Autonomy Operating System for UAVs: Pilot-in-a-Box

The Autonomy Operating System (AOS) is an open flight software platform with Artificial Intelligence for smart UAVs. It is built to be extendable with new apps, similar to smartphones, to enable an expanding set of missions and capabilities. AOS has as its foundations NASAs core flight executive and core flight software (cFEcFS). Pilot-in-a-Box (PIB) is an expanding collection of interacting AOS apps that provide the knowledge and intelligence onboard a UAV to safely and autonomously fly in the National Air Space, eventually without a remote human ground crew. Longer-term, the goal of PIB is to provide the capability for pilotless air vehicles such as air taxis that will be key for new transportation concepts such as mobility-on-demand. PIB provides the procedural knowledge, situational awareness, and anticipatory planning (thinking ahead of the plane) that comprises pilot competencies. These competencies together with a natural language interface will enable Pilot-in-a-Box to dialogue directly with Air Traffic Management from takeoff through landing. This paper describes the overall AOS architecture, Artificial Intelligence reasoning engines, Pilot-in-a-box competencies, and selected experimental flight tests to date

A Hierarchy of Monitoring Properties for Autonomous Systems

Monitoring capabilities play a central role in mitigating safety risks of current, but especially future autonomous aircraft systems. These future systems are likely to include complex components such as neural networks for environment perception, which pose a challenge for current verification approaches; they are considered as black-box components. To assure that these black-boxes comply to their specification, they are typically monitored to detect violations during execution in respect to their input and output behavior. Such behavioral properties often include more complex aspects such as temporal or spatial notions. Besides monitoring their behavior, the outputs can also be compared to data from other assured sensors or components of the aircraft, making monitoring an even more integral part of the system, which ideally has access to all available resources to assess the overall health of the operation. Current approaches using handwritten code for monitoring functions run the risk of not being able to keep up with these challenges. Therefore, in this paper, we present a hierarchy of monitoring properties that provides a perspective for overall health. We also present a categorization of monitoring properties and show how different monitoring specification languages can be used for formalization. These monitoring languages represent a higher abstraction of general-purpose code and are therefore more compact and easier for a user to write and read. They improve the maintainability of monitoring properties that is required to handle the increased complexity of future autonomous aircraft systems

Rotor wake vortex definition–evaluation of 3-C PIV results of the HART-II study

An evaluation is made of extensive three-component (3-C) particle image velocimetry (PIV) measurements within the wake across a rotor disk plane. The model is a 40 percent scale BO-105 helicopter main rotor in forward flight simulation. This study is part of the HART II test program conducted in the German-Dutch Wind Tunnel (DNW). Included are wake vortex field measurements over the advancing and retreating sides of the rotor operating at a typical descent landing condition important for impulsive blade-vortex interaction (BVI) noise. Also included are advancing side results for rotor angle variations from climb to steep descent. Using detailed PIV vector maps of the vortex fields, methods of extracting key vortex parameters are examined and a new method was developed and evaluated. An objective processing method, involving a centerof-vorticity criterion and a vorticity “disk” integration, was used to determine vortex core size, strength, core velocity distribution characteristics, and unsteadiness. These parameters are mapped over the rotor disk and offer unique physical insight for these parameters of importance for rotor noise and vibration prediction