Unmanned aircraft, commonly referred to as drones, represent a valuable alternative for various operational tasks due to their versatility, flexibility, cost-effectiveness, and reusability. These features make them particularly advantageous in environments that are hazardous or inaccessible to humans. Recent developments have highlighted a significant increase in the use of unmanned aircraft within metropolitan areas. This growth has necessitated the implementation of new regulations and guidelines to ensure the safe integration of UAS into urban environments. Consequently, the concept of UAM has emerged. UAM refers to an innovative air transportation paradigm designed for both passengers and cargo within urban settings, leveraging the capabilities of drones. This review manuscript explores the latest advancements for UAS, focusing on updated regulations, definitions, enabling technologies, and airspace classifications relevant to UAM operations. Additionally, it provides a comprehensive overview of unmanned aircraft systems, including their classifications, key features, and primary applications.

Multiconstellation satellite navigation is critical in signal-degraded environments where signals are strongly corrupted. In this case, the use of a single GNSS system does not guarantee an accurate and continuous positioning. A possible approach to solve this problem is the use of multiconstellation receivers that provide additional measurements and allows robust reliability testing; in this work, a GPS/GLONASS combination is considered. In urban scenario, a modification of the classical RAIM technique is necessary taking into account frequent multiple blunders. The FDE schemes analysed are the “Observation Subset Testing,” “Forward-Backward Method,” and “Danish Method”; they are obtained by combining different basic statistical tests. The considered FDE methods are modified to optimize their behaviour in urban scenario. Specifically a preliminary check is implemented to screen out bad geometries. Moreover, a large blunder could cause multiple test failures; hence, a separability index is implemented to avoid the incorrect exclusion of blunder-free measurements. Testing the RAIM algorithms of GPS/GLONASS combination to verify the benefits relative to GPS only case is a main target of this work too. The performance of these methods is compared in terms of RMS and maximum error for the horizontal and vertical components of position and velocity.

In this paper a procedure that creates a three dimensional (3D) virtual map useful for Safe Landing of an UAV Quadcopter [3], through a Ultrasonic Sensor Model is presented. Reflective sound waves produced from sonar sensor were analyzed and managed to generate a constructed plane where the UAV landing is possible and safe. The low-cost sensor gives relatively accurate range readings if there are disregarding angular uncertainty and specular reflections. Application works for an horizontal plane with and without obstacles inside it. Ultrasonic sensor detects obstacles in the landing field and if there are obstacles higher than UAV landing legs, the landing procedure is aborted. A series of ultrasonic sensors installed on UAV are used for low-altitude mapping on an horizontal plane. Experiments were ducted on a model at 150 cm from the landing field.

In recent years the use of Unmanned Aerial Vehicles (UAVs) has considerably grown in the civil sectors, due to their high flexibility of use. Currently, two important key points are making them more and more successful in the civil field, namely the decrease of production costs and the increase in navigation accuracy. In this paper, we propose a Kalman filtering-based sensor fusion algorithm, using a low cost navigation platform that contains an inertial measurement unit (IMU), five ultrasonic ranging sensors and an optical flow camera. The aim is to improve navigation in indoor or GPS-denied environments. A multi-rate version of the Extended Kalman Filter is considered to deal with the use of heterogeneous sensors with different sampling rates, and the presence of non-linearities in the model. The effectiveness of the proposed sensor platform is evaluated by means of numerical tests on the dynamic flight simulator of a quadrotor. Results show high precision and robustness of the attitude estimation algorithm, with a reduced computational cost, being ready to be implemented on low-cost platforms.

Unmanned Aerial Vehicles (UAV) with on-board augmentation systems (UAS, Unmanned Aircraft System) have penetrated into civil and general-purpose applications, due to advances in battery technology, control components, avionics and rapidly falling prices. This paper describes the conceptual design and the validation campaigns performed for an embedded precision Positioning, field mapping, Obstacle Detection and Avoiding (PODA) platform, which uses commercial-off-the-shelf sensors, i.e., a 10-Degrees-of-Freedom Inertial Measurement Unit (10-DoF IMU) and a Light Detection and Ranging (LiDAR), managed by an Arduino Mega 2560 microcontroller with Wi-Fi capabilities. The PODA system, designed and tested for a commercial small quadcopter (Parrot Drones SAS Ar.Drone 2.0, Paris, France), estimates position, attitude and distance of the rotorcraft from an obstacle or a landing area, sending data to a PC-based ground station. The main design issues are presented, such as the necessary corrections of the IMU data (i.e., biases and measurement noise), and Kalman filtering techniques for attitude estimation, data fusion and position estimation from accelerometer data. The real-time multiple-sensor optimal state estimation algorithm, developed for the PODA platform and implemented on the Arduino, has been tested in typical aerospace application scenarios, such as General Visual Inspection (GVI), automatic landing and obstacle detection. Experimental results and simulations of various missions show the effectiveness of the approach.

Basalt fibers are receiving increasing consideration because they seem to be adequate as reinforcement of composites and to comply with the environmental safeguard rules. However, many factors affect the performance of composite material, demanding specific testing; one may be performance assessment under impact tests. The attention of the present work is focused on the detection of impact damage in basalt-based composites with two non-destructive testing techniques: lock-in thermography (LT) and ultrasonic testing (UT). Two different types of materials are considered which both include basalt fibers as reinforcement but two different matrices: Polyamide and polypropylene. Polypropylene is used either pure or modified with the addition of a coupling agent; the latter improves the fiber/matrix interface strength, giving in practice, a material of different characteristics. Specimens are first subjected to low-velocity impact tests and then non-destructively examined with the two above mentioned techniques. The obtained results are analyzed and compared to highlight the advantages and limitations of the two techniques to detect impact damage in basalt-based composites. Both techniques seem effective for the inspection of polyamide/basalt composite; in particular, there is a general agreement between results. Conversely, UT seems not suitable for the inspection of polypropylene/basalt composites because of their superficial porosity, while lock-in thermography is effective also for this type of composite material.