This student paper discusses basic concept of quadrocopter for purpose of academic platform for testing and development. Paper includes basic overview of construction, stabilization and controlling of quadrocopter. As result of this paper is presented concept of quadrocopter and its constructed prototype with included software console for wireless control under Android system.

The purpose of this thesis is to investigate the potential of delivery drones to contribute to the logistic network (specifically the last-mile), while reducing the environmental impact, in the region of Jämtland, Sweden. This is done by making a current technological overview of delivery drones. Using this summary, it is possible to estimate the possibilities of the technology itself. Best practises are gathered and analysed since it is valuable to learn from the previous experiences. The environmental impact of drone delivery is analysed because the main reason to use drones in Jämtland, should be to reduce the overall environmental impact of the logistic sector. This is combined with the legislation and challenges drones face. Hereby it is possible to determine when drone delivery could reduce the environmental impact of last-mile logistics. Drones or Unmanned Aerial Vehicles (UAV’s) are claimed by many companies (Google, Amazon, UPS) in the logistic sector to be a great technology for last-mile delivery, which is faced with high costs. Delivery drones promise to be cost reducing, fast and eco-friendly. There is a vast amount of research going into planning and economics of delivery drones for the logistic sector. The research on the environmental impact is limited and outcomes are highly dependent on the limitations of the study. Therefore, it is hard to analyse in which situations delivery drones can contribute to the society by lowering the environmental impact of the logistic sector, while following current trends of faster deliveries. The main findings are: the ideal case is a single light package or payload that needs to be transported, without the possibility for other packages to be delivered on the same route. Once package deliveries can be grouped in an efficient route, there are better technologies on the market nowadays. These are further developed and have a lower impact on the environment. Even if drones can be used in a way that benefits the environment and is cost-efficient, they still face challenges. More testing and scientific research is needed to prove drone delivery can be done in a safe manner, that benefits the environment while being cost-effective. / <p>2020-06-10</p>

There is a significant amount of ongoing research on developing multi-agent algorithms for mobile robots. Moving those algorithms beyond simulation and into the real world requires multi-robot testbeds. However, there is currently no easily accessible source of information for guiding the creation of such a testbed. In this thesis, we describe the process of creating a testbed at Purdue University involving a set of unmanned aerial vehicles (UAVs). We discuss the components of the testbed, including the software that is used to interface with the UAVs. We also describe the challenges that we faced during the setup process, and evaluate the UAV platforms that we are using. Finally, we demonstrate the implementation of a multi-agent task allocation algorithm on our testbed.

The forest industry needs an up-to-date overview of certain areas of a forest, to either estimate damages after a storm or assess its overall health. Today, the use of unmanned aerial vehicles (UAV) have exploded. Almost anyone can own one and they are very easy to operate. They are often equipped with accurate sensors and cameras that can be used for several productive applications. This paper investigates if a UAV equipped with positional sensors and a high resolution camera can be used for taking aerial photographs and together with a mobile devices create a coherent orthophoto in real- or near real-time. Three different seam stitching algorithms are tested and evaluated based on speed, accuracy and visual appearance. The results of using a DJI Matrice 100 UAV together with an iPad Air is presented both qualitative and quantitative.

<p>Unmanned aerial vehicles (UAV) have revolutionized data collection in large scale agronomic field trials (10+ ha). Vegetative index (VI) maps derived from UAV imagery are a potential tool to characterize temporal and spatial treatment effects in a more efficient and non-destructive way compared to traditional data collection methods that require manual sampling. The overall objective of this study was to characterize and quantify maize responses to experimental treatments in field-scale research using UAV imagery. The specific objectives were: 1) to assess the performance of several VI as predictors of grain yield and to evaluate their ability to distinguish between fertilizer treatments, and the effects of removing soil and shadow background, 2) to assess the performance of VI and canopy cover fraction (CCF) as predictors of maize biomass at vegetative and reproductive growth stages under field-scale conditions, and 3) to compare the performance of VI derived from consumer-grade and multispectral sensors for predicting grain yield and identifying treatment effects. For the first objective, the results suggest that most VI were good indicators of grain yield at late vegetative and early reproductive growth stages, and that removing soil background improved the characterization of maize responses to experimental treatments. For objective two, overall, CCF was the best to predict biomass at early vegetative growth stages, while VI at reproductive growth stages. Finally, for objective three, performance of consumer-grade and multispectral derived VI were similar for predicting grain yield and identifying treatment effects.</p>

A UAV swarm is simulated using Horizon Simulation Framework. The asset utilized for the swarm agent is a simplified model of the MQ-1 Predator, a large fixed-wing aircraft. The simulated swarm utilizes a decentralized cooperative control approach to command the assets through the use of digital pheromones and a pheromone map. Each vehicle operates at steady-state flight conditions of 36 m/s with an altitude of 1,800 m, and utilize an LQR set-point controller to maneuver through the pheromone map. All pheromone and aircraft related models are written in Python to expand the HSF scripting capability and include airborne scenarios. The simulation study focuses in the variation of three parameters in the repelling pheromone model. The first two are the update and deposit parameters with values of 2, 10, and 18. The third is the threshold parameter with values of 1e-02, 1e-10, and 1e-18. The lower parameter values provide more time-on-target while the higher parameters allow the swarm to search the surrounding area by only visiting the grid-space once.

