Failures of the air data system due to exceptional weather conditions have shown to be among the leading causes of aviation accidents. In this respect, most of aircraft Pitot tube failure detection schemes rely on mathematical models and simplified assumptions on the uncertain parameters and disturbances. These methods typically require "ad hoc" time-consuming tuning procedures that may produce unreliable performance when validated with actual flight data. In this paper, a complete semiautomated data-driven approach is introduced to select the model regressors, to identify NARX input-output prediction models, to set up robust fault detection filters and to compute fault detection thresholds. To cope with time-dependent and flight-dependent levels of uncertainties online model adaption mechanisms are introduced to limit the critical problem of minimizing the false alarm rate. Extensive validation tests have been conducted using actual flight data of a P92 Tecnam aircraft through the introduction of artificially injected hard and soft failure of the Pitot tube sensor. The approach showed to be remarkably robust in terms of false alarms while maintaining fault detectability to faults of amplitudes less than 1 m/s.

Among non-destructive inspection (NDI) techniques, General Visual Inspection (GVI), global or zonal, is the most widely used, being quick and relatively less expensive. In the aeronautic industry, GVI is a basic procedure for monitoring aircraft performance and ensuring safety and serviceability, and over 80% of the inspections on large transport category aircrafts are based on visual testing, both directly and remotely, either unaided or aided via mirrors, lenses, endoscopes or optic fiber devices coupled to cameras. This paper develops the idea of a global and/or zonal GVI procedure implemented by means of an autonomous unmanned aircraft system (UAS), equipped with a low-cost, high-definition (HD) camera for carrying out damage detection of panels, and a series of distance and trajectory sensors for obstacle avoidance and inspection path planning. An ultrasonic distance keeper system (UDKS), useful to guarantee a fixed distance between the UAS and the aircraft, was developed, and several ultrasonic sensors (HC-SR-04) together with an HD camera and a microcontroller were installed on the selected platform, a small commercial quad-rotor (micro-UAV). The overall system concept design and some laboratory experimental tests are presented to show the effectiveness of entrusting aircraft inspection procedures to a small UAS and a PC-based ground station for data collection and processing.

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.

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.