Optimization of Low-Altitude UAV Wing Design Using Comparative Statistical Analysis Method

The control parameters of the unmanned aerial vehicle (UAV) should be carefully designed to improve UAV spraying performance on citrus trees. The present study investigated the optimal droplet distribution control parameters in citrus trees using a UAV and the Taguchi method, of which optimal results were observed with an inverted triangle citrus tree canopy shape, a spraying height of 1.40 m, and a flight speed of 1.0 m/s. Among the discussed control parameters, the flight speed presented the most significant effect with a contribution percentage of 74.0%. The established multiple regression model predicted an optimal spraying height of 1.27 m and a maximum droplet density of 35.39 droplets/cm2. In addition, the effects of individual control parameter on the droplet density of the lower layer of citrus trees were systematically analyzed, of which inverted triangle shape more significantly affected the droplet density of the lower layer and presented an 82.0% increase in droplet density as compared to the triangle shape. An improvement of 59.6% in the lower layer droplet density was observed at a spraying height of 1.40 m. In addition, the other spraying heights did not present significant differences in their coefficient of variation (CV) values. Keywords: droplet density, citrus, Taguchi method, aerial spraying, unmanned aerial vehicle (UAV), plant protection DOI: 10.25165/j.ijabe.20191204.4139 Citation: Hou C J, Tang Y, Luo S M, Lin J T, He Y, Zhuang J J, et al. Optimization of control parameters of droplet density in citrus trees using UAVs and the Taguchi method. Int J Agric & Biol Eng, 2019; 12(4): 1–9.

UAV (Unmanned aerial vehicle) has become an optimal platform for surveying and mapping applications. One of the most important performances of UAV is flight speed. It is a key factor to impact surveying safety and efficiency within the limited flight endurance. Researchers have not treated UAV's flight speed in much detail, only focus on the image motion in order to limit the maximum flight speed in path. In this paper, an optimal flight speed estimation method is proposed, which is according to the multicopter flight mechanics and aerial photogrammetry theories. Through this method, the energy consumption characteristics of surveying UAV in the field operation can be obtained. A safe and efficient flight speed also can be determined. To verify the feasibility of the method, different test scenarios are carried out in the SITL (Software in the loop) simulation and obtained promising results.

As the combination of FW(Fixed-Wing) and VTOL(Vertical-Taking-Off-and-Landing) UAV (Unmanned Aerial Vehicle), the hybrid drone is more accepted as its multipurpose application. With the aid of CFD method for test and RSM (Response Surface Methodology) for optimization, this article presents new concept of canard wings that were integrated to the fuselage of the UAV. It combines the conventional delta wing with winglet and the canard configuration. Based on the requirements and the limitations of the design, the lift was optimized and distributed respectively to the main wing (90-95%) and to the front wing (5-10%). Multi-level optimization approach using RSM was developed for the optimization. At the first level, we optimize the delta wing, then a second level is applied to evaluate the final design of the canard wing from the previous step. By comparison with the initial design, this concept provides enough lift with less drag in cruise model. Besides, the control surface of the canard wing in the front could replace the tail wing that was used for trimming and dynamic controls which turns the drone into a tailless vehicle which reduce also the weight of the UAV.

