Search

Due to the increase in air traffic, additional systems with high reliability and highperformance must be adopted in order to keep the safety requirements. On the other hand, they must be compliant with the specifications in terms of light-weight, small size, and low power-consumption for the small aerial vehicles. The paper presents an advanced integrated solution for increasing the performance of the traditional Air Data System adopting an Inertial Measurement Unit based on Micro Electro-Mechanical System sensors, i.e., accelerometer and gyroscope. The aim is to identify the Air Data System failure, thus overcoming the issues of adopting additional technology that increase the size, weight, and power consumption of the systems and make. This way, the considered solution is tailored for small manned and unmanned vehicles applications. To this aim, the authors propose an

This paper presents a fully autonomous multisensor anti-collision system for Unmanned Aerial Vehicles (UAVs). This system is being developed by the Italian Aerospace Research Center (CIRA) in collaboration with the Dept. of Aerospace Engineering of the University of Naples “Federico II”, within a research project named TECVOL, funded in the frame of the National Aerospace Research Program (PRO.R.A.) on UAV. The system prototype will be initially installed onboard a manned laboratory aircraft equipped for automatic control so that flight tests will verify the adequacy of attained performances for supporting fully autonomous flight. In order to perform the obstacle detection and identification function, a multisensor configuration has been designed in the TECVOL preliminary studies. The hardware configuration is made up by a pulsed Ka-band radar, two visible (panchromatic and colour) videocameras, two infrared (IR) videocameras, and two computers, one dedicated to sensor fusion and communication with the flight control computer and with the radar, the other devoted to image processing. They are connected to the Flight Control Computer by means of a deterministic data bus. On the basis of these tracking estimates and of a Collision Avoidance Software, the GNC computer generates and follows in real time a proper escape trajectory. In order to evaluate the performance of the entire collision avoidance system, numerical simulations have been performed taking into account the DS&A sensors’ accuracy, UAV’s and intruder’s flight dynamics, navigation system accuracy and latencies, collision avoidance logic, and practical real-time implementation issues. The relevant results helped to assess overall system performances. They are discussed in depth at the end of the paper.

This paper presents the idea of a hybrid bistatic synthetic aperture radar (SAR) formed by a constellation of spaceborne illuminators and receiving-only airborne forward-looking SARs. The study aims at identifying and developing new imaging techniques for avoidance, landing, and observation, able to guarantee forward-looking real-time, all-weather, day and night, high geometric and radiometric resolution images for Unmanned Airborne Systems (UAS). Nowadays, UAS Navigation is mainly performed with satellite navigation systems (e.g. GPS, Galileo) which offer accurate and reliable navigation data, but no information about the surrounding environment. A vision-based navigation system can greatly improve UAS autonomy with an additional beneficial impact on obstacle avoidance capability. SAR systems have been already experienced on-board aircrafts of different classes. However, such sensors have been only used as remote sensing payloads in side-looking geometry. For navigation purposes a forward looking geometry is preferable, but it has been rarely experienced due to major limitations (left/right ambiguity and poor Doppler resolution) which can be only partially mitigated at the cost of strong complexities. These limitations can be overcome by the proposed space airborne bistatic system provided that specific requirements on the acquisition geometry are satisfied. In addition, the airborne receiver can be much more compact and lightweight, with a reduced power request thus complying with typical UAS requirements. Preliminary feasibility assessment and definition of strawman system concept have been conducted under ESA contract (22499/09/F/MOS) considering UAS vision-based navigation as main application even if further uses, e.g. remote sensing or surveillance, have been envisaged.

Unmanned Aerial Vehicles will have a safe access to the Civil Airspace only when they will be able to avoid collisions even with non cooperative flying obstacles. Thus, they need to replace the capability of human eye to detect potential mid-air collisions with other airframes and the pilot expertise to find an adequate voidance trajectory. This paper deals with sensors and logics equired on-board to achieve the necessary situational awareness. n particular, it refers to the research activities carried out by the Department of Aerospace Engineering of the university of Naples “Federico II” in collaboration with the Italian Center for Aerospace Research in the framework of TECVOL project. An integrated multi-sensor system is described which is comprised of a Ka-band radar, two visible and two thermal infrared cameras, and two processing units. Ground hardware-in-the-loop tests and images from first preliminary flights confirm the system potential which was estimated in past studies by off-line simulations.

