight speed. We integrate wind energy harvesting strategies for single vehicles with the spatial–temporal coordination of the swarm system. Considering the wind effects into the trajectory planning allows UAVs to enhance their operational capabilities and extend mission duration without changes on the vehicle design. The trajectory planning method is formalized as an optimal control problem (OCP) that ensures spatial–temporal coordination, inter-vehicle collision avoidance, and incorporates a 3-degree of freedom kinematic model of UAVs, extending wind energy harvesting trajectory optimization from an individual UAV to swarm-level applications. The cost function is formulized to comprehensively evaluate electrical energy consumption, endurance, and range. Simulation results demonstrate significant energy savings in both low- and high-altitude mission scenarios. Efficient wind energy utilization can double the maximum formation rendezvous distance and even allow for rendezvous without electrical power consumption when the phase durations are extended reasonably. The subsequent formation flight phase exhibits a maximum endurance increase of 58%. This reduction in electrical energy consumption directly extends the range and endurance of air-launched swarm, thereby enhancing the mission capabilities of the swarm in subsequent flight. Full article

Unmanned aerial vehicle (UAV)-assisted simultaneous wireless information and power transfer (SWIPT) systems have recently gained significant attraction in internet-of-things (IoT) applications that have limited or no infrastructure. Specifically, the free mobility of UAVs in three-dimensional (3D) space allows us good-quality channel links, thereby enhancing the communication environment and improving performance in terms of achievable rates, latency, and energy efficiency. Meanwhile, IoT devices can extend their battery life by harvesting the energy following the SWIPT protocol, which leads to an increase in the overall system lifespan. In this paper, we propose a secure UAV-assisted SWIPT system designed to optimize the secrecy energy efficiency (SEE) of a ground network, wherein a base station (BS) transmits confidential messages to an energy-constrained device in the presence of a passive eavesdropper. Here, we employ a UAV acting as a helper node to improve the SEE of the system and to aid in the energy harvesting (EH) of the battery-limited ground device following the SWIPT protocol. To this end, we formulate the SEE maximization problem by jointly optimizing the transmit powers of the BS and UAV, the power-splitting ratio for EH operations, and the UAV’s flight path. The solution is obtained via a proposed algorithm that leverages successive convex approximation (SCA) and Dinkelbach’s method. Through simulations, we corroborate the feasibility and effectiveness of the proposed algorithm compared to conventional partial optimization approaches. Full article

Birds and experienced glider pilots frequently use atmospheric updrafts for long-distance flight and energy conservation, with harvested energy from updrafts serving as the foundation. Inspired by their common characteristics in autonomous soaring, a reinforcement learning algorithm, the Twin Delayed Deep Deterministic policy gradient, is used to investigate the optimal strategy for an unpowered glider to harvest energy from thermal updrafts. A round updraft model is utilized to characterize updrafts with varied strengths. A high-fidelity six-degree-of-glider model is used in the dynamic modeling of a glider. The results for various flight initial positions and updraft strengths demonstrate the effectiveness of the strategy learned via reinforcement learning. To enhance the updraft perception ability and expand the applicability of the trained glider agent, an extra wind velocity differential correction module is introduced to the algorithm, and a strategy symmetry method is applied. Comparison experiments regarding round updraft, the Gedeon thermal model, and Dryden continuous turbulence indicate the crucial role of the further optimized methods in improving the updraft-sensing ability of the reinforcement learning glider agent. With optimized methods, a glider trained in a simplified thermal updraft with a simple training method can have more effective flight strategies. Full article

Energy management plays a crucial role in achieving extended endurance for solar-powered Unmanned Aerial Vehicles (UAVs). Current studies in energy management primarily focus on natural energy harvesting and task-oriented path planning. This paper aims to optimize energy consumption during the climb and glide stages by exploring variable climb speeds and glide powers. To achieve this, fitness functions are established for both the climb and glide stages, taking into account the maximum climb speed and glide power limits of the aircraft. The particle swarm optimization (PSO) algorithm is employed to solve the problem, resulting in significant energy savings of over 68% in the climb stage and 4.8% in the glide stage. Based on an analysis of the optimization trends, this study proposes an energy-management strategy to fulfill the demand for long-endurance flights. The findings of this study can serve as a valuable reference for high-altitude missions that require extended flight times. Full article