As a quite interesting subject, there are an increasing number of researches about UAV (Unmanned Aerial Vehicle) in the literature.These robots are quite suitable to dull, dirty and dangerous missions (e.g., over flights at low altitude, especially at night, and longrunning operations, which exposes the crew to extreme fatigue).Thus, an important application of these vehicles is the search operations involving multiple UAVs.However, despite the huge potential use of the UAVs, cooperative search operations with this kind of flying robots are not yet occurring.This research topic is a multidisciplinary area of study in its beginning.Several issues can be studied related to cooperative UAV for search operations, such as centralized versus decentralized control, path planning for cooperative flies, agent reasoning for UAV tactical planning, safety assessments, reliability in automatic target reconnaissance by cameras, agent coordination mechanisms applied for UAV cooperation and the application itself (e.g., patrolling, remote sensing, search and rescue operations supporting, etc.).This paper proposes a model that combines path planning algorithms, search patterns and multiagent coordination techniques to obtain a cooperative UAV model.Different path planning algorithms were studied aiming to get the most suitable to these kinds of operations, and the conclusions are presented.In addition, official documents of Search and Rescue operations are also studied in order to know the best practices already established for this kind of operations, and, finally, an overview of the coordination multi-agent theory is presented and evaluated to achieve the UAV coordination.The great goal for cooperative UAV is to achieve such performance that the performance of the group overcomes the sum of the individual performances isolatedly.Comparing two cooperative UAVs with two non-cooperative UAVs, simulations of previous work showed an average increase in efficiency by about 100%.Besides, another aspect was simulated and evaluated: the impact of the density of the initial sweep.The computational simulations show a high potential of reducing even more the time of search.Furthermore, it is worth noting that the usefulness of cooperative UAVs is not only the increase in efficiency, but also the reduction of costs and risks for the crew.

CFD study of the effect of leading-edge tubercles on the aerodynamic characteristics of a small UAV based on eppler 186 airfoils

This study presents a surrogate-model-assisted Quasi-Newton optimisation framework for simultaneously improving the aerodynamic performance and radar stealth characteristics of an unmanned aerial vehicle (UAV). High-fidelity computational fluid dynamics (CFD) and computational electromagnetics (CEM) simulations are integrated through surrogate models generated via a face-centred central composite design within a design of experiments framework. Quadratic polynomial response surface equations are constructed for key aerodynamic and radar cross-section (RCS) metrics, enabling analytical gradient evaluation. A gradient-based quasi-Newton method with Broyden–Fletcher–Goldfarb–Shanno Hessian updates is employed to minimise a scalarised objective function combining normalised maximum lift coefficient, overall RCS and frontal RCS. Constraints are imposed on the lift-to-drag ratio ( $L/D \geq 10$ ) and static longitudinal stability ( ${C_{m0}} \geq 0$ ). Analytical derivatives from the response surface equations (RSEs) eliminate the need for direct numerical differentiation of CFD/CEM outputs, reducing computational cost and eliminating simulation noise. An interior-point sequential quadratic programming strategy is used to ensure satisfaction of nonlinear constraints during the optimisation process. The optimised UAV design demonstrates a $12{\rm{\% }}$ increase in maximum lift coefficient and a $30{\rm{\% }}$ reduction in both overall and frontal RCS compared to the baseline configuration. The results are confirmed through high-fidelity CFD and RCS simulations and are further validated experimentally in an anechoic chamber, with close agreement across all measured frequencies. The proposed methodology provides an efficient and experimentally verified approach for integrated aerodynamic and stealth optimisation in UAV design.

Due to the characteristics of high maneuverability, low cost, and high line-of-sight (LoS) probability, unmanned aerial vehicles (UAVs) have attracted more and more attention in the field of communications recently. Using UAVs as a mobile aerial base station (BS), commonly referred to as UAV-aided communication, can provide a quick and flexible solution for sudden communication needs and coverage holes. To maximize the coverage effect, effective three-dimensional (3-D) deployment is crucial. As a result of the complexity of air to ground channels and interference between UAV s and BSs, 3- D placement of UAVs is currently one of the main challenges for UAV-aided communications. In this paper, Taguchi's method is used for 3-D placement optimization of multiple UAV s, aiming to make up for the coverage holes and assist the ground cellular network to cover the area as seamlessly as possible. Simulation results indicate that Taguchi's method can effectively improve the coverage ratio of the area by optimizing the 3-D placement of UAV s, and the optimization effect is better than simulated annealing alagrithm.

