Unmanned Aerial Vehicle Mission Research Articles

UAV Trajectory Planning from a Comprehensive Energy Efficiency Perspective in Harsh Environments

As a highly promising application paradigm, unmanned aerial vehicles (UAVs) in wireless communications have gained widespread attention by virtue of their advantages, such as low cost and high agility. Unfortunately, the discharge capacity of onboard batteries will be greatly weakened in harsh environments, thereby reducing the expected endurance of the UAV. To this end, a UAV data distribution system with expected data requirements is considered in this article. Our objective is to reassess the trade-off between UAV mission time and its propulsion energy consumption (PEC), and provide a novel designing perspective for energy-efficient UAV deployment based on trajectory planning. First, we analyze the energy consumption composition and approximate the PEC to the total energy consumption of the UAV. Then, based on the “hover-fly-communicate” design, we focus on the factors that affect mission time and PEC. Furthermore, the simulation results illustrate the influence of critical system parameter settings on the propulsion energy consumption efficiency (PEC-E) of the UAV. Finally, future research is discussed, and the article is concluded.

An Accurate UAV Ground Landing Station System Based on BLE-RSSI and Maximum Likelihood Target Position Estimation

Earth observation with unmanned aerial vehicles (UAVs) offers an extraordinary opportunity to bridge the gap between field observations and traditional air and space-borne remote sensing. In this regard, ground landing stations (GLS) systems play a central role to increase the time and area coverage of UAV missions. Bluetooth low energy (BLE) technology and the received signal strength indicator (RSSI) techniques have been proposed for target location during UAV landing. However, these RSSI-based techniques present a lack of precision due to the propagation medium characteristics, which leads to UAV position vagueness. In this sense, the development of a novel low-cost GLS system for UAV tracking and landing is proposed. The GLS system has been embodied for the purpose of testing the UAV landing navigation capability. The maximum likelihood estimator (MLE) algorithm is addressed on an embedded microcontroller for the position estimation based on the RSSI acquired from an array of BLE devices. Experimental results demonstrate the feasibility and accuracy of the ground landing station system, achieving average errors of less than 0.04 m with the UAV-MLE target position estimation approach. This 0.04 m distance represents an order of magnitude increase in location precision over other currently available solutions. In many cases, this increased precision can enable more innovative docking mechanisms, less likelihood of mishaps in docking, and also quicker docking. It may also facilitate docking procedures where the docking station is itself moving, which may be the case if the docking unit is a mobile ground rover.

Toward flexible and persistent UAV service: Multi-period and multi-objective system design with task assignment for disaster management

Usability of lightweight unmanned aerial vehicles (UAVs) limited in a variety of activities in disaster management due to two fundamental drawbacks: limited flight duration, and limited loading capacity. With the proper system design and task assignment methodologies, these limitations can be overcome to enable long-duration UAV missions over large areas. The present study set out to determine the optimal unmanned aerial system (UAS) system design and UAV task assignment for such applications. Based on a proposed multi-objective mathematical model, exact Pareto optimal solutions were derived using the ε–constraint method. To find approximate Pareto solutions, an approximate two-phase (ATP) approach was developed, which is computationally inexpensive compare to the optimal solution approach. Through numerical experiments based on a realistic case study, it was shown that the ATP approach results in high accuracy and a two-orders-of-magnitude reduction in computation time. Additionally, for prompt application of our study to continuously changing situations, a rolling-horizon UAS approach herein is suggested.

Multi-UAV Cooperative Trajectory Planning Based on Many-Objective Evolutionary Algorithm

The trajectory planning of multiple unmanned aerial vehicles (UAVs) is the core of efficient UAV mission execution. Existing studies have mainly transformed this problem into a single-objective optimization problem using a single metric to evaluate multi-UAV trajectory planning methods. However, multi-UAV trajectory planning evolves into a many-objective optimization problem due to the complexity of the demand and the environment. Therefore, a multi-UAV cooperative trajectory planning model based on many-objective optimization is proposed to optimize trajectory distance, trajectory time, trajectory threat, and trajectory coordination distance costs of UAVs. The NSGA-III algorithm, which overcomes the problems of traditional trajectory planning, is used to solve the model. This paper also designs a segmented crossover strategy and introduces dynamic crossover probability in the crossover operator to improve the solving efficiency of the model and accelerate the convergence speed of the algorithm. Experimental results prove the effectiveness of the multi-UAV cooperative trajectory planning algorithm, thereby addressing different actual needs.