Flapping Wing Air Vehicles (FWAVs) have proven to be attractive alternatives to fixed wing and rotary air vehicles at low speeds because of their bio-inspired ability to hover and maneuver. However, in the past, they have not been able to reach their full potential due to limitations in wing control and payload capacity, which also has limited endurance. Many previous FWAVs used a single actuator that couples and synchronizes motions of the wings to flap both wings, resulting in only variable rate flapping control at a constant amplitude. Independent wing control is achieved using two servo actuators that enable wing motions for FWAVs by programming positions and velocities to achieve desired wing shapes and associated aerodynamic forces. However, having two actuators integrated into the flying platform significantly increases its weight and makes it more challenging to achieve flight than a single actuator. This article presents a retrospective overview of five different designs from the “Robo Raven” family based on our previously published work. The first FWAVs utilize two servo motors to achieve independent wing control. The basic platform is capable of successfully performing dives, flips, and button hook turns, which demonstrates the potential maneuverability afforded by the independently actuated and controlled wings. Subsequent designs in the Robo Raven family were able to use multifunctional wings to harvest solar energy to overcome limitations on endurance, use on-board decision-making capabilities to perform maneuvers autonomously, and use mixed-mode propulsion to increase payload capacity by exploiting the benefits of fixed and flapping wing flight. This article elucidates how each successive version of the Robo Raven platform built upon the findings from previous generations. The Robo Raven family collectively addresses requirements related to control autonomy, energy autonomy, and maneuverability. We conclude this article by identifying new opportunities for research in avian-scale flapping wing aerial vehicles. Full article

Innovative drone-based technologies provide novel techniques to guarantee the safety and quality of power supply and to perform these tasks more efficiently. Electric multirotor drones, which are at the forefront of technology, face significant flight time limitations due to battery capacity and weight constraints that limit their autonomous operation. This paper presents a novel drone charging station that harvests energy from the magnetic field present in power lines to charge the drone’s battery. This approach relies on a charging station that is easy to install by the drone on an overhead AC power line without modifying the electrical infrastructure. This paper analyses the inductive coupling between the energy harvester and the power line, electrical protection, the power electronics required for maximum power point tracking and the mechanical design of the charging station. A drone that perches on a cable, an end effector for installation procedures and the charging maneuver are described, along with discussion of the robotic and electrical tests performed in a relevant environment. Finally, a lightweight drone charging station capable of harvesting 145 W of power from a 600 A line current is reported. Full article

This study is aimed at assessing the feasibility of employing an innovative, smart-material-based control effector for an inflatable wing. A shape memory alloy (SMA) actuator is primarily investigated as a control effector in this work for its advantages of a simple actuation mechanism and a high force-to-weight ratio. This paper presents the design, control strategy and simulation results of the SMA actuator used as a stability augmentation system for a small-scale prototype kite. Stable flight of the kite is achieved during open wind tunnel tests using the SMA actuator. Based on experimental and simulation analyses, it is evident that the current SMA actuator is better for low-frequency actuations rather than stability augmentation purposes, as its performance is sensitive to practical conditions. The study also discusses potential improvements and applications of the SMA actuator. Full article

