An architecture for UAV team control

This paper considers the problem of building environment models by fusing information gathered by robotic platforms with human perceptual information. Rich environment models are required in real applications for both autonomous operation of robots and to support human decision-making. Hierarchical models are well suited to represent complex environments because they: offer multiple abstractions of the available information to support analysis and decision-making, and permit the incorporation of higher-level human observations. The contributions of this paper are two-fold: (1) development of a probabilistic three-level environment model for distributed information gathering, and (2) experimental demonstration of fully decentralized, cooperative human-robot information gathering using an outdoor sensor network comprised of an unmanned air vehicle, a ground vehicle, and two human operators. Several information exchange patterns are presented which qualitatively demonstrate human-robot information fusion.

In the last few years, radio technologies for unmanned aircraft vehicle (UAV) have advanced very rapidly. The increasing need to fly unmanned aircraft systems (UAS) in the national airspace system (NAS) to perform missions of vital importance to national security, defense, and science has pushed ahead the design and implementation of new radio platforms. However, a lot still has to be done to improve those radios in terms of performance and capabilities. In addition, an important aspect to account for is hardware cost and the feasibility to implement these radios using commercial off-the-shelf (COTS) components. UAV radios come with numerous technical challenges and their development involves contributions at different levels of the design. Cognitive algorithms need to be developed in order to perform agile communications using appropriate frequency allocation while maintaining safe and efficient operations in the NAS and, digital reconfigurable architectures have to be designed in order to ensure a prompt response to environmental changes. Command and control (C2) communications have to be preserved during “standard” operations while crew operations have to be minimized. It is clear that UAV radios have to be software-defined systems, where size, weight and power consumption (SWaP) are critical parameters. This paper provides preliminary results of the efforts performed to design a fully digital radio architecture as part of a NASA Phase I STTR. In this paper, we will explain the basic idea and technical principles behind our dynamic/adaptive frequency hopping radio for UAVs. We will present our Simulink model of the dynamic FH radio transmitter design for UAV communications and show simulation results and FPGA system analysis.

It is increasingly popular to use unmanned air vehicle (UAV) and network model to detect targets and gather information in water resources, environment and hydraulic engineering field. The intermittent connectivity in a dynamically mobile UAV network (UAVN) makes it challenging to efficiently gather sensed target data. Centralized and distributed fusion rules in traditional fusion systems are often based on global message connectivity. This paper investigates the severe communication of sensed data from a set of mobile survey UAVs to a fusion center in large outdoor environment. Then a centralized fusion model for UAVN is proposed to schedule the UAVs to collect detection data to solve water problems with the dynamic connectivity of links in UAV network. Based on this model, a specific fusion method named Capacity Fusion Rule (C-FR) is provided for the fusion center. C-FR utilizes the information theory to discuss the fusion performance in view of link connectivity. Evaluation shows that the proposed C-FR can realize the centralized fusion system with simpler formulas and express the numerical relationship between outage probability, outage capacity, and connectivity probability. Otherwise, computational complexity of traditional centralized fusion rule and C-FR are also analyzed.

In recent years, networks for unmanned aircraft vehicle (UAV)-assisted device-to-device (D2D) communication have attracted attention, as they can be employed for emergency communication in the event of a large-scale disaster. However, many existing UAV-assisted D2D communication technology strategies neglect the UAV energy consumption during flight. Further, only the links between the UAV and ground devices are evaluated, without assumption of a wide information diffusion range. Herein, we propose a novel multihop D2D communication method that exploits UAV technology and uniquely designed virtual cells to achieve information diffusion to remote areas. Moreover, we establish a model to evaluate the trade-off between the UAV flight energy consumption and the communication performance between the UAV and mobile devices. By employing the constructed model and demonstrating the energy efficiency trend, we confirm the effectiveness of the proposed method combining multihop D2D communication and UAV technology.

A novel battery thermal management system for an unmanned aerial vehicle using the graphene directional heat transfer structure

Designing and building of the unmanned aircraft, especially light and ultra light vehicles, is mainly performed using the experience gained when constructing the flying models. There have not been uniform principles of building and exploiting of the mini and micro UAV (Unmanned Aerial Vehicle) in the form of regulations similar to those for manned airplanes. The unmanned vehicles of these classes in terms of their abilities and attractive price are more frequently exploited using the same air area as manned airplanes performing missions over the inhabited areas. An urgent necessity arises to work out the norms of flight suitability of the mini and micro unmanned aerial vehicles. The work contains the analysis of suitability of the current aviation regulations to determine the requirements for the mini unmanned vehicles. The work concentrates on the phenomenon of determining the symmetrical loads from the maneuvers and the turbulence atmosphere. The result of this analysis is the Limit Maneuver Envelope, Limit Gust Envelope and Limit Combined Envelope for mini UAV. The analyzed flight states allowed selecting the so called design cases which can become a basis for determining the norms of loading of mini unmanned aerial vehicles which can constitute the beginning of the regulations for building of the unmanned aerial vehicles of this class.

