Operation Of Unmanned Aerial Vehicles Research Articles

Design and Development of Solar Drone

Abstract: The harnessing of solar energy during operations of unmanned aerial vehicle(UAV) provides potential solution to combat energy constraints. This Thesis examines the practicality of mini solar drone and provide experimental validation in regards to energy maximization through solar energy management and flight time optimization.The simulations results are consistent with real time measurements during flight test. This project deals with a quadcopter using solar energy as well as lithium polymer battery as power source combination to give maximum flight output. Using solar panels, it collects the energy during day time for immediate use and also store the remaining part for night as well. The objective is to identify , design and analyze such a reusable solar powered quadcopter for high altitude endurance and longer battery life.

Detection of Power Poles in Orchards Based on Improved Yolov5s Model

During the operation of agricultural unmanned aerial vehicles (UAVs) in orchards, the presence of power poles and wires pose a serious threat to flight safety, and can even lead to crashes. Due to the difficulty of directly detecting wires, this research aimed to quickly and accurately detect wire poles, and proposed an improved Yolov5s deep learning object detection algorithm named Yolov5s-Pole. The algorithm enhances the model’s generalization ability and robustness by applying Mixup data augmentation technique, replaces the C3 module with the GhostBottleneck module to reduce the model’s parameters and computational complexity, and incorporates the Shuffle Attention (SA) module to improve its focus on small targets. The results show that when the improved Yolov5s-Pole model was used for detecting poles in orchards, its accuracy, recall, and mAP@50 were 0.803, 0.831, and 0.838 respectively, which increased by 0.5%, 10%, and 9.2% compared to the original Yolov5s model. Additionally, the weights, parameters, and GFLOPs of the Yolov5s-Pole model were 7.86 MB, 3,974,310, and 9, respectively. Compared to the original Yolov5s model, these represent compression rates of 42.2%, 43.4%, and 43.3%, respectively. The detection time for a single image using this model was 4.2 ms, and good robustness under different lighting conditions (dark, normal, and bright) was demonstrated. The model is suitable for deployment on agricultural UAVs’ onboard equipment, and is of great practical significance for ensuring the efficiency and flight safety of agricultural UAVs.

NEW CHALLENGES IN THE OPERATION OF UNMANNED AERIAL VEHICLES. CHANGES IN LEGAL REGULATIONS REGARDING THE SAFETY OF UNMANNED AVIATION

The extremely dynamic development of civil aviation in recent years has simultaneously contributedto the growing popularity of unmanned aerial vehicles (UAVs), which have taken over air traffic inthe areas of protection and security of the population and, since the armed conflict in Ukraine, havealso been put to use as dual-use products in military operations. However, one must bear in mind alsousers who fly drones for private and recreational purposes. Such a rapid growth in interest caused thenecessity of introducing regulations that would enable their safe design, production, maintenanceand operation in the EU. Thanks to these requirements, the civil aviation authorities in the EU havemade every effort to create a solid legal framework for the operations of UAVs, keeping up with theirrapid technological development, minimizing accidents that involve flight participants and reducingthe risk of material damage or safety of bystanders present on the surface.The authors adopted the assumption that aviation safety regulations are excessively conservative.The first part of this hypothesis is not a revelation, the rule being that legal regulations are delayedin relation to the dynamics of technological progress in this field. The second is an open questionregarding the need for greater liberalization in the field of unmanned flights.