Airborne Obstacle Tracking has a significant role onboard Unmanned Aerial Systems. The primary function of this system is to detect and track location of obstacles in the flight path in an accurate and timely manner. Advanced filtering methodologies, such as Particle Filters, can provide very accurate estimates of the target state when the aircraft trajectories are described by non-linear dynamic models and linear filters could cause a loss of accuracy. The paper focuses on algorithm and test results from an Obstacle Tracking system based on Particle Filter. A customized version of the Particle Filter has been developed by exploiting data acquired during flight tests by means of a Very Light Aircraft in the framework of a research project carried out by the Italian Aerospace Research Centre and the University of Naples “Federico II”. First of all, a brief description of the hardware/software architecture installed onboard the aircraft is presented. Then, the Particle Filter performance is analyzed in order to point out the impact of non-linear filters on the estimate of the Distance at Closest Point of Approach

Echinococcosis hydatidosis is a chronic disabling disease of parasitic origin that constitutes an exemplary case of one-health. According to data from the national epidemiological bulletin, is one of the most important zoonoses in terms of the number of cases, severity, and economic costs in Italy. This paper aims to contribute to the prevention of the spread of this infection by proposing the development of an embedded system on a drone that can release decoys containing canine medicine. To this aim has been chosen a microcontroller, which is responsible for interfacing and controlling the different peripherals required to accomplish the release of decoys from the custom payload. In addition to the hardware part of the embedded system, has been implemented an ad hoc program that is able to coordinate all the elements, satisfying the proposed purpose. In particular, suitable software architecture has been implemented by means of an ARM-Cortex microcontroller not only for the advantages related to size, cost, and power consumption but also to exploit their high-performance and safety requirements.

The growing interest in Unmanned Aerial Systems has led, especially in recent years, to the need for a precise structure of rules and procedures aimed to guarantee safe traffic management. As an important type of dynamic data-driven application system, Unmanned Aerial Systems are widely used for civilian, commercial, and military applications across the globe and require a high level of autonomy. Indeed, the absence of the pilot onboard leads to stricter requirements regarding the level of accuracy for the Unmanned Systems, with the need to provide autonomous services such as Conflict Detection and Resolution during the strategic and tactical phases, avoiding possible collisions. In order to explore efficient and robust solutions for autonomous Collision Avoidance, an increasing research effort has been devoted to Trajectory Prediction for non-cooperative UASs. In this work, an analysis of existent solutions for Trajectory Prediction has been evaluated, with particular emphasis on methods based on Machine Learning (ML). Besides, four different Machine Learning methods based on Regression (Linear Regression, Regression Tree, Gaussian Process Regression, and Support Vector Machine for Regression) have been implemented for the prediction of the flight times of a single drone over planned paths. The four regression methods are used as benchmark for a proposed Deep Learning based approach, and the results have been compared.