In this contribution two different types of broadband antennas namely a logarithmic-periodic dipole antenna (LPDA) and a transversal electromagnetic (TEM) horn antenna for ground penetrating synthetic aperture radar sensors are presented. These antennas are designed to be mounted on an unmanned aircraft vehicle (UAV). The antennas are evaluated in terms of their matching, radiation pattern pulse response and weight. Finally, an integration example of one of these antennas is shown on an UAV.

The high portability of small Unmanned Aircraft Vehicles (UAVs) makes them play an important role in surveillance and reconnaissance tasks, so the military and civilian desires for UAVs are constantly growing. Recently, we have developed a real-time video exploitation system for our small UAV which is mainly used in forest patrol tasks. Our system consists of six key models, including image contrast enhancement, video stabilization, mosaicing, salient target indication, moving target indication, and display of the footprint and flight path on map. Extensive testing on the system has been implemented and the result shows our system performed well.

Recent developments in Unmanned Aircraft Vehicles (UAVs), thermal imaging, and Auto-machine learning (AutoML) have shown high potential for precise wildlife surveys but have rarely been studied for habitat assessment. Here, we propose a framework that leverages these advanced techniques to achieve cost-effective habitat quality assessment from the perspective of actual wildlife community usage. The framework exploits vision intelligence hidden in the UAV thermal images and AutoML methods to achieve cost-effective wildlife distribution mapping, and then derives wildlife use indicators to imply habitat quality variance. We conducted UAV-based thermal wildlife surveys at three wetlands in the Rainwater Basin, Nebraska. Experiments were set to examine the optimal protocols, including various flight designs (61 and 122 m), feature types, and AutoML. The results showed that UAV images collected at 61 m with a spatial resolution of 7.5 cm, combined with Faster R-CNN, returned the optimal wildlife mapping (more than 90% accuracy). Results also indicated that the vision intelligence exploited can effectively transfer the redundant AutoML adaptation cycles into a fully automatic process (with around 33 times efficiency improvement for data labeling), facilitating cost-effective AutoML adaptation. Eventually, the derived ecological indicators can explain the wildlife use status well, reflecting potential within- and between-habitat quality variance.

This paper presents a new evolutionary planner for optimizing the input commands of multiple Unmanned Aircraft Vehicles (UAVs) in target search missions. This planner minimizes the target detection time and maximizes the UAVs performance, given 1) the uncertainty in the target location and sensor information, and 2) the UAV motion and sensorial payload models. On one hand, it calculates the detection time related criteria using Bayesian theory to handle the uncertainty of the problem. On the other hand, it measures the UAVs performance against a real kinematic model that takes into account some environmental effects. Besides, it exploits the typical versatility and good performance of evolutionary algorithms to tackle this search problem as a multi-objective and multi-stepped receding horizon controller, capable of providing acceptable long term (less-myopic) decisions due to a novel optimization criterium that weights the future expected observations with the UAV manoeuvrability constraints. All these properties let it handle successfully the minimum time target detection task in real world scenarios, as the results analyzed in this paper, obtained over different setups, show.

In the collaborative situation assessment of manned aircraft vehicle (MAV) and unmanned aircraft vehicle (UAV), because of the uncertainty, dynamic and fuzzification of the battlefield environment information and associated information, how to obtain relatively accurate situation assessment results is a problem to be solved. In this paper, a dynamic intuitionistic fuzzy cognitive map (DIFCM) is formed by combining intuitionistic fuzzy sets with fuzzy cognitive map. A MAV-UAV cooperative situation assessment model is established based on DIFCM. Considering the human factor, the workload of pilot is introduced into the evaluation model as a concept node. Each node in the model is composed of membership degree and non membership degree, and the node states are compared by the score and accuracy. Through the simulation experiment, the rationality and feasibility of the evaluation model are verified. Different initial battlefield information, environment variables and node hesitation are set up to analyze the situation assessment output. The experimental results show that the situation assessment model can objectively evaluate the battlefield situation and provide more decision-making information for commanders.KeywordsSituation assessmentMAV-UAV collaborationDynamic intuitionistic fuzzy cognitive mapsPilot workload

