This paper focuses on the development of a new logistic approach based on reliability and maintenance assessment, with the final aim of establishing a more efficient interval for the maintenance activities for Unmanned Aerial Vehicles (UAV). In the first part, we develop an architectural philosophy to obtain a more detailed reliability evaluation; then, we study the intrinsic reliability at the design stage in order to avoid severe critical issues in the UAV. In the second part, we compare different maintenance philosophies for UAVs and develop the concepts of preventive and corrective maintenance that consider the system subjected (until real “hard failure”) to partial performance degradation (“soft failure”). Finally, by evaluation of the uncertainty through the confidence interval, we determine the new soft failure limits, taking into account the general knowledge of the systems and subsystems in order to guarantee the proper preventive maintenance interval.

This paper deals with the reliability assessment for an UAV (Unmanned Aerial Vehicle) system: once a specific architecture is selected (after a trade-off phase), the reliability assessment represents a key issue to optimize the design of any complex system. The final aim is to evaluate the intrinsic reliability at the design stage to avoid any critical failure that may leads to a catastrophic effect on the UAV.

This paper is a section of several preliminary studies of the Underwater Drones Group of the Università degli Studi “Roma Tre” Science Department: We describe the study philosophy, the theoretical technological considerations for sizing and the development of a technological demonstrator of a high accuracy buoyancy and depth control. We develop the main requirements and the boundary conditions that design the buoyancy system and develop the mathematical conditions that define the main parameters.

The advent of new sensors allows to create smaller and lighter inertial systems than ever before: thanks to their compact size, low cost and low power consumption, they can be installed on flying platforms such as UAV systems, to improve the reliability of autonomous navigation. The merging of the Inertial Reference System (IRS) and GNSS data through the Kalman filter gives a significantly improve to the autonomous navigation performance both in terms of accuracy and integrity. This paper is focused on the methods and techniques of GNSS integrated with an Inertial Reference System and their applications in UAV autonomous navigation systems.

Because of integrating measurements, the Inertial Navigation System (INS) for UAVs or small planes has the great drawback to increase the uncertainty for those entities, as acceleration and angular velocity that are function of time. For this reason, it generally employs GPS in order to correct and calibrate itself. When GPS is not available, typically in environments such as urban canyons or indoor navigation, is necessary to measure the altitude using stored map terrain profile and a LiDAR altimeter: weighting these data through a least square method, we can obtain the altitude of the vehicle non affected by local discontinuities of the ground.