Background: The method was proposed to optimize the warpage and volume shrinkage of the blade of a plant protection unmanned aerial vehicle (UAV) during injection molding. The influence of molding defects on the geometric parameters of airfoil was analyzed from two aspects of blade geometry structure and molding process based on blade element theory. This paper focuses on reducing the warpage and volume shrinkage of the blade in the production process and improving molding quality through moldflow analysis and calculation. Objective: The purpose of this patent study was to combine injection molding CAE with orthogonal test and response surface method to reduce product warpage and volume shrinkage, improve dimensional accuracy and production efficiency, and combine fluid dynamics simulation experiments to prove the experiment's reliability. Methodology: The optimization method of injection molding process parameters was proposed based on the orthogonal test and response surface method, combining the principles of aerodynamics mathematics and polymer injection molding. The Taguchi experiment was designed with melt temperature injection time and mold temperature pressure holding time as the optimization variable. The Box-Behnken Design (BBD) experiment was designed, and the response surface model was established with Design-Expert software to analyze the mapping relationship between process parameters and warpage and volume shrinkage. Moldflow software was used for flow analysis on the basis of analysis by response surface method, and the deformed parts were analyzed by computational fluid dynamics (CFD) with ANSYS software to prove the reliability of the calculation process. Results: The warpage and volume shrinkage of the product can be significantly reduced based on the orthogonal test and response surface model. The aerodynamic performance of the blade can be improved by optimization injection molding, making the actual production of the blade close to the original design blade model to the greatest extent and reducing the actual production trial and error cost. Conclusion: An optimization method of the injection molding process of the UAV rotor was proposed based on the response surface method and blade element theory. Through the systematic design and optimization of injection molding parameters, the molding quality and performance of the rotor can be improved, which provides a new way to optimize the dynamic components of massproduced UAVs.