Automated execution of a pest bird deterrence system using a programmable unmanned aerial vehicle (UAV)

Crop damage caused by pest birds is a big problem around the world. As many studies have shown, existing bird control methods have not met growers' expectations regarding efficacy, reliability, and cost. Researchers are investigating the potential of using unmanned aerial vehicles (UAVs) as a tool for bird deterrence in agricultural fields to address this challenge. UAV-based bird deterrence techniques investigated in the past needed human operators to locate bird flocks and fly UAVs to desired trajectories. This study focused on automating a UAV-based bird deterrence system to operate it in real-time. Specifically, we focused on; i) optimizing the existing machine vision system to detect and locate birds-like movement in a real-time; ii) dynamically creating and launching two intelligent UAV missions in real-time to scare incoming birds (target intercept mission) or birds that are already inside the field (field patrol mission); and iii) executing these missions independently (short duration mission) as well as jointly (longer missions with more variation) in a vineyard using a programmable UAV. Outdoor experiment was conducted to evaluate the mission execution efficiency of the proposed system using a programmable UAV without a built-in global positioning system (GPS). Simulated bird movements generated using waving human hands were used for evaluating the target intercept mission. Results show that the algorithms developed for automated operation completed ∼92% of the assigned tasks successfully during shorter mission and 90% of the task during longer mission. Despite, successful execution of algorithms, the automated system experienced major shortcomings due to the lack of GPS for navigation of the UAV, which can be improved using a GPS-enabled programmable UAV. Future work also includes full scale field evaluation using machine vision system to identify actual incoming birds to determine the efficiency in deterring birds in commercial operations.

Numerical Evaluation of Riblet Drag Reduction on a MALE UAV

Flow control methods for aerodynamic drag reduction have been a field of interest to aircraft designers, who seek to minimize fuel consumption and increase the aircraft’s aerodynamic performance. Various flow control techniques, applied to aeronautical applications ranging from large airliners to small hand-launched unmanned aerial vehicles (UAVs), have been conceptualized, designed and tested in the past. Among others, the concept of riblets, inspired by the shark’s skin morphology, has been proposed and evaluated for airliners. In this work, the implementation of riblets on a medium-altitude long-endurance UAV (MALE) is investigated. The riblets can offer drag reduction due to the decrease in total skin friction, by altering the boundary layer characteristics in the near-wall region. The riblets are implemented on specific locations on the UAV (main wing, fuselage and empennage) and appropriately selected, on which the boundary layer becomes transitional from the laminar to the turbulent flow regime. For this reason, computational fluid dynamics modelling is performed by solving the Reynolds-averaged Navier–Stokes equations, incorporating the k-ω SST eddy viscosity turbulence model. The effect of the riblets in the near-wall region is modelled with the use of an appropriate wall boundary condition for the specific turbulence dissipation rate transport equation. It is shown that a drag reduction benefit, for both the loiter and the cruise flight segments of the UAV mission, can be obtained, and this is clearly presented by the drag polar diagrams of the air vehicle. Finally, the potential benefit to flight performance in terms of endurance and payload weight increase is also evaluated.