When developing an aircraft, one of several important aspects is to predict and properly design the dynamic behaviour of the aircraft. This holds for manned aircraft as well as for UAVs. The optimal dynamic behaviour for an aircraft depends on the mission or purpose: for a certain use an aircraft should be agile, other may require a more stable one. In aeronautics, the properties that describe the aircraft ecacy with respect to some task are known as ying qualities, and our goal is to study their dependence on some design parameters. As a test model we use an existing UAV. After deriving its 6-DOF dynamic model and assessing its baseline characteristics, we perform parametric studies. The strategy followed is divided in two steps: the rst consists on analyzing ying qualities sensitivity to changes in model parameters. The second step studies how specific design changes affect model parameters. Because the rst step only depends on the dynamic model form, we verify, by testing two other dierent aircrafts, that conclusions drawn from this step are valid to other congurations. Finally we show how results from parametric studies can be used to improve the UAV ying qualities regarding a certain mission, through the introduction of slight modications on baseline design.

An unmanned aerial vehicle (UAV) swarm allows for a more time-efficient method of searching a specified area than a single UAV or piloted plane. There are a variety of factors that affect how well an area is surveyed. We specifically analyzed the effect both vehicle properties and communication had on the swarm search performance. We used non-dimensionalization so the results can be applied to any domain size with any type of vehicle. We found that endurance was the most important factor. Vehicles with good endurance sensed approximately 90% to 100% of the grid, even when other properties were lacking. If the vehicles lacked endurance, the amount of area the vehicles could sense at a given time step became more important and 10% more of the grid was sensed with the increase in sensed area. The maneuverability of the vehicles was measured as the vehicles' radii of turn compared to the search domain size. The maneuverability mattered the most in the middle-range endurance cases. In some cases 30% more of the grid was searched with improving vehicle maneuverability. In addition, we also examined four communication cases with different amounts of information regarding vehicle location. We found communication increased search performance by at least 6.3%. However, increasing the amount of information only changed the performance by 2.3%. We also studied the impact the range of vehicle communication had on search performance. We found that simulations benefited most from increasing the communication range when the amount of area sensed at a given time step was small and the vehicles had good maneuverability. We also extended the optimization to a multi-objective process with the inclusion of target tracking. We analyzed how the different weightings of the objectives affected the performance outcomes. We found that target tracking performance dramatically changes based on the given weighting of each objective and saw an increase of approximately 52%. However, the amount of the grid that was sensed only dropped by approximately 10%.

Unmanned aerial systems (UAS) are effective for surveillance and monitoring, but struggle with persistent, long-term tracking, especially without GPS, due to limited flight time. Persistent tracking can be accomplished using multiple vehicles if one vehicle can effectively hand off the tracking information to another replacement vehicle. This work presents a solution to the moving-target handoff problem in the absence of GPS. The proposed solution (a) a nonlinear complementary filter for self-pose estimation using only an IMU, (b) a particle filter for relative pose estimation between UAS using a relative range (c) visual target tracking using a gimballed camera when the target is close to the handoff UAS, and (d) track correlation logic using Procrustes analysis to perform the final target handoff between vehicles. We present hardware results of the self-pose estimation and visual target tracking, as well as an extensive simulation result that demonstrates the effectiveness of our full system, and perform Monte-Carlo simulations that indicate a 97% successful handoff rate using the proposed methods.

The development of high-energy-density batteries, advanced sensor technologies, and advanced control algorithms for multirotor electric vertical takeoff and landing (eVTOL) unmanned aerial vehicles (UAVs) has led to interest in using these vehicles for a variety of applications including surveillance, package delivery, and even human transportation. In each of these cases, the ideal vehicle is one that can maneuver in congested spaces, but is efficient for traveling long distances. The combination of wings and vectored thrust make winged eVTOLs the obvious choice. However, these aircraft experience a much wider range of flight conditions that makes them challenging to model and control. This thesis contributes an aerodynamic model and a planning and control method for small, 1-2 m wingspan, winged eVTOLs. We develop the aerodynamic model based on first-principles, lumped-element aerodynamics, extending the lift and drag models to consider high-angle-of-attack flight conditions using models proposed in the literature. We present two methods for generating spline trajectories, one that uses the singular value decomposition to find a minimum-derivative polynomial spline, and one that uses B-splines to produce trajectories in the convex hull of a set of waypoints. We compare the quality of trajectories produced by both methods. Current control methods for winged eVTOL UAVs consider the vehicle primarily as a fixed-wing aircraft with the addition of vertical thrust used only during takeoff and landing. These methods provide good long-range flight handling but fail to consider the full dynamics of the vehicle for tracking complex trajectories. We present a trajectory tracking controller for the full dynamics of a winged eVTOL UAV in hover, fixed-wing, and partially transitioned flight scenarios. We show that in low- to moderate-speed flight, trajectory tracking can be achieved using a variety of pitch angles. In these conditions, the pitch of the vehicle is a free variable that we use to minimize the necessary thrust, and therefore energy consumption, of the vehicle. We use a geometric attitude controller and an airspeed-dependent control allocation scheme to operate the vehicle at a wide range of airspeeds, flight path angles, and angles of attack. We provide theoretical guarantees for the stability of the proposed control scheme assuming a standard aerodynamic model, and we present simulation results showing an average tracking error of 20 cm, an average computation rate of 800 Hz, and an 85% reduction in tracking error versus using a multirotor controller for low-speed flight.