In recent years, the overall performances of inertial Micro-Electro Mechanical Sensors (MEMSs) exhibited substantial improvements to values very close or similar to so-called tactical-grade sensors. However, due to their high costs, numerous researchers are currently focusing on the performance enhancement of cheap consumer-grade MEMS inertial sensors for all those applications (as an example, small unmanned aerial vehicles, UAVs), where cost effectiveness is a relevant request; the use of redundancy proves to be a feasible method for this purpose. In this regard, the authors propose, hereinafter, a suitable strategy aimed at fusing raw measurements provided by multiple inertial sensors mounted on a 3D-printed structure. In particular, accelerations and angular rates measured by the sensors are averaged according to weights associated with the results of an Allan variance approach; the lower the noise figure of the sensors, the greater their weight on the final averaged values. On the other hand, possible effects on the measurements due to the use of a 3D structure in reinforced ONYX (a material capable of providing better mechanical specifications for avionic applications with respect to other solutions for additive manufacturing) were evaluated. The performance of a prototype implementing the considered strategy is compared with that of a tactical-grade inertial measurement unit in stationary conditions, exhibiting differences as low as 0.3 degrees in heading measurements. Moreover, the reinforced ONYX structure does not significantly affect the measured values in terms of both thermal and magnetic field while assuring better mechanical characteristics with respect to other 3D printing materials, thanks to a tensile strength of about 250 MPa and a specific stacking sequence of continuous fibers. Finally, a test conducted on an actual UAV highlights performance very close to that of a reference unit, with root-mean-square error in heading measurements as low as 0.3 degrees in observation intervals up to 140 s.

This paper describes an adaptive method to predict the battery discharge of a multirotor drone over a generic path. A proper assessment of battery state of discharge trend is critical to ensure a safe operation of battery-powered aerial vehicles in critical environments, such as the urban ones. Several standard paths were executed by a commercial, battery-powered drone to acquire the data needed to train a Deep Learning based method; telemetry files and ground-collected data were processed to train the proposed method according to a trajectory segmentation strategy. Two learning configurations were trained to predict the time-of-flight and the integral of the battery current needed to fly the standard path segments. The current integral for each standard path segment is exploited to estimate the corresponding reduction of the battery state of charge. Based on path segmentation into predefined standard sections, the presented solution allows to predict time of flight and battery consumption along any generic path. This can be exploited to plan a complete path at strategic stage, as well as to estimate the remaining available power resource at any intermediate point along a generic trajectory both at strategic stage and during mission execution to be exploited by the drone operator or by a traffic management service. To validate the technique, a scaled package delivery mission is presented as an example of contingency management application. The maximum distance that the drone can reach from selected points along the mission path was computed according to the remaining battery level. Considering a random distribution of safe-landing areas around the planned path, the computed maximum distance was used to determine which safe-landing areas can be reached by the drone from a generic point of the path after an unexpected event.

Systems for accurate attitude and position monitoring of large structures, such as bridges, tunnels, and offshore platforms are changing in recent years thanks to the exploitation of sensors based on Micro-ElectroMechanical Systems (MEMS) as an Inertial Measurement Unit (IMU). Currently adopted solutions are, in fact, mainly based on fiber optic sensors (characterized by high performance in attitude estimation to the detriment of relevant costs large volumes and heavy weights) and integrated with a Global Position System (GPS) capable of providing low-frequency or single-update information about the position. To provide a cost-effective alternative and overcome the limitations in terms of dimensions and position update frequency, a suitable solution and a corresponding prototype, exhibiting performance very close to those of the traditional solutions, are presented and described hereinafter. The solution leverages a real-time Kalman filter that, along with the proper features of the MEMS inertial sensor and Real-Time Kinematic (RTK) GPS, allows achieving performance in terms of attitude and position estimates suitable for this kind of application. The results obtained in a number of tests underline the promising reliability and effectiveness of the solution in estimating the attitude and position of large structures. In particular, several tests carried out in the laboratory highlighted high system stability; standard deviations of attitude estimates as low as 0.04° were, in fact, experienced in tests conducted in static conditions. Moreover, the prototype performance was also compared with a fiber optic sensor in tests emulating actual operating conditions; differences in the order of a few hundredths of a degree were found in the attitude measurements.

The paper describes the development of a drone swarm configuration that can electromagnetically obscure a target-drone from radar detection. At the beginning, different drone swarm architectures were analyzed in order to select the best solution to develop ground and in-flight tests. During the tests execution, a compact software radar was used to detect a target-drone. Moreover, trihedral corner reflectors were used to perform the electromagnetic obscuration of the target-drone. Several ground tests were executed to characterize the radar echo response of the target-drone installed on a wooden tripod. Then, the radar echo response of the target-drone was compared with the one obtained by the target-drone obscured by corner reflectors. Different configurations of corner reflectors were tested on wooden tripods to select the swarm configuration that better obscures the target-drone. At the end, flight tests were executed involving decoy-drones equipped with a corner reflector and a target-drone to assess and validate the developed drone swarm as countermeasure against ground-based radar detection.