Cooperative Multi-UAV Task Assignment in Cross-Regional Joint Operations Considering Ammunition Inventory

As combat missions become increasingly complex in both space and time, cross-regional joint operations (CRJO) is becoming an overwhelming trend in modern air warfare. How to allocate resources and missions prior to the operation becomes a central issue to improve the combat efficiency. In this paper, we focus on the cooperative mission planning of multiple heterogeneous unmanned aerial vehicles (UAVs) in a CRJO. A multi-objective optimization problem is presented with the aim of minimizing the makespan while maximizing the value expectation obtained. Moreover, it is not mandatory for each UAV to return exactly to the base which it takes off. Furthermore, in addition to the constraints commonly found in UAV mission assignment problems, the ammunition inventory at each base is also taken into account. To solve such a problem, we developed an improved genetic algorithm (IGA) with a novel chromosome encoding format. It can determine the number of attacks on a given target based on the expectations obtained, rather than being predetermined. Specifically, an efficient logic-based unlocking mechanism is designed for the crossover and mutation operations in the algorithm. Simulation results show that the developed IGA can efficiently solve the considered problem. Through numerical experimental comparisons, the algorithm proposed in this work is superior to other existing IGA-like algorithms in terms of computational efficiency.

Improving UAV Mission Quality and Safety through Topographic Awareness

The field of Unmanned Aerial Vehicles (UAVs) has progressed greatly in the last years. UAVs are now used for many applications and are often flown automatically. One commonly implemented feature in an automatic flight is that of following a mission at a stable altitude. However, this altitude is almost always referenced from the take-off location and does not take terrain profile levels into account. This is a critical and dangerous issue because if the terrain level changes abruptly (e.g., mountain regions or buildings in a city), this can lead to crashes or an unintended (illegal) high altitude. Our aim for this work is to provide a solution such that a constant altitude above ground level is maintained. To this end, we make use of the readily available Digital Elevation Models (DEMs). These models, which contain the terrain elevation, help us in dynamically adjusting the VTOL UAV altitude so that it remains nearly constant in relation to the ground. Results have shown that with the use of our method, the altitude can be maintained sufficiently constant while introducing a limited increase in flight time and battery consumption that is proportional to the terrain’s irregularity. In a moderately changing terrain, the error could be reduced to just ±5 m.

An UAV-assisted mobile edge computing offloading strategy for minimizing energy consumption

Owing to the high economic benefits, flexible deployment, and controllable maneuverability, unmanned aerial vehicles (UAVs) have been envisioned as promising and potential technologies for dispensing wireless communication services. This paper investigates a mobile edge computing (MEC) system assisted by multiple access points (APs) and an UAV, in which APs may not be able to straightly establish wireless communications with terrestrial Internet of Thing devices (IoT) due to ground signal blockage. Consequently, an UAV is dispatched as a mobile AP to serve a group of users and render the air-to-ground channel. In this scenario, we contemplate dividing the computing tasks of IoTDs into three parts: either be calculated locally, or offloaded to the UAV for processing, or accomplished on AP through relaying. This work attempts to minimize the weighted sum of communication consumption, calculation consumption, and the UAV’s flight consumption over a finite UAV mission duration by jointly optimizing calculation task allocation ratio, power distribution as well as the UAV’s trajectory. However, the resulting problem we put forward is demonstrated to be highly non-convex and challenging to solve. To tackle this issue, we decompose the original problem into two sub-problems hinging on the block coordinate descent (BCD) method. We settle the two sub-problems iteratively through the Lagrangian duality method and succession convex approximation (SCA) technique. The simulation results further reveal that the proposed approach is superior to other comparison baselines.

An Optimal Fuzzy Logic-Based Energy Management Strategy for a Fuel Cell/Battery Hybrid Power Unmanned Aerial Vehicle