Unmanned Aerial Vehicles for Search and Rescue: A Survey

In recent years, unmanned aerial vehicles (UAVs) have gained popularity due to their flexibility, mobility, and accessibility in various fields, including search and rescue (SAR) operations. The use of UAVs in SAR can greatly enhance the task success rates in reaching inaccessible or dangerous areas, performing challenging operations, and providing real-time monitoring and modeling of the situation. This article aims to help readers understand the latest progress and trends in this field by synthesizing and organizing papers related to UAV search and rescue. An introduction to the various types and components of UAVs and their importance in SAR operations is settled first. Additionally, we present a comprehensive review of sensor integrations in UAVs for SAR operations, highlighting their roles in target perception, localization, and identification. Furthermore, we elaborate on the various applications of UAVs in SAR, including on-site monitoring and modeling, perception and localization of targets, and SAR operations such as task assignment, path planning, and collision avoidance. We compare different approaches and methodologies used in different studies, assess the strengths and weaknesses of various approaches, and provide insights on addressing the research questions relating to specific UAV operations in SAR. Overall, this article presents a comprehensive overview of the significant role of UAVs in SAR operations. It emphasizes the vital contributions of drones in enhancing mission success rates, augmenting situational awareness, and facilitating efficient and effective SAR activities. Additionally, the article discusses potential avenues for enhancing the performance of UAVs in SAR.

Habitat use by South American fur seals (Arctocephalus australis) and sea lions (Otaria flavescens) in a marine protected area in southern Brazil

We present an analysis of the influence of relief on habitat use by South American fur seals (Arctocephalus australis) and sea lions (Otaria flavescens) in the Wildlife Refuge of Ilha dos Lobos (WRIL) in southern Brazil. To test the hypothesis that the spatial occupation of WRIL by these two species of South American pinnipeds is related to the higher relief found in the northern region of the island, we generated a digital three-dimensional model of Ilha dos Lobos. This model was based on photogrammetry of 157 aerial images obtained from an unmanned aerial vehicle (UAV) in 2019. Aerial censuses were conducted between 2010 and 2020 using a helicopter and a UAV to assess the abundance of pinnipeds. The effects of altitude and seasonality on species abundances were assessed using Generalized Linear Mixed Models with Negative Binomial error distribution. The results indicated the existence of variation in the relief of the island, with higher areas in the northern region highly correlated with higher densities of both South American pinniped species on the island. The relationship between pinniped density and altimetry was statistically significant, indicating that these animals preferred to use the higher areas of the island when resting. These results suggest the importance of UAV operations and employment for understanding habitat use by fauna in protected areas.

Tackling different aspects of drone services utilizing technologies from cross-sectional industries

Enabling autonomous and Beyond Visual Line of Sight (BVLOS) operation of Unmanned Aerial Vehicles (UAVs) in the Very Low Level (VLL) airspace requires further advancement of technologies such as sensing the environment or secure and reliable communication. This paper addresses these challenges by presenting solutions developed within the project Airborne Data Collection on Resilient System Architectures (ADACORSA). Here, findings from cross-sectional areas such as the automotive industry are being further enhanced to fulfill the demands of aviation, in particular for use in the UAV domain. The developed technologies include an advanced Ethernet-based deterministic network for reliable onboard communication, a multi-sensor architecture for sensing the spatial environment as well as a multi-link communication gateway that provides reliable communication to the ground and a secure handover architecture.

Refined fault tolerant tracking control of fixed-wing UAVs via fractional calculus and interval type-2 fuzzy neural network under event-triggered communication

The refined fault tolerant tracking control (FTTC) scheme is developed for multiple fixed-wing unmanned aerial vehicles (UAVs) against actuator faults and wind effects under event-triggered communication. To decrease the communication overload caused by the continuous information transmissions among neighboring UAVs, an event-triggered communication mechanism is incorporated to dynamically reduce and increase the triggering frequency at the pre-fault and post-fault phases, respectively, thus reducing the communication burden while ensuring the synchronization tracking performance of faulty UAVs. Moreover, refined FTTC performance with event-triggered communications can be achieved by skillfully invoking fractional-order calculus. Furthermore, interval type-2 fuzzy neural networks with fractional-order adaptive modulation factors are constructed to compensate for the unknown nonlinearities. Lyapunov stability analysis has revealed that all attitudes can synchronously track their references. Simulation results are presented to demonstrate that communication resource-saving refined FTTC performance can be guaranteed with different fractional-order operators.

