Design of all electric secondary power system for future advanced MALE UAV

SAvE (Systems for UAV Alternative Energy) is a research project funded in 2007 by Piemonte Regional Government, Italy, and assigned to Politecnico di Torino and Alenia Aeronautica. Aim of the project is the study of new, more efficient, more effective and more environmentally friendly on board systems for future advanced Unmanned Aerial Vehicles (UAV), particularly for future advanced MALE UAVs. The paper deals with the analysis and design of the all electric Secondary Power System of a future advanced MALE UAV, that we consider as "reference aircraft". After a thorough trade-off analysis of different configurations of the Secondary Power System, the hybrid configuration, characterized by generators (or better, starter/generators), fuel cells and traditional and innovative batteries, has been selected as the most promising. Detailed investigations to find the best way to apportion the supply of secondary power, considering the various power sources (generators or starter/generators, batteries and fuel cells) in the different modes of operations, have been performed thanks to an integrated simulation environment, where physical, functional and mission scenario simulations continuously exchange data and results

Analysing temporal performance profiles of UAV operators using time series clustering

The continuing growth in the use of Unmanned Aerial Vehicles (UAVs) is causing an important social step forward in the performance of many sensitive tasks, reducing both human and economical risks. The work of UAV operators is a key aspect to guarantee the success of this kind of tasks, and thus UAV operations are studied in many research fields, ranging from human factors to data analysis and machine learning. The present work aims to describe the behaviour of operators over time using a profile-based model where the evolution of the operator performance during a mission is the main unit of measure. In order to compare how different operators act throughout a mission, we describe a methodology based of multivariate-time series clustering to define and analyse a set of representative temporal performance profiles. The proposed methodology is applied in a multi-UAV simulation environment with inexperienced operators, obtaining a fair description of the temporal behavioural patterns followed during the course of the simulation

A study on performance metrics and clustering methods for analyzing behavior in UAV operations

Unmanned Aerial Vehicles (UAVs) are starting to provide new possibilities to human societies and their demand is growing according to the new industrial application fields for these revolutionary tools. The current systems are still evolving, specially from an Artificial Intelligence perspective, which is increasing the different tasks that UAVs can perform. However, the current state still requires a strong human supervision. As a consequence, a good preparation for UAV operators is mandatory due to some of their applications might affect human safety. During the training process, it is important to measure the performance of these operators according to different factors that can help to decide what operators are more suitable for different kinds of missions creating operator profiles. Having this goal in mind, this work aims to present an extensive and robust methodology to automatically extract different performance profiles from the training process of operators in an UAV simulation environment. Our method combines the definition of a set of performance metrics with clustering techniques to define operators profiles, ensuring that the behavior discrimination is suitable and consistent

Analysing temporal performance profiles of UAV operators using time series clustering

The continuing growth in the use of Unmanned Aerial Vehicles (UAVs) is causing an important social step forward in the performance of many sensitive tasks, reducing both human and economical risks. The work of UAV operators is a key aspect to guarantee the success of this kind of tasks, and thus UAV operations are studied in many research fields, ranging from human factors to data analysis and machine learning. The present work aims to describe the behaviour of operators over time using a profile-based model where the evolution of the operator performance during a mission is the main unit of measure. In order to compare how different operators act throughout a mission, we describe a methodology based of multivariate-time series clustering to define and analyse a set of representative temporal performance profiles. The proposed methodology is applied in a multi-UAV simulation environment with inexperienced operators, obtaining a fair description of the temporal behavioural patterns followed during the course of the simulation

Skyport airframe: design and manufacturing

Many rural areas of developing countries lack the necessary transportation infrastructure to have reliable access to basic needs. This is particularly true for medical supplies. To combat the issue of insufficient access to vaccines in developing areas, the SkyPort project has developed the SkyPort UAV (Unmanned Aerial Vehicle). The SkyPort UAV has the vertical takeoff and landing (VTOL) capabilities of a quadcopter, as well as the efficient, sustained flight of a fixed-wing aircraft. It provides a cheaper, quicker, and safer delivery method than existing alternatives for vaccines in areas that lack a reliable transportation infrastructure. The role of the SkyPort Airframe Design Team was to design and build the primary support structure of the UAV, which will house the payload, controls, and propulsion systems being designed by the other two SkyPort teams. The airframe consists of a lightweight and durable fuselage, wing, tail, and framing subsystems and it is designed to be modular so that parts are easy to replace and require minimal maintenance. Primary materials used in construction were foam, carbon fiber, and aluminum. Testing of the frame yielded a weight of 8.63 kg, minimum foam strength of 1.70 MPa, and a minimum factor of safety of 16 for the structural members of the frame. Although the weight of the airframe is higher than the desired weight, this was necessary in order to satisfy the strength requirements and protect sensitive electrical components during initial flight tests. In the future, this extra weight could be decreased by using less carbon fiber, lower density foam, smaller, lighter material for the structural members, or smaller fasteners

Architecture and Information Requirements to Assess and Predict Flight Safety Risks During Highly Autonomous Urban Flight Operations

As aviation adopts new and increasingly complex operational paradigms, vehicle types, and technologies to broaden airspace capability and efficiency, maintaining a safe system will require recognition and timely mitigation of new safety issues as they emerge and before significant consequences occur. A shift toward a more predictive risk mitigation capability becomes critical to meet this challenge. In-time safety assurance comprises monitoring, assessment, and mitigation functions that proactively reduce risk in complex operational environments where the interplay of hazards may not be known (and therefore not accounted for) during design. These functions can also help to understand and predict emergent effects caused by the increased use of automation or autonomous functions that may exhibit unexpected non-deterministic behaviors. The envisioned monitoring and assessment functions can look for precursors, anomalies, and trends (PATs) by applying model-based and data-driven methods. Outputs would then drive downstream mitigation(s) if needed to reduce risk. These mitigations may be accomplished using traditional design revision processes or via operational (and sometimes automated) mechanisms. The latter refers to the in-time aspect of the system concept. This report comprises architecture and information requirements and considerations toward enabling such a capability within the domain of low altitude highly autonomous urban flight operations. This domain may span, for example, public-use surveillance missions flown by small unmanned aircraft (e.g., infrastructure inspection, facility management, emergency response, law enforcement, and/or security) to transportation missions flown by larger aircraft that may carry passengers or deliver products. Caveat: Any stated requirements in this report should be considered initial requirements that are intended to drive research and development (R&D). These initial requirements are likely to evolve based on R&D findings, refinement of operational concepts, industry advances, and new industry or regulatory policies or standards related to safety assurance