With the development of high-altitude and long-endurance unmanned aerial vehicles (UAVs), optimization of the coordinated energy dispatch of UAVs’ energy management systems has become a key target in the research of electric UAVs. Several different energy management strategies are proposed herein for improving the overall efficiency and fuel economy of fuel cell/battery hybrid electric power systems (HEPS) of UAVs. A rule-based (RB) energy management strategy is designed as a baseline for comparison with other strategies. An energy management strategy (EMS) based on fuzzy logic (FL) for HEPS is presented. Compared with classical rule-based strategies, the fuzzy logic control has better robustness to power fluctuations in the UAV. However, the proposed FL strategy has an inherent defect: the optimization performances will be determined by the heuristic method and the past experiences of designers to a great extent rather than a specific cost function of the algorithm itself. Thus, the paper puts forward an improved fuzzy logic-based strategy that uses particle swarm optimization (PSO) to track the optimal thresholds of membership functions, and the equivalent hydrogen consumption minimization is considered as the objective function. Using a typical 30 min UAV mission profile, all the proposed EMS were verified by simulations and rapid controller prototype (RCP) experiments. Comprehensive comparisons and analysis are presented by evaluating hydrogen consumption, system efficiency and voltage bus stability. The results show that the PSO-FL algorithm can further improve fuel economy and achieve superior overall dynamic performance when controlling a UAV’s fuel-cell powertrain.

ANALYSIS OF SURFACE THERMAL BEHAVIOR FOR THE STUDY OF URBAN HEAT ISLANDS: AN EXPERIMENT USING UNMANNED AERIAL VEHICLE (UAV)

The objective of this study was to analyze the variation of horizontal superficial temperature of different typologies of surfaces through thermographies using a thermal camera embedded in an unmanned aerial vehicle (UAV). The temperature data of some typologies of ground cover (asphalt, cement sidewalk, lawn and trees) and buildings cover (ceramic roof and cement tile) were analyzed. The results showed that there was a greater retention of heat by the asphalt covering, followed by the cement sidewalk. The temperature of the lawn was high during the day, but at night it was the surface with the lowest temperature among all analyzed. The top of the tree accumulated less heat during the day and it took longer to lose it during the night, getting the lowest thermal amplitude. At the building covering surfaces, while the temperature of the ceramic roof suffered little variation during the day, the cement tile suffered a high variation, showing that it is not a good thermal insulator. The use of UAV allowed a quick acquisition of the thermographies of the selected targets. The weather analysis was a determining factor to carry out the UAV missions.

Positioning aerial relays to maintain connectivity during drone team missions

Approaches that aim to maintain the connectivity of unmanned aerial vehicles (UAVs) or drones either utilize cellular networks or propose to jointly optimize connectivity needs with other mission tasks. Therefore, most solutions either rely on existing infrastructure or are optimized for specific applications. Use of UAVs to assist the connectivity of ground nodes is commonly proposed. In this work, we deploy UAVs as relays to support mission-oriented UAV networks in order to decouple the mission and communication tasks. We propose a modular relay positioning and trajectory planning algorithm that guarantees connectivity of the UAV mission team with minimum number of relays and feasible trajectories, where the cost, network structure and setup can be changed, allowing its use for different types of missions, without relying on infrastructure. We propose different approaches to relay position decisions and compare the proposed schemes with an ideal scheme and a Voronoi-based benchmark scheme. Our results show that different solutions are applicable for achieving fewer number of relay nodes, higher utilization or lower number of hops between the nodes. With the proposed scheme the maximum number of relays in the air can be reduced by up to 40% and utilization can be increased up to 50% in comparison to the benchmark scheme, with less average traveled distances and average velocities for the relay nodes. These advantages come with a cost of higher maximum number of hops compared to the benchmark.

Multiview Image Matching of Optical Satellite and UAV Based on a Joint Description Neural Network

Matching aerial and satellite optical images with large dip angles is a core technology and is essential for target positioning and dynamic monitoring in sensitive areas. However, due to the long distances and large dip angle observations of the aerial platform, there are significant perspective, radiation, and scale differences between heterologous space-sky images, which seriously affect the accuracy and robustness of feature matching. In this paper, a multiview satellite and unmanned aerial vehicle (UAV) image matching method based on deep learning is proposed to solve this problem. The main innovation of this approach is to propose a joint descriptor consisting of soft descriptions and hard descriptions. Hard descriptions are used as the main description to ensure matching accuracy. Soft descriptions are used not only as auxiliary descriptions but also for the process of network training. Experiments on several problems show that the proposed method ensures matching efficiency and achieves better matching accuracy for multiview satellite and UAV images than other traditional methods. In addition, the matching accuracy of our method in optical satellite and UAV images is within 3 pixels, and can nearly reach 2 pixels, which meets the requirements of relevant UAV missions.