On the performance of all-optical RORIS dual hop UAV based FSO systems

In this work, we investigate the performance of an all-optical dual hop free-space optical (FSO) communication system using an unmanned aerial vehicle (UAV) as a relay and utilizing a reconfigurable optical intelligent reflecting surface (ROIRS) to improve the coverage. We statistically characterize the combined effects of the three impairments: atmospheric turbulence (AT), angle of arrival (AoA) fluctuation of UAV, and the geometric and misalignment loss (GML) caused by the fluctuations of the transmitter, ROIRS, and the position and orientation of the UAV. Incorporating the aforementioned effects and the non-orthogonality of the received beam at the UAV and the receiver, closed-form expressions of the overall outage probability and the average bit error rate for the considered system are derived. Furthermore, the analytically derived expressions are verified by Monte-Carlo simulations.

Deep reinforcement learning-based model-free path planning and collision avoidance for UAVs: A soft actor–critic with hindsight experience replay approach

In this paper, we propose a soft actor–critic (SAC) algorithm with hindsight experience replay (HER), called SACHER, which is a class of deep reinforcement learning (DRL) algorithm. SAC is an off-policy model-free DRL algorithm that outperforms earlier DRL algorithms in terms of exploration and robustness. However, in SAC, maximizing the entropy-augmented objective degrades the optimality of learning outcomes. We propose SACHER to improve the learning performance of SAC. We apply SACHER to the path planning and collision avoidance control of unmanned aerial vehicles (UAVs). We demonstrate the effectiveness of SACHER in terms of the success rate, learning speed, and collision avoidance performance of UAV operation.

UAV Implementations in Urban Planning and Related Sectors of Rapidly Developing Nations: A Review and Future Perspectives for Malaysia

The rapid growth of urban populations and the need for sustainable urban planning and development has made Unmanned Aerial Vehicles (UAVs) a valuable tool for data collection, mapping, and monitoring. This article reviews the applications of UAV technology in sustainable urban development, particularly in Malaysia. It explores the potential of UAVs to transform infrastructure projects and enhance urban systems, underscoring the importance of advanced applications in Southeast Asia and developing nations worldwide. Following the PRISMA 2020 statement, this article adopts a systematic review process and identifies 98 relevant studies out of 591 records, specifically examining the use of UAVs in urban planning. The emergence of the UAV-as-a-service sector has led to specialized companies offering UAV operations for site inspections, 3D modeling of structures and terrain, boundary assessment, area estimation, master plan formulation, green space analysis, environmental monitoring, and archaeological monument mapping. UAVs have proven to be versatile tools with applications across multiple fields, including precision agriculture, forestry, construction, surveying, disaster response, security, and education. They offer advantages such as high-resolution imagery, accessibility, and operational safety. Varying policies and regulations concerning UAV usage across countries present challenges for commercial and research UAVs. In Malaysia, UAVs have become essential in addressing challenges associated with urbanization, including traffic congestion, urban sprawl, pollution, and inadequate social facilities. However, several obstacles need to be overcome before UAVs can be effectively deployed, including regulatory barriers, limited flight time and range, restricted awareness, lack of skilled personnel, and concerns regarding security and privacy. Successful implementation requires coordination among public bodies, industry stakeholders, and the public. Future research in Malaysia should prioritize 3D modeling and building identification, using the results of this study to propel advancements in other ASEAN countries.

Assuring Safe and Efficient Operation of UAV Using Explainable Machine Learning

The accurate estimation of airspace capacity in unmanned traffic management (UTM) operations is critical for a safe, efficient, and equitable allocation of airspace system resources. While conventional approaches for assessing airspace complexity certainly exist, these methods fail to capture true airspace capacity, since they fail to address several important variables (such as weather). Meanwhile, existing AI-based decision-support systems evince opacity and inexplicability, and this restricts their practical application. With these challenges in mind, the authors propose a tailored solution to the needs of demand and capacity management (DCM) services. This solution, by deploying a synthesized fuzzy rule-based model and deep learning will address the trade-off between explicability and performance. In doing so, it will generate an intelligent system that will be explicable and reasonably comprehensible. The results show that this advisory system will be able to indicate the most appropriate regions for unmanned aerial vehicle (UAVs) operation, and it will also increase UTM airspace availability by more than 23%. Moreover, the proposed system demonstrates a maximum capacity gain of 65% and a minimum safety gain of 35%, while possessing an explainability attribute of 70%. This will assist UTM authorities through more effective airspace capacity estimation and the formulation of new operational regulations and performance requirements.

