Unmanned Aerial Vehicle

There are intensifying actions to combat the mosquito Aedes which is admittedly responsible for the transmission of diseases: chikungunya, dengue and zika. Among these dengue is a recurring problem that affects the entire world, especially the tropical areas. It is considered one of the world’s greatest public health problems by the World Health Organization, which estimates that approximately 390 million people get infected by this disease each year worldwide. In Brazil, since the first report of the disease in 80’s, dengue has continually occurred, alternating epidemic periods with peaks of increasing disease. Therefore, this study aims to assess the feasibility of using unmanned aerial vehicle, popularly known as drone, in aid of the dengue control program executed in Maringá - PR. In conclusion the use of this aircraft is feasible, since it is an economically attractive investment due to its low cost against the annual investment with manpower.

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...

Unmanned Aerial Vehicles (UAVs) are quickly becoming the future of payload delivery. Over the past few years, many designs have surfaced with a variety of different solutions to this problem. Current systems are often bulky and designed for a specific purpose. This makes them difficult to adapt to new environments or payloads. This paper details the design and development of a novel, reconfigurable vehicle system that is able to adapt to a variety of environments and payload dimensions. The system consists of multiple individual mobile modules equipped with rotors, which work collaboratively to grasp a single payload. Each module has the capability to act independently of one another and can move along a 2D plane in any direction, like a mobile robot. Grasping is accomplished using a tethering system which joins adjacent modules together and allows them to clamp onto a payload. The payload becomes the backbone that offers rigidity to the formed drone. Thus, upon reconfiguration, the system is essentially a rigid body with the modules on the exterior surrounding the payload. The system could then takeoff and transport the package to a different location. Significant testing was carried out with the designed prototype, and open loop takeoff was achieved, proving the feasibility of the concept. The system has been experimentally tested to provide up to 14 N per vehicle with a theoretical capacity of 20 N. This results in each module having an estimated payload of 500 g with 25% thrust capacity still available.

Nonlinear synthesis is developed to solve the tracking control problem restricted to a two degrees-of-freedom (DOF) underactuated mechanical manipulator where position measurements are the only available information for feedback. A local controller is derived by means of a certain perturbation of the differential Riccati equations, appearing in solving the control problem for the linearized system. Stabilizability and detectability properties of the control system are thus ensured by the existence of the proper solutions of the unperturbed differential Riccati equations, and hence the proposed synthesis procedure obviates an extra verification work of these properties. Due to the nature of the approach, the resulting controller additionally yields the desired robustness properties against unknown but bounded external disturbances. The desired trajectory is centered at the upright position where the manipulator becomes a nonminimum-phase system. Simulation results made for a double p...

International audienceIn the Leviathan model, agents form opinions about each other. During dyadic meetings, they directly influence each other, or talk about peers through gossip. Speakers highly valued by their listeners are more influential. Moreover, they are subject to a vanity process; if an agent feels undervalued, then he decreases his opinion about the despising agent and on the contrary he increases his opinion about a compliment-giver agent. This model produces several different patterns depending on the parameter values, but the average opinion is always negative whatever the emerging pattern. As a consequence, the gossips tend to be mostly negative. Since this is not in complete accordance with the social-psychology literature indicating that both mostly negative and positive gossip can be observed, we investigate the changes to operate to obtain also mostly positive gossips. We found that when the vanity process is less triggered by agents held in low esteem, the avera...

Performance characteristic of an Unmanned Air Vehicle (UAV) is investigated using a newly developed heuristic approach. Almost all flight phases of any air vehicle can be categorized into trim and maneuvering flights. In this paper, a new envelope called trim-ability envelope, is introduced and sketched within the conventional flight envelope for a small UAV. Optimal maneuverability of the intended UAV is evaluated for minimum time pull-up and turn maneuvers. For both the trim and the maneuver problems, the nonlinear 6DOF dynamic models as well as the vehicle constraints are considered. A heuristic based constrained optimization approach is developed to solve both the trim and maneuver problems. Several interesting performance characteristics are extracted. The results are indicative of a good potential for the proposed algorithm to handle complex constrained optimization problems in aerospace engineering .

The rapid advancement of drone technology has sparked a growing interest in its potential applications, with one intriguing question emerging: Can a drone lift a person or carry human passengers? This research paper delves into the feasibility and challenges associated with the prospect of human transportation by drones. Through a comprehensive review of existing literature, analysis of current technological capabilities, and exploration of regulatory considerations, we aim to provide a nuanced understanding of the possibilities and limitations in this evolving field.

Drone‐based remote sensing is a promising new technology that combines the benefits of ground‐based and satellite‐derived forest monitoring by collecting fine‐scale data over relatively large areas in a cost‐effective manner. Here, we explore the potential of the GatorEye drone‐lidar system to monitor tropical forest succession by canopy structural attributes including canopy height, spatial heterogeneity, gap fraction, leaf area density (LAD) vertical distribution, canopy Shannon index (an index of LAD), leaf area index (LAI), and understory LAI. We focus on these variables’ relationship to aboveground biomass (AGB) stocks and species diversity. In the Caribbean lowlands of northeastern Costa Rica, we analyze nine tropical forests stands (seven second‐growth and two old‐growth). Stands were relatively homogenous in terms of canopy height and spatial heterogeneity, but not in their gap fraction. Neither species density nor tree community Shannon diversity index was significantly cor...

The Multi-Robot Task Allocation problem is the situation where some tasks and robots are given, then assignments between them must be found in order to optimize a certain measure (e.g. allocate the maximum number of tasks, etc.). We propose a generic framework to address heavily constrained MRTA problems. Some objective functions are proposed and extensively tested on ten datasets, which is our main contribution. Also, two allocation methods – exact and heuristic – are implemented, in order to compare values of adopted evaluation metrics. Performed simulations, obtained results, and comparative study show the effectiveness of the solution, even with a large number of robots and tasks.