UAV path design with connectivity constraint based on deep reinforcement learning

Cellular networks are expected to communicate effectively with unmanned aerial vehicles (UAVs) and support various applications. However, existing cellular networks are primarily designed to cover users on the ground; thus, coverage holes in the sky will exist. In this paper, we investigate the problem of path design for cellular-connected UAVs, taking into account the interruption performance throughout the UAV mission to minimize the completion time. Two types of connectivity constraints requirements are assumed to be available. The first is defined as the maximum continuous time interval that the UAV loses connection with base stations (BSs) below a predefined threshold. For the second, we consider the sum outage of UAV is limited during the entire UAV mission. The UAV is tasked with flying from a starting location to a final destination while minimization the mission time, satisfying the two constraints, separately. The formulated path design problem which involves continues variables and a dynamic radio environment, is not convex and thus is extremely difficult to solve directly. To tackle this challenge, a deep reinforcement learning (DRL) based trajectory design algorithm is proposed, where the Dueling Double Deep Q Network(Dueling DDQN) with multi-steps learning method is applied. Simulation results demonstrate the effectiveness of the proposed DRL algorithm and achieve a trade-off between the trajectory length of the UAV and connection quality.

Comparison of exact and approximate approaches to UAVs mission contingency planning in dynamic environments.

This paper presents a novel approach to the joint proactive and reactive planning of deliveries by an unmanned aerial vehicle (UAV) fleet. We develop a receding horizon-based approach to contingency planning for the UAV fleet's mission. We considered the delivery of goods to spatially dispersed customers, over an assumed time horizon. In order to take into account forecasted weather changes that affect the energy consumption of UAVs and limit their range, we propose a set of reaction rules that can be encountered during delivery in a highly dynamic and unpredictable environment. These rules are used in the course of designing the contingency plans related to the need to implement an emergency return of the UAV to the base or handling of ad hoc ordered deliveries. Due to the nonlinearity of the environment's characteristics, both constraint programming and genetic algorithm paradigms have been implemented. Because of the NP-difficult nature of the considered planning problem, conditions have been developed that allow for the acceleration of calculations. The multiple computer experiments carried out allow for comparison representatives of the approximate and exact methods so as to judge which approach is faster for which size of the selected instance of the UAV mission planning problem.

Применение модели глобальной атмосферы в оценке летно-технических характеристик и построении рубежа досягаемости при подготовке полетных заданий для беспилотных летательных аппаратов морского базирования

When standard atmosphere is used in range and altitude flight profile assessment of the unmanned aerial vehicles (UAVs) flight performance, it does not take into account the huge variety of meteorological factors on different routes. Hence, it does not accurately assess the maximum flight range. It is a strategy of the “worst combinations in terms of the probability of meteorological factors” for the entire surface of the Earth at all seasons. The paper proposes a global atmosphere model to be used in flight range assessment, and compares the model with the meteorological data archive. To build the reach line when preparing flight missions for sea-based unmanned aerial vehicles, the study proposes to use the global atmosphere model, which takes into account the meteorological parameters of a given place and month, since the wind, for example, has a particularly strong effect on the flight range and has significant seasonal-latitudinal variability. The global atmosphere model and a simplified mathematical flight model can be also used in assessing the maximum permissible length of the operational section when preparing a flight route.

AERO2022-flying danger reduction for quadcopters by using machine learning to estimate current, voltage, and flight area

Choosing the appropriate battery capacity for unmanned aerial vehicle (UAV) missions is critical, as draining the battery during flight nearly always results in vehicle damage and a significant risk of human harm or property damage. Predicting energy usage on a difficult trip is critical since the flying location, weather conditions, and other factors all impact power use. We develop a drone model that employs machine learning techniques to forecast battery and current consumption, as well as the quadcopter flying area, extremely precisely and quickly. As a result, the flight danger is lowered, and we will have a safe flight.