Mental workload assessments of aerial photography missions performed by novice unmanned aerial vehicle operators.

Mental workload is one of the important variables in understanding human performance in drone operation. To test the effects of gender, age group, flight route, and altitude on the flight performance and mental workload of the novice drone operators. Ten male and ten female participants without prior drone operating experience joined. They were split into two age groups. After attending a training, the participants operated a drone to perform photo taking missions under flight route and altitude conditions. The weighted NASA Task Load Index (TLX), Modified Cooper-Harper (MCH) scale, heart rate, and interbeat interval were measured to assess the mental workload of the participants. Flight time to complete the mission was adopted to indicate flight performance. The effect of age group was significant (p < 0.05) on flight time, weighted TLX score, and MCH score. Flight route and altitude were not significant on the two subjective ratings and two cardiac measures. The flight performance of younger participants was significantly better than that of their older counterpart. The effects of both the flight route and altitude on the perceived mental workload of the drone operators were insignificant. Both the weighted NASA TLX and MCH scales were appropriate in measuring the mental workload of the novice drone operators.

Clothoid-Based Path Planning for a Formation of Fixed-Wing UAVs

Unmanned aerial vehicles (UAVs) are playing an increasingly crucial role in many applications such as search and rescue, delivery services, and military operations. However, one of the significant challenges in this area is to plan efficient and safe trajectories for UAV formations. This paper presents an optimization procedure for trajectory planning for fixed-wing UAV formations using graph theory and clothoid curves. The proposed planning strategy consists of two main steps. Firstly, the geometric optimization of paths is carried out using graphs for each UAV, providing piece-wise linear paths whose smooth connections are made with clothoids. Secondly, the geometric paths are transformed into time-dependent trajectories, optimizing the assigned aircraft speeds to avoid collisions by solving a mixed-integer optimal control problem for each UAV of the flight formation. The proposed method is effective in achieving suboptimal paths while ensuring collision avoidance between aircraft. A sensitivity analysis of the main parameters of the algorithm was conducted in ideal conditions, highlighting the possibility of decreasing the length of the optimal path by about 4.19%, increasing the number of points used in the discretization and showing a maximum path length reduction of about 10% compared with the average solution obtained with a similar algorithm using a graph based on random directions. Furthermore, the use of clothoids, whose parameters depend on the UAV performance constraints, provides smoother connections, giving a significant improvement over traditional straight-line or circular trajectories in terms of flight dynamics compliance and trajectory tracking capabilities. The method can be applied to various UAV formation scenarios, making it a versatile and practical tool for mission planning.

Multiobjective 3-D UAV Movement Planning in Wireless Sensor Networks Using Bioinspired Swarm Intelligence

The use of unmanned aerial vehicles (UAVs) is a promising solution to efficiently acquire data in large-scale wireless sensor networks (WSNs). In a wide-area WSN environment, UAV path planning is one of the challenging issues in solving the optimization problem to achieve complex and multiple objectives under various constraints related to UAV operation. In this paper, we propose a novel asynchronous UAV path planning mechanism for multi-objective UAV operation. In a three-dimensional sensor field, a grid-based sensor field and information gathering model is introduced. We define a UAV coverage area where line-of-sight communication is possible between a UAV and the sensors and propose a method to quickly find the grid-cells within the coverage area. We define a multi-purpose fitness function that maximizes the value of the acquired sensing information and at the same time minimizes the time and energy required for UAV operation. The value of the sensing information reflects the sensor density for each sensor type within UAV coverage, as well as changes in the sensing information values over time. In the proposed method, time and energy objective functions are learned by considering location-dependent communication link quality, sensor density, and the next UAV locations. The optimum UAV position and the movement schedule of each UAV are asynchronously derived using the proposed particle swarm optimization (PSO) algorithm with several UAV operational constraints. The experiment results demonstrate that the proposed method can maximize the utility of the objective function and achieve fast convergence in finding the optimal solution compared with other methods.