<p>This study investigates the innovative application of neural networks algorithms in the aviation industry's mechanical design process, motivated by the pursuit of creating a more accurate and efficient method for performance prediction. Traditional approaches, such as computational fluid dynamics (CFD) simulations based on solving Navier-Stokes’s equations, demand substantial computational power and often exhibit limited accuracy, particularly when compared with complex geometries. The state-of-the-art review unveils a growing research trend advocating for data-driven methodologies to revolutionize design practices, addressing the limitations of conventional techniques. The primary objective of this study is to explore how neural network algorithms can overcome the drawbacks of CFD simulations, offering a more effective alternative for predicting the performance of airfoils. To achieve this objective, we conducted a performance analysis of airfoils using neural network algorithms. The results presented a promising avenue for a more accurate and efficient performance prediction method through digital twinning. The study highlights the advantageous features of neural network methods in unmanned aircraft vehicles (UAV) component mechanical design, showcasing their potential to outperform traditional methods and offering practical recommendations for integration into the design process.</p>

The purpose of this LDRD is to develop technology allowing warfighters to provide high-level commands to their unmanned assets, freeing them to command a group of them or commit the bulk of their attention elsewhere. To this end, a brain-emulating cognition and control architecture (BECCA) was developed, incorporating novel and uniquely capable feature creation and reinforcement learning algorithms. BECCA was demonstrated on both a mobile manipulator platform and on a seven degree of freedom serial link robot arm. Existing military ground robots are almost universally teleoperated and occupy the complete attention of an operator. They may remove a soldier from harm's way, but they do not necessarily reduce manpower requirements. Current research efforts to solve the problem of autonomous operation in an unstructured, dynamic environment fall short of the desired performance. In order to increase the effectiveness of unmanned vehicle (UV) operators, we proposed to develop robots that can be 'directed' rather than remote-controlled. They are instructed and trained by human operators, rather than driven. The technical approach is modeled closely on psychological and neuroscientific models of human learning. Two Sandia-developed models are utilized in this effort: the Sandia Cognitive Framework (SCF), a cognitive psychology-based model of human processes, and BECCA, a psychophysical-based model of learning, motor control, and conceptualization. Together, these models span the functional space from perceptuo-motor abilities, to high-level motivational and attentional processes.

<p>Unmanned Aircraft Systems (UAS) can be used to enhance monitoring of a wide range of environmental parameters, including acquiring data on various types of hydro-geomorphic phenomena.</p><p>Their capabilities to provide on demand images and videos of high resolution, are particularly useful in the case of flash flood phenomena, which occur in spatial and temporal scales that do not favor traditional monitoring processes.</p><p>In this work, flow velocity is estimated using aerial imaging acquired by means of an Unmanned Aircraft Vehicle (UAV) as well as ground observations during the catastrophic flash flood event of November 2017 in Mandra, Greece.</p><p>In these imaging detailed tracing of various floating objects and particles such as light trash, debris etc. was carried out using multiple high-resolution video frames with specific time marks. Water velocity estimations were also cross-examined using flood mark-derived velocity hydraulic heads extracted by ground observations after the flood.</p><p>The analysis was applied at a variety of locations across the study area, leading to a map of velocities for parts of the floodplain. Velocity values varied significantly depending on location, reaching up to 10m/s.</p><p>The UAS proved to be very useful for the collection of important information for an extended area during the flood since a large portion of it was inaccessible due to road closures and safety issues. Nevertheless, the approach comes with certain limitations, including flight regulations, safety precautions and that rainfall is at a level that allows the deployment of a UAV during a flash flood.</p><p>The findings show that the integration of aerial with ground observations in post-flood analysis contributes the completeness and accuracy of datasets regarding specific flash flood parameters and in the future could become a useful source of information, especially in data-poor regions.</p>

This paper proposed fuzzy PID controller which prevents compressor surge and reduces the acceleration time of the fuel flow control system for turbo-jet engine. The fuzzy PID controller is to stabilize the unmanned aircraft vehicle upon occurring unexpected engine surge. This controller is designed by applying fuzzy PID control algorithm, inferred by applying Mamdani’s inference method and defuzzified by using the center of gravity method. Fuzzy inference results are used as the fuel flow control inputs to prevent compressor surge and flame-out for turbo-jet engine. The controller is designed to converge to the desired speed quickly and safely. The performance of the proposed controller is verified by performing computer simulations with MATLAB.KeywordsTurbojet engineFuzzy PID controlUAVFuel flow controlSurge control