Modeling and Characterization of Traffic Flow Patterns and Identification of Airspace Density for UTM Application

Current airspace has limited resources, and the widespread use of unmanned aerial vehicles (UAVs) increases airspace density, which is already crowded with manned aircraft. This demands the improvement of airspace safety and capacity while considering all parametric uncertainties that may hinder aircraft and UAV mobility such as dynamic airspace structures and weather conditions. This paper proposes a data analytics framework to characterize traffic flow patterns of unmanned traffic management (UTM) airspace by analyzing simulated historical data. Mission patterns are characterized and identified by considering multiple UAV missions and scenarios with different priority levels to highlight UAVs’ trajectories and deviations from the actual path due to these constraints. The pertinent data analysis supports risk analysis and improves trajectory planning in different airspace regions considering all dynamic parameters such as extreme weather, emergency services, and dynamic airspace structures. The data processing framework, which is density-based spatial clustering of applications with noise (DBSCAN), identified significant deviations in mission patterns with almost 82% confidence level. The UTM traffic flow characterization is conducted by three key characterization parameters mainly Distance from Centroid (DFC), Distance to Complete Mission (DTCM) and Time to Complete Mission (TTCM). This work also analyzed the airspace congestion using the Kernel density estimation (KDE). This analysis identified some regions of interference as potential congested areas representing safety concerns. The proposed framework is envisioned to assist UTM authority by characterizing air traffic behavior, managing its flow, improving airspace design, and providing the basis for developing predictive capabilities that support traffic flow management.

Time-Critical Maritime UAV Mission Planning Using a Neural Network: An Operational View

An operational planning procedure for a time-critical maritime unmanned aerial vehicle (UAV) search mission is introduced and evaluated. The mission is the fast identification of a target vessel. The triggering report only contains information regarding the category and displacement of a vessel carrying out a prohibited activity, resembling operational situations. A neural network trained to classify vessels is combined with vessel clustering to reduce waypoints in the flight plan. The UAV’s onboard sensors provide input for the neural network regarding each vessel in the search area, resulting in a prioritization of vessels to be visited. As the accuracy of the classification and the possibilities for clustering depend on several operational factors as well as on the UAV’s sensor degradation, we investigate three methodologies to identify which planning procedure to use in various operational situations. The results show that our robust and agile approach can help a UAV find the unknown target vessel as soon as possible.

Development of High-Altitude Hybrid Fan Centrifugal Compressor System for Turbochargers and Utilization of AI to Improve Combustor Performance

This opinion paper focus on the belief of the authors, that adding a fan or pre-compression stage in front of a centrifugal impeller typically found in commercial turbochargers has the potential to increase the overall pressure ratio of the turbocharger, which help with high-altitude operation. Additionally, this paper address conviction of the authors that the utilization of artificial intelligence and data science will improve combustor performance.  The focus is on the development of high-altitude hybrid fan centrifugal and compressor system for turbochargers. Current attainable flight times of Unmanned Aerial Vehicles (UAVs) at high altitudes greatly hinder potential mission capabilities. Specifically, the US Army’s small UAVs are propelled by automotive compression-ignition (CI) engines, which deliver the required high efficiency and low fuel consumption but lack the ability to ingest an adequate mass flow rate of low-density air into their compression cylinders at high altitudes. A hybrid fan- centrifugal compressor system will be developed and integrated onto these UAV engines to provide the air mass flow rate necessary to operate at 60% of maximum sea-level power while at 30,000 feet altitude; thus, reducing the hindrance of high altitude environmental factors on UAV missions. The combined system will be integrated on a dual shaft to allow for fan and compressor to rotate at different speeds. Dual shafts are, typically, used in turbojet engines for combined fan and axial flow compressors for delivering large air mass flow. This hybrid fan-centrifugal compressor system will offer higher pressure and mass flow rate to the current Army UAV engines than standard turbochargers, while maintaining simplicity and manufacturability.