Boeing and NASA are conducting a joint study program to design a wing flap system that will provide mission-adaptive lift and drag performance for future transport aircraft having light-weight, flexible wings. This Variable Camber Continuous Trailing Edge Flap (VCCTEF) system offers a lighter-weight lift control system having two performance objectives: (1) an efficient high lift capability for take-off and landing, and (2) reduction in cruise drag through control of the twist shape of the flexible wing. This control system during cruise will command varying flap settings along the span of the wing in order to establish an optimum wing twist for the current gross weight and cruise flight condition, and continue to change the wing twist as the aircraft changes gross weight and cruise conditions for each mission segment. Design weight of the flap control system is being minimized through use of light-weight shape memory alloy (SMA) actuation augmented with electric actuators. The VCCTEF program is developing better lift and drag performance of flexible wing transports with the further benefits of lighter-weight actuation and less drag using the variable camber shape of the flap.
This paper presents a data fusion algorithm, using an Adaptive Extended Kalman filter (AFK) for estimation of velocity and position of a UAV. A LIDAR sensor provides local position updates using a SLAM technique, a GPS provides corrections when available and an Inertial Navigation System (INS) is used as an additional input to the Extended Kalman filter. We adapt the measurement noise covariance (R) of the AKF based on both the Global Positioning System (GPS) receiver error as well as on the LiDAR point cloud point-to-point match error. A simulation environment was developed to test the proposed SLAM as well as navigation (e.g., autopilot) algorithms in a virtual, but accurate environment. We show that by adapting the measurement noise covariance (R) of the AKF we improve both the accuracy and reliability of the position estimate, specially in areas with GPS signal drop outs such as urban canyon environments.
This paper addresses a system centric approach for design and analysis of airspace use in urban unmanned aerial vehicle (UAS) traffic flow control. The approach is based on numerical traffic simulations with a behavioral model of UASs for estimating characteristics of the future UAS air traffic in urban areas and performances of airspace structures. A concept on urban UAS traffic flow control is proposed with various airspace structural designs of different levels of freedom in flight, and a microscopic traffic model of UASs in one of the designs is developed. Fundamental diagrams of simple UAS traffic are obtained and performances of basic airspace structures are compared by using the traffic simulations.
For unmanned aerial systems (UAS) to be successfully deployed and integrated within the national airspace, it is imperative that they possess the capability to effectively complete their missions without compromising the safety of other aircraft, as well as persons and property on the ground. This necessity creates a natural requirement for UAS that can respondto uncertain environmental conditions and emergent failures in real-time, with robustness and resilience close enough to those of manned systems. We introduce a system that meets this requirement with the design of a real-time onboard system health management (SHM) capability to continuously monitor sensors, software, and hardware components. This system can detect and diagnose failures and violations of safety or performance rules during the flight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and software signals; (2) signal analysis, preprocessing, and advanced on-the-fly temporal and Bayesian probabilistic fault diagnosis; and (3) an unobtrusive, lightweight, read-only, low-power realization using Field Programmable Gate Arrays (FPGAs) that avoids overburdening limited computing resources or costly re-certification of flight software. We call this approach rt-R2U2, a name derived from its requirements. Our implementation provides a novel approach of combining modular building blocks, integrating responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. We demonstrate this approach using actual flight data from theNASA Swift UAS.
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