It is well known that the initial field of use of Unmanned Aircraft Systems (UAS, commonly known as drones) was the military one, but nowadays they are used in a huge number of civilian tasks such as agriculture, cinematography, and on demand transport of goods and services and many other applications are in development. Particularly, the healthcare area is a prime concern, and during the Covid-19 pandemic the considerable potential of drones was highlighted both in terms of contrasting and overcoming the emergency. Indeed, their applications included monitoring for prevention, sanitization, and transfer (deliveries / collections) of material without approaching between operators and users of the service. This paper presents the service for equipment delivery by drone remotely operated, performed using drones for the delivery or picking up of small and medium-sized objects to/from the home of people subjected to quarantine. The users of these services can include all those who are affected by pathologies similar to the pandemic disease Covid-19 and who are forced into quarantine situations, finding themselves in an asymptomatic or symptomatic state.

This paper presents a method developed to predict the flight-time employed by a drone to complete a planned path adopting a machine-learning-based approach. A generic path is cut in properly designed corner-shaped standard sub-paths and the flight-time needed to travel along a standard sub-path is predicted employing a properly trained neural network. The final flight-time over the complete path is computed summing the partial results related to the standard sub-paths. Real drone flight-tests were performed in order to realize an adequate database needed to train the adopted neural network as a classifier, employing the Bayesian regularization backpropagation algorithm as training function. For the network, the relative angle between two sides of a corner and the wind condition are the inputs, while the flight-time over the corner is the output parameter. Then, generic paths were designed and performed to test the method. The total flight-time as resulting from the drone telemetry was compared with the flight-time predicted by the developed method based on machine learning techniques. At the end of the paper, the proposed method was demonstrated as effective in predicting possible collisions among drones flying intersecting paths, as a possible application to support the development of unmanned traffic management procedures.

This paper describes a configuration of drone swarm that can be used in support of the actions to limit the virus spread during a pandemic period, such as the COVID-19 emergency. The proposed study analyzes a system architecture for the identification of individuals affected by the virus, estimating their biomedical parameters. The presented method exploits different techniques, such as stereoscopy vision, thermal measures and remote photoplethysmography, to acquire standalone data that can be compared to evaluate the target risk. The tested solutions are proposed to measure the social distancing among multiple individuals, the skin temperature of a target person, and the image photoplethysmography signal, that are critical parameters to detect a potentially infect individual during the COVID-19 pandemic. Different test strategies were adopted to carry out the mentioned tasks. To measure the distance between target individuals, two drones equipped with visible band cameras were employed. To measure the skin temperature of a target, a drone equipped with a thermal camera was adopted, performing measures at different distances and heights. To obtain the image photoplethysmography signal, a video file from drone camera is processed. Image processing techniques are required to elaborate the data coming from several images and videos acquired by drones. Comparing the measures, altered biomedical parameters of several targets can be detected and later tested with medical equipment.

This paper describes the study conducted to predict the trajectory flight-time of a drone adopting a machine learning approach. The proposed method has been carried out developing a feedforward neural network to estimate the flight-time needed by the drone to perform a selected corner of a designed path. To acquire a consistent database for the neural network training several reference corner paths have been flown by a test drone. The reference corners have fixed side length and different turning angle. Neural network input parameters are the corner angle, relative orientation and intensity of wind. From the telemetry analysis the flight-time to fly the corner path has been computed and employed to train the neural network. The Levenberg-Marquardt algorithm and the Bayesian Regularization backpropagation algorithm have been exploited as training functions, analyzing several neural network architectures with a different number of hidden layers and neurons. At the end, the neural networks that are characterized by the best training and test performance have been selected for each training function. Stating the trained network, a generic path has been planned to test the proposed method. The error between the estimated flight-time and the real flight-time from the drone telemetry has been evaluated.