Research on Demand-Based Scheduling Scheme of Urban Low-Altitude Logistics UAVs

Aiming at the problem of the scheduling scheme of urban low-altitude logistics unmanned aerial vehicles (UAVs), this paper establishes a demand-based UAV scheduling scheme model using an improved simulated annealing algorithm, taking minimizing the cost of distribution as the objective function and considering restrictions such as UAV performance constraints, airspace constraints, and distribution constraints, among others. For verification, actual express data and airspace constraints in Shanghai are taken as examples. Two urban air traffic networks are constructed using road and building data. The analysis results show that the planning scheme of this model is superior to other forecasting models in terms of delivery cost and delivery time. In addition, this model can flexibly calculate the optimal scheduling scheme under the constraints of multiple parameters, according to the requirements of delivery volume, delivery distance, UAV performance, etc.

Effect of Control Parameters on Hybrid Electric Propulsion UAV Performance for Various Flight Conditions: Parametric Study

Nowadays, great efforts of ongoing research are devoted to hybrid-electric propulsion technology that offers various benefits, such as reduced noise and pollution emissions and enhanced aircraft performance and fuel efficiency. The ability to estimate the performance of an aircraft in any flight situation in which it may operate is essential for aircraft development. In the current study, a simulation model was developed that allows estimating the flight performance and analyzing the mission of a fixed-wing multi-rotor Unmanned Aerial Vehicle (UAV) with a hybrid electric propulsion system (HEPS), with both conventional and Vertical Takeoff and Landing (VTOL) capabilities. The control is based on the continuous specification of pitch angle, propulsion thrust, and lift thrust to achieve the required conditions of a given flight segment. Six different missions were considered to analyze the effect of control parameters exhibiting the most influence on the UAV mission performance. An appropriate set of control parameters was selected through a multidimensional parametric study. The results show that the control parameters, if not well tuned, affect the mission performance: for example, in the deceleration transition, a longer time to reduce the cruise speed to stand still may be the result because the controller struggles to adjust the pitch angle. In addition, the implemented methodology captures the effects of transient maneuvers, unlike typical quasi-static analysis without the complexity of full simulation models.

Flight Parameter—Wind Vortex Characteristic Control Model of a Four-Multirotor Unmanned Aerial Vehicle Operating in Pesticide Spraying of Rice

The downwash airflow generated by the rotors can enhance the penetration of pesticide droplets, allowing them to penetrate deeper into the canopy and more comprehensively control pests. Downwash air will produce a “wind vortex” in plant canopy, and the parameters of a “wind vortex” represent the effect of pesticide deposition to a certain extent. To obtain the corresponding relationship between wind vortices and flight parameters and control the effect of pesticide spraying, this paper carried out unmanned aerial vehicle (UAV) flight experiments in the field. Wind vortices were generated in rice canopy by downwash airflow, and the parameters of wind vortices were obtained by identifying wind-vortex images using the inter-frame difference method. The wind-vortex parameter control model was established, which can calculate the altitude and speed of the UAV when applying pesticide according to the target wind-vortex parameter. The deviations in the altitude were determined to be 0.67 and 0.43 m, and the deviations in the speed were 0.29 and 0.35 m/s during downwind and headwind UAV operations, respectively. The model relation functions were established, and their accuracies were found to be 97.1%, 92.3%, 69%, and 58% (downwind), and 97%, 78.4%, 62%, and 57% (upwind), respectively, indicating that downwind UAV operation leads to a clear relation between the wind-vortex parameters and the UAV-flight parameters. The model establishes the corresponding relationship between the wind-vortex parameters and flight parameters, which provides a theoretical basis for studying the precise application control method of UAV.