Airborne wind energy systems using flexible membrane wings have the advantages of a low weight, small packing volume, high mobility and rapid deployability. This paper investigates the aero-structural deformation of a leading edge inflatable kite for airborne wind energy harvesting. In the first step, a triangular two-plate representation of the wing is introduced, leading to an analytical description of the wing geometry depending on the symmetric actuation state. In the second step, this geometric constraint-based model is refined to a multi-segment wing representation using a particle system approach. Each wing segment consists of four point masses kept at a constant distance along the tubular frame by linear spring-damper elements. An empirical correlation is used to model the billowing of the wing’s trailing edge. The linear spring-damper elements also the model line segments of the bridle line system, with each connecting two point masses. Three line segments can also be connected by a pulley model. The aerodynamic force acting on each wing segment is determined individually using the lift equation with a constant lift coefficient. The particle system model can predict the symmetric deformation of the wing in response to a symmetric actuation of the bridle lines used for depowering the kite (i.e., changing the pitch angle). The model also reproduces the typical twist deformation of the wing in response to an asymmetric line actuation used for steering the kite. The simulated wing geometries are compared with photogrammetric information taken by the onboard video camera of the kite control unit, focusing on the wing during flight. The results demonstrate that a particle system model can accurately predict the geometry of a soft wing at a low computational cost, making it an ideal structural building block for the next generation of soft wing kite models. Full article

The conceptual design of a hybrid aerial vehicle for the exploration of the upper Venus atmosphere is presented. The vehicle will float like a balloon and harvest solar energy which is stored in batteries. The neutral buoyancy reduces the energy consumption and makes the vehicle robust and durable. Energy stored in the batteries can be used for powered flight with good horizontal and vertical mobility to explore aspects of the atmosphere. The vehicle is intended to operate near 55.3 km altitude and to explore the cloud layer of the planet. The vehicle takes its inspiration from the Stingray inflatable wing by Prospective Concepts. Based on a trade study, the wing span was set to 25 m. Equations are developed for the altitude, gas and skin temperature, and skin stress during neutrally buoyant flight. To keep the equations in a simplified analytical form, the complex compartmentalized gas pockets of the vehicle are lumped into a single gas sphere. The equations take into account the volumetric expansion of the structure and the requirement that the differential pressure needs to be large enough to allow for brief periods of powered flight without significant structural deformation. An aerodynamic analysis provides the lift and drag coefficient curves and indicates that the vehicle is pitch-stable. A powered flight analysis shows that an airspeed of 30 m/s can be maintained for 31 min at 55 km and 69 min at 69 km altitude. Full article

People have raised their expectations for UAV performance due to the widespread use of UAVs in both military and non-military settings. One of the most significant fields of research right now is how to enhance UAVs’ endurance capabilities. Many birds in the wild have the ability to fly for extended periods of time or great distances using the wind, which is called energy harvesting. A seabird called an albatross can use the wind’s horizontal gradient on the water’s surface to generate energy. By gliding, they may fly for a very long time without flapping their wings, thus lowering their own energy consumption. Due to the albatrosses’ nostrils sensitive sensory nerves and sensitivity to environmental information, such as airflow, the albatrosses can modify its flight attitude. Similar to this, real-time dynamic planning of the trajectory can only be done for UAVs in order to realize energy-capturing flying if correct and real-time wind field information is obtained. As a result, developing wind field sensing technology is crucial to the realization of energy-capturing gliding. In this study, we built a 3D wind field sensing system with wind vane sensor and pitot tube. Wind tunnel tests were used to calibrate and alter it. The system’s operation is initially validated by real flight, which may give environmental information, enabling UAVs to utilize the wind field as a reference for planning their flight paths. Full article

Inspired by the flapping wings of seagulls during flight, a new low-cost, magnet-free, bistable piezoelectric energy harvester is proposed to obtain energy from low-frequency vibration and convert it into electrical energy and reduce fatigue damage caused by stress concentration. In order to optimize the power generation efficiency of this energy harvesting, finite element analysis and experimental tests were carried out. The results of finite element analysis and experimental results are in good agreement, and the superior performance in improving stress concentration of the energy harvester compared to the previous parabolic (bow-shaped) one using bistable technology was quantitatively analyzed using finite element simulation, with a maximum stress reduction of 32.34%. The experimental results showed that under optimal operating conditions, the maximum open-circuit voltage of the harvester was 11.5 V, and the maximum output power was 73 μW. These results indicate that this is a promising strategy, which provides a reference for collecting vibrational energy in low-frequency environments. Full article