The paper presents an integrated inertial measurement unit based on an innovative redundant configuration of low-cost Micro-Electro-Mechanical Sensors for small unmanned aircraft systems. The prototype developed consists of six different IMU in a cubic configuration with triaxial gyroscopes, accelerometers, and magnetometers. The redundancy allows improving the performance of the system because of the mutual calibration of single sensors. The performance of the prototype are preliminarily assess through short flight sessions. The attitude measures obtained from multiple-IMU turns out to be compliant with requirements for drone navigation.

In this paper, the application of Artificial Neural Networks to perform the thermal calibration of bias for Micro Electro-Mechanical gyros that are installed in Inertial Measurement Units is discussed. In recent years, the interest in using these systems to perform integrated inertial navigation has increased. Several new applications, related to the use of autonomous systems and personal navigation systems in GPS-challenging environments, have been developed. Thermal calibration of bias is a key issue to be assessed to achieve the best performance of a Micro Electro-Mechanical gyro. It can reduce sensor bias to one order of magnitude lower than non-calibrated conditions. Usually, thermal calibration is performed by exploiting polynomial fitting, i.e. finding the least-square polynomial that fits experimental data collected during laboratory tests in a climatic chamber. Polynomials have some drawbacks when they are applied to Micro Electro-Mechanical gyro calibration. They are not adequate to model abrupt change of bias trend in small temperature intervals and sensor hysteresis. For this reason, in the present paper, the use of Back Propagation Artificial Neural Networks is suggested as an improvement of polynomial fitting. Indeed, Neural Networks have intrinsic adaptive configurations and standard training and testing techniques, so that they can be adequately adopted for mapping thermal bias variations. In this paper, the polynomial fitting and Neural Network compensation algorithms are compared on selected testing points where the two techniques have the largest difference. Results highlight that the proposed method has better performance on these points. Therefore, the time in which the flight attitude accuracy meets the requirements imposed by the current regulations is improved by 20%.

The safe integration of Unmanned Aircraft Systems in the civil airspace requires the development of Sense and Avoid (also named Detect and Avoid) systems, which represent a technology needed to detect a threat (e.g., an intruder vehicle) and, consequently, command a maneuver suitable to avoid collision within a small amount of time. In this framework, this paper presents a quantitative assessment of conflict detection performance achievable by different sensing architectures, which can be designed and installed on board of small Unmanned Aerial Vehicles. The attention is focused on non-cooperative systems, which can rely on active sensors (such as radars and LIDARs), passive sensors (such as visual cameras) or multi-sensor-based solutions. The aim is to quantitatively identify the potentialities and limitations of each technological solution. The analysis is carried out by means of a numerical simulation environment able to reproduce horizontal collision scenarios. Specifically, conflict detection performance is expressed in terms of two probabilities, namely the probability of missed conflict detection and false conflict detection, which are evaluated as a function of three parameters which allow uniquely defining the collision scenario, i.e., the time to and distance at the closest point of approach and the azimuth of the intruder with respect to the velocity vector of the ownship. Since, the probability determination process includes the effect of tracking uncertainties (i.e., probability of firm tracking and false track generation), this analysis also allows highlighting the diverse but interconnected effects of sensing accuracy and detection range.

This paper presents an innovative integrated navigation unit developed for small unmanned aircraft systems, i.e. drones. It exploits an original redundant configuration of "consumer grade" Micro Electro-Mechanical Sensors to generate measurements that are compliant with specifications for drone navigation, both in terms of accuracy and integrity. To this aim, six boards equipped with inertial sensors are arranged in a cube containing triaxial gyros, accelerometers and magnetometers. This way, single sensor biases are preliminarily compensated by mutual calibration thanks to an average operation for quantities of each axis. To assess the performance of the proposed solution, the paper presents and discusses also the preliminary results obtained during an actual navigation test session.