Wind dynamic and energy-efficiency path planning for unmanned aerial vehicles in the lower-level airspace and urban air mobility context

Unmanned aerial vehicles (UAVs) have been extensively used in urban environments for logistics, parcel delivery and surveillance, and the development of air taxi services. Given the dynamic nature of urban air mobility in terms of decision-making time limit, wind dynamics and other external factors, one should consider their safe and efficient operations in an urban context. Therefore, we propose an energy-efficient path-planning model for UAVs under large and complex urban environments and wind dynamics. The proposed method adopted Voronoi Diagram to decompose the complex urban environment into a simplified network model, given the presence of no-fly zones and restricted areas as obstacles. One could obtain the feasible initial path by solving the network model using the Dijkstra shortest path algorithm concerning the distance matrix. Given the nonlinearity of energy consumption along a path. We further model the UAV energy consumption and propose an efficient particle swarm optimisation (PSO) metaheuristic algorithm to achieve better solution quality. Compared to traditional PSO, the proposed algorithm achieved a 5% to 22% improvement under different wind scenarios by simulating real-life situations. In conclusion, the proposed method can achieve safe UAV operations with sufficient separation and less energy consumption.

An Electric Vehicle Assisted Charging Mechanism for Unmanned Aerial Vehicles

The global greenhouse effect and air pollution problems have been deteriorating in recent years. The power generation in the future is expected to shift from fossil fuels to renewables, and many countries have also announced the ban on the sale of vehicles powered by fossil fuels in the next few decades, to effectively alleviate the global greenhouse effect and air pollution problems. In addition to electric vehicles (EVs) that will replace traditional fuel vehicles as the main ground transportation vehicles in the future, unmanned aerial vehicles (UAVs) have also gradually and more recently been widely used for military and civilian purposes. The recent literature estimated that UAVs will become the major means of transport for goods delivery services before 2040, and the development of passenger UAVs will also extend the traditional human ground transportation to low-altitude airspace transportation. In recent years, the literature has proposed the use of renewable power supply, battery swapping, and charging stations to refill the battery of UAVs. However, the uncertainty of renewable power generation cannot guarantee the stable power supply of UAVs. It may even be very possible that a large number of UAVs need to be charged during the same period, causing congestion in charging stations or battery swapping facilities and delaying the arranged schedules of UAVs. Although studies have proposed the method of that employing moving EVs along with wireless charging technology in order to provide electricity to UAVs with urgent needs, the charging schemes are still oversimplified and have many restrictions. In addition, different charging options should be provided to fit the individual need of each UAV. In view of this, this work attempts to meet the mission characteristics and needs of various UAVs by providing an adaptive flight path and charging plan attached to individual UAVs, as well as reducing the power load of the renewable power generation during the peak period. We ran a series of simulations for the proposed flight path and charging mechanism to evaluate its performance. The simulation results revealed that the solutions proposed in this work can be used by UAV operators to fit the needs of each individual UAV.

Wind Preview-Based Model Predictive Control of Multi-Rotor UAVs Using LiDAR

Autonomous outdoor operations of Unmanned Aerial Vehicles (UAVs), such as quadrotors, expose the aircraft to wind gusts causing a significant reduction in their position-holding performance. This vulnerability becomes more critical during the automated docking of these vehicles to outdoor charging stations. Utilising real-time wind preview information for the gust rejection control of UAVs has become more feasible due to the advancement of remote wind sensing technology such as LiDAR. This work proposes the use of a wind-preview-based Model Predictive Controller (MPC) to utilise remote wind measurements from a LiDAR for disturbance rejection. Here a ground-based LiDAR unit is used to predict the incoming wind disturbance at the takeoff and landing site of an autonomous quadrotor UAV. This preview information is then utilised by an MPC to provide the optimal compensation over the defined horizon. Simulations were conducted with LiDAR data gathered from field tests to verify the efficacy of the proposed system and to test the robustness of the wind-preview-based control. The results show a favourable improvement in the aircraft response to wind gusts with the addition of wind preview to the MPC; An 80% improvement in its position-holding performance combined with reduced rotational rates and peak rotational angles signifying a less aggressive approach to increased performance when compared with only feedback based MPC disturbance rejection. System robustness tests demonstrated a 1.75 s or 120% margin in the gust preview's timing or strength respectively before adverse performance impact. The addition of wind-preview to an MPC has been shown to increase the gust rejection of UAVs over standard feedback-based MPC thus enabling their precision landing onto docking stations in the presence of wind gusts.