In the last decades, the growing use of low-cost electronics has pushed Unmanned Aerial Vehicles (UAVs) to general purpose audience in the civil sector. An accurate attitude and position estimation is crucial for a precise attitude and altitude control of this type of aircraft and represents a fundamental aspect to be taken into account. The main aim of this paper is to propose a Moving Horizon Estimator (MHE) able to fuse raw data coming from a set of sensors composed of an Inertial Measurement Unit (IMU), an optimal flow camera and several Time of Flight (ToF) distance sensors, oriented downward, with the scope of improving estimation performance in indoor or GPS-denied environment. The use of MHE allows to take into account constraints on state dynamics and noise features. To test the effectiveness of the proposed MHE algorithm, numerical simulations were conducted, evaluating the performance of the proposed algorithm in the presence of attitude maneuvers.

During recent decades, Unmanned Aerial Vehicles (UAVs) has increased their success in several areas of applications thanks to their versatility. Furthermore, the growing availability of low-cost electronics has pushed their use in civil consumer applications. Attitude control is one of the needed tasks to effectively carry out missions, whose accuracy and adaptability to high payload imbalances or atmospheric disturbances are fundamental requirements. This paper focuses on the design of a flight control scheme based on Model Reference Adaptive Control with Neural Networks. The effectiveness of the proposed controller is assessed through experimental tests carried out on a Crazyflie 2.1 quadrotor.

Unmanned Aerial Vehicles (UAVs) have experienced considerable expansion in civilian applications thanks to their operational simplicity and versatility. This document introduces a distributed navigation strategy tailored for UAV formations in conducting post-avalanche search-and-rescue operations. The deployment of UAV formations is identified as a interesting approach with respect to individual UAV operations in environments characterized by dynamism and complexity. This configuration simplifies the allocation of several and different sensors across the formation, reducing the payload on single vehicles, enhancing robustness, and improving overall operational efficiency. The proposed navigation algorithm involves a consensus-based Kalman filter for distributed state estimation. The efficacy of this method was validated through its application in a realistic scenario, demonstrating the capability to identify several victims and preserve situational awareness while circumventing areas yet to be searched. This approach is presented as a viable substitute for search-and-rescue missions that traditionally require significant human involvement.

In the last decades, the growing use of small Unmanned Aerial Vehicles (UAVs) has resulted in an escalating demand for low-cost Inertial Measurement Units (IMUs), usually made of Micro Electro-Mechanical Systems (MEMS) to measure accelerations, angular velocities and, optionally, magnetic field components along three axes. An accurate calibration of these devices is needed for the precise determination of aircraft attitude. Although their versatile applications, MEMS-based IMUs exhibit a high level of noise, including both systematic and stochastic errors. Systematic errors need an appropriate calibration process to be identified and eliminated. This paper presents a portable low-cost IMU calibration platform to provide the parameters to mitigate the errors that characterize this kind of devices. Using three servomotors positioned to enable rotations around three orthogonal axes, the system allows for calibrating IMUs both statically and dynamically. To prove the effectiveness of the proposed platform, a performance evaluation is provided, showcasing the difference in estimating the attitude of an IMU, calibrated with and without the use of the platform.