U NMANNED Aerial Vehicles (UAVs) hold good promise for autonomously carrying out complex civilian and military operations. However, many of these missions require them to fly at low altitudes, making them vulnerable to collision with both stationary as well as moving obstacles. Hence, it is vital that UAVs are equipped with autonomous capability to sense and avoid collisions, especially for the pop-up threats. When such a threat is sensed and a collision is predictedwithin a short time ahead, theUAV should be able to react and maneuver away quickly so that the collision is avoided. An algorithmwhich can assure such amaneuver is called a “reactive collision avoidance algorithm.” Since the available reaction time in such a scenario is usually is small and UAVs are usually limited by computational resources, such an algorithm should also be computationally very efficient (it should preferably be noniterative). It is also required that whilemaneuvering away, it should notmaneuver toomuch away from the obstacle either. This is both to avoid collision from other nearby obstacles and not to compromise on the overall mission objective. There are various attempts in the literature to develop algorithms for collision avoidance purpose, many of which are inspired from global path planning algorithms. The artificial potential field method is such an approach where the motion of the vehicle is guided under the influence of a potential field. The potential field (which is essentially a cost function) is designed in such a way that obstacles have repulsive fields while the destination has an attractive field. The safe path of the UAV is then found by optimizing the carefully selected cost function. To tune this basic philosophy for reactive collision avoidance, a model predictive control-based algorithm has been proposed in the literature. This algorithm essentially assures path following under safe conditions (i.e. if no collision is predicted in the near future) and invokes the potential field function when new collisions are sensed. However, in potential field based techniques the associated optimization process is typically done in an iterative manner. Because of this they are usually computationally intensive and hence are not suitable for reactive collision avoidance of airborne UAVs in general. A promising algorithm in collision avoidance and global path planning is the philosophy of rapidly-exploring random tree (RRT) [1], which has also been used for reactive collision avoidance. However, there are many concerns about the RRT approach, which can largely be attributed to the random nature of the algorithm. For example, the path predicted by RRT is usually a sting of connected straight lines that does not reflect the path followed by a vehicle with nonholonomic constraints. More important, it is a probabilistic approach and hence there is no guarantee of finding a feasible path within a limited finite time. Other graph search algorithms such as best-first search are also implemented for reactive collision avoidance [2]. However, this is not systematic approach and could result in the algorithm searching far too many nodes under some conditions. Moreover, precomputing motion primitives and saving them in a lookup table is infeasible for UAVs, which are usually resource-limited. An interesting perspective to collision avoidance problem is the minimum effort guidance [3], where an optimal control-based approach has been proposed after applying the collision cone philosophy to detect collisions. This method is computationally nonintensive as a closed form solution has been proposed. Even though this is an interesting idea, by minimizing the lateral acceleration, perhaps it imposes unwanted extra constraint on the problem formulation as reactive collision avoidance problems do not necessarily have to be carried out with minimum lateral acceleration. More important, one can observe that this formulation only assures position guarantee and no constraint is imposed on the velocity vector. Hence, even though it guides the vehicle to a carefully selected target point on the safety boundary (we call it the “aiming point”), it causes the vehicle to maneuver until this point. This can be risky as the vehicle may enter the safety ball before reaching the aiming point. Even though the collision cone based aiming point philosophy is a very good idea, the authors of this Note strongly believe that instead of only position guarantee, rather the velocity vector should be aligned towards the aiming point as soon as a collision is detected (which will automatically lead to position guarantee as well). Towards this objective, two new nonlinear guidance laws are proposed in this Note, which are named as nonlinear geometric guidance (NGG) and differential geometric guidance (DGG). These guidance laws are inspired by the philosophy of “aiming point guidance” (APG) [4], which has been proposed in missile guidance literature. It turns out that the APG is a simplified case of the NGG where the associated since function is replaced by its linear approximation (hence, for a systematic discussion, it is renamed as linear geometric guidance (LGG) in this Note). Both of the guidance algorithms proposed in this Note quickly align the velocity vector of the UAValong the aiming point within a part of the available time-togo, which ensures quick reaction and hence safety of the vehicle. The main feature of this philosophy is that they effect high maneuvering at the beginning, causing the velocity vector of the UAV to align with the aiming point direction quickly and then settling along it. Therefore there is no need to maneuver all the way until the aiming point is reached and hence the chance of the UAV entering into the safety ball is minimized. Using the point of closest approach (PCA) [5], the proposed NGG andDGGalgorithms have also been extended for collision avoidance with moving obstacles in both cooperative as well as ignorant scenarios. Mathematical correlations between the guidance laws have also been established, which show that the NGG and DGG are exactly correlated to each other with appropriate gain selections, while the LGG is an approximation of DGG. A “sphere-tracking algorithm” is also proposed in this Note where the UAV is guided to track the surface of the safety spherewhenever a brief violation of the safety boundary occurs after reaching the aiming point because of the location of the next aiming point (which may include the target in Presented as Paper 2010-8315 at the AIAA Guidance, Navigation and Control, Toronto, 2–5August 2010; received 26May 2010; revision received 4 October 2010; accepted for publication 6 October 2010. Copyright © 2010 by Radhakant Padhi. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission. Copies of this paper may be made for personal or internal use, on condition that the copier pay the $10.00 per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923; include the code 0731-5090/11 and $10.00 in correspondence with the CCC. ∗Former Project Assistant, Department of Aerospace Engineering; anushamujumdar87@gmail.com. Associate Professor, Department of Aerospace Engineering; padhi@ aero.iisc.ernet.in. JOURNAL OF GUIDANCE, CONTROL, AND DYNAMICS Vol. 34, No. 1, January–February 2011

Report No. 648, Design charts for predicting downwash angles and wake characteritics behind plain and flapped wings: by Abe Silverstein and S. Katzoff. 25 pages, illustrations, 23 × 29 cms. Washington, Government Printing Office, 1939. Price 15 cents

The classification of drones - both military and civilian according to the relevant criteria - is presented. Block diagrams of a typical unmanned aerial vehicle and its power system are shown. Typically, a drone power system of the "micro", "mini" or "short range" category, according to the UVS international classification, is a set of DC / DC converters with a microprocessor control system. The power source for such aircraft is usually a battery, less often - fuel cells. During the flight, the engine speed is variable, and its change depends not only on changes in flight speed or direction, but also on weather conditions, such as wind, as the stabilization system constantly aligns the drone. This leads to the fact that the power supply system of such a device is actually a significant part of the time in transition condition. This leads to a significant content of the component of exchange energy that battery consumed which in turn will increase losses, and thus reduces the range of the unmanned aerial vehicle. The phenomenon of the occurrence of exchange power in the power supply systems of electric vehicles which powered from DC sources and specifically in the power supply system of an unmanned aerial vehicle is analyzed. The time diagrams of current, voltage, active and inactive components of power consumed from the mains power supply network of electric locomotive DE1 are illustrated. It can be seen that with a sharp change in the modes of operation of traction units there is a significant component of exchange power in the motor-network system. Such processes are typical for almost any electric vehicle and are associated with the presence of a significant number of reactive elements in the power supply systems, as well as frequent changes in load parameters. A block diagram of a typical unmanned aerial vehicle is presented. The unmanned aerial vehicle system consists of three parts: the air part, the unmanned aerial vehicle itself, the ground control station, which can be autonomous or manned, the control system, which provides communication and data transmission. The block diagram of the power system of a typical unmanned aerial vehicle is presented A simplified schematic diagram of the DC motor power supply system is presented. The relations for determining the amount of exchange power in the power supply system of an unmanned aerial vehicle are derived. It is concluded that to reduce the impact of this phenomenon, it is necessary to modify the power supply system by adding compensation units of inactive power component.

Plasma flow control (PFC) is a promising active flow control method with its unique advantages including the absence of moving components, fast response, easy implementation, and stable operation. The effectiveness of plasma flow control by microsecond dielectric barrier discharge (μs-DBD), and by nanosecond dielectric barrier discharge (NS-DBD) are compared through the wind tunnel tests, showing a similar performance between μs-DBD and NS-DBD. Furthermore, the μs-DBD is implemented on an unmanned aerial vehicle (UAV), which is a scaled model of a newly developed amphibious plane. The wingspan of the model is 2.87m, and the airspeed is no less than 30m/s. The flight data, static pressure data, and Tufts images are recorded and analyzed in detail. Results of the flight test show that the μs-DBD works well on board without affecting the normal operation of the UAV model. When the actuators are turned on, the stall angle and maximum lift coefficient can be improved by 1.3° and 10.4%, and the static pressure at the leading edge of the wing can be reduced effectively in a proper range of angle of attack, which shows the ability of μs-DBD to act as plasma slats. The rolling moment produced by left-side μs-DBD actuation is greater than that produced by the maximum deflection of ailerons, which indicates the potential of μs-DBD to act as plasma ailerons. The results verify the feasibility and efficacy of μs-DBD plasma flow control in a real flight and lay the foundation for the full-sized airplane application.

The use of unmanned aerial vehicles (UAVs) for natural resource applications has increased considerably in recent years due to their greater availability, the miniaturization of sensors, and the ability to deploy a UAV relatively quickly and repeatedly at low altitudes. We examine in this paper the potential of using a small UAV for rangeland inventory, assessment and monitoring. Imagery with a ground resolved distance of 8 cm was acquired over a 290 ha site in southwestern Idaho. We developed a semiautomated orthorectification procedure suitable for handling large numbers of small-footprint UAV images. The geometric accuracy of the orthorectified image mosaics ranged from 1.5 m to 2 m. We used object-based hierarchical image analysis to classify imagery of plots measured concurrently on the ground using standard rangeland monitoring procedures. Correlations between imageand ground-based estimates of percent cover resulted in r-squared values ranging from 0.86 to 0.98. Time estimates indicated a greater efficiency for the image-based method compared to ground measurements. The overall classification accuracies for the two image mosaics were 83 percent and 88 percent. Even under the current limitations of operating a UAV in the National Airspace, the results of this study show that UAVs can be used successfully to obtain imagery for rangeland monitoring, and that the remote sensing approach can either complement or replace some ground-based measurements. We discuss details of the UAV mission, image processing and analysis, and accuracy assessment.

Airborne transport of herbicides from UAV applications under varied operational and meteorological conditions.

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.