The Commercial Aviation Safety Team found the majority of recent international commercial aviation accidents attributable to loss of control inflight involved flight crew loss of airplane state awareness (ASA), and distraction was involved in all of them. Research on attention-related human performance limiting states (AHPLS) such as channelized attention, diverted attention, startle/surprise, and confirmation bias, has been recommended in a Safety Enhancement (SE) entitled "Training for Attention Management." To accomplish the detection of such cognitive and psychophysiological states, a broad suite of sensors was implemented to simultaneously measure their physiological markers during a high fidelity flight simulation human subject study. Twenty-four pilot participants were asked to wear the sensors while they performed benchmark tasks and motion-based flight scenarios designed to induce AHPLS. Pattern classification was employed to predict the occurrence of AHPLS during flight simulation also designed to induce those states. Classifier training data were collected during performance of the benchmark tasks. Multimodal classification was performed, using pre-processed electroencephalography, galvanic skin response, electrocardiogram, and respiration signals as input features. A combination of one, some or all modalities were used. Extreme gradient boosting, random forest and two support vector machine classifiers were implemented. The best accuracy for each modality-classifier combination is reported. Results using a select set of features and using the full set of available features are presented. Further, results are presented for training one classifier with the combined features and for training multiple classifiers with features from each modality separately. Using the select set of features and combined training, multistate prediction accuracy averaged 0.64 +/- 0.14 across thirteen participants and was significantly higher than that for the separate training case. These results support the goal of demonstrating simultaneous real-time classification of multiple states using multiple sensing modalities in high fidelity flight simulators. This detection is intended to support and inform training methods under development to mitigate the loss of ASA and thus reduce accidents and incidents.

The jet-powered flying testbed (J-FLiC) unmanned aircraft system (UAS) successfully completed twenty-six flights at Fort AP Hill, VA, from 27 August until September 3 2015, supporting tests of a microphone array system for aircraft noise measurement. The test vehicles, J-FLiC NAVY2 (N508NU), and J-FLiC 4 (N509NU), were flown under manual and autopiloted control in a variety of test conditions: clean at speeds ranging from 80 to 150 knots; and full landing configuration at speeds ranging from 50 to 95 knots. During the test campaign, autopilot capability was incrementally improved to ultimately provide a high degree of accuracy and repeatability of the critical test requirements for airspeed, altitude, runway alignment and position over the microphone array. Manual flights were performed for test conditions at the both ends of the speed envelope where autopiloted flight would have required flight beyond visual range and more extensive developmental work. The research objectives of the campaign were fully achieved. The ARMD Integrated Systems Research Program (ISRP) Environmentally Responsible Aviation (ERA) Project aims to develop the enabling capabilities/technologies that will allow prediction/reduction of aircraft noise. A primary measurement tool for ascertaining and characterizing empirically the effectiveness of various noise reduction technologies is a microphone phased array system. Such array systems need to be vetted and certified for operational use via field deployments and overflights of the array with test aircraft, in this case with sUAS aircraft such as J-FLiC.

Software tools are being developed by the Autonomy Incubator at NASA's Langley Research Center that will provide an integrated and scalable capability to support research and non-research flight operations across several flight domains, including urban and mixed indoor-outdoor operations. These tools incorporate a full range of data products to support mission planning, approval, flight operations, and post-flight review. The system can support a number of different operational scenarios that can incorporate live and archived data streams for UAS operators, airspace regulators, and other important stakeholders. Example use cases are described that illustrate how the tools will benefit a variety of users in nominal and off-nominal operational scenarios. An overview is presented for the current state of the toolset, including a summary of current demonstrations that have been completed. Details of the final, fully operational capability are also presented, including the interfaces that will be supported to ensure compliance with existing and future airspace operations environments.

We present ongoing work in the Autonomy Incubator at NASA Langley Research Center (LaRC) exploring the efficacy of a data set aggregation approach to reinforcement learning for small unmanned aerial vehicle (sUAV) flight in dense and cluttered environments with reactive obstacle avoidance. The goal is to learn an autonomous flight model using training experiences from a human piloting a sUAV around static obstacles. The training approach uses video data from a forward-facing camera that records the human pilot’s flight. Various computer vision based features are extracted from the video relating to edge and gradient information. The recorded human-controlled inputs are used to train an autonomous control model that correlates the extracted feature vector to a yaw command. As part of the reinforcement learning approach, the autonomous control model is iteratively updated with feedback from a human agent who corrects undesired model output. This data driven approach to autonomous obstacle avoidance is explored for simulated forest environments furthering autonomous flight under the tree canopy research. This enables flight in previously inaccessible environments which are of interest to NASA researchers in Earth and Atmospheric sciences.

The NASA Langley Autonomy Incubator focuses on enabling autonomous, cooperative operations of multiple small Unmanned Aerial Vehicles (UAV) and, more generally, creating autonomous system technologies that change the way people and goods are moved from place to place. To enable rapid test and deployment of autonomous algorithms an avionics system is under development. The system, called the Autonomous Entity Operations Network Flight Control System (AEON-FCS), is being developed to be capable of implementing both classic Guidance, Navigation, and Control (GNC) law algorithms in a monolithic system architecture and also a network connected fully distributed control system. AEON-FCS is a subset of the overall AEON system that includes the Avionics System for Remotely Operated Vehicles LiTe (ASROV-LT) codebase and additional data centric tools for distributed control implementation and rapid simulation and testing. The ASROV-LT codebase provides utilities for hard real-time flight control loop processing and serialized sensor parsing. To enable rapid testing of autonomous algorithms, AEON-FCS provides seamless integration between simulation and hardware by utilizing a data centric inter-process communication approach and a global data bus available on the network. A goal for the AEON-FCS is to enable implementation of fully distributed control. Processing locations may be paired with sensors and distributed across either an airframe or across different air and/or ground vehicles on the network connected system. AEON-FCS aims to enable agile, high performance, robust operation of single or multiple cooperative UAV performing challenging missions like search and rescue under the forest canopy, delivery in rapidly changing unstructured environments (rescue in burning building), and ensemble collaboration for robust resilient system operations in hostile environments. The current state of the structure, function, and novel features of the AEON-FCS are described herein.

We present ongoing work in the Autonomy Incubator at NASA Langley Research Center (LaRC) exploring the efficacy of a data set aggregation approach to reinforcement learning for small unmanned aerial vehicle (sUAV) flight in dense and cluttered environments with reactive obstacle avoidance. The goal is to learn an autonomous flight model using training experiences from a human piloting a sUAV around static obstacles. The training approach uses video data from a forward-facing camera that records the human pilot's flight. Various computer vision based features are extracted from the video relating to edge and gradient information. The recorded human-controlled inputs are used to train an autonomous control model that correlates the extracted feature vector to a yaw command. As part of the reinforcement learning approach, the autonomous control model is iteratively updated with feedback from a human agent who corrects undesired model output. This data driven approach to autonomous obstacle avoidance is explored for simulated forest environments furthering autonomous flight under the tree canopy research. This enables flight in previously inaccessible environments which are of interest to NASA researchers in Earth and Atmospheric sciences.

With the anticipated increase of small unmanned aircraft systems (sUAS) entering into the National Airspace System, it is highly likely that vehicle operators will be teaming with fleets of small autonomous vehicles. The small vehicles may consist of sUAS, which are 55 pounds or less that typically will y at altitudes 400 feet and below, and small ground vehicles typically operating in buildings or defined small campuses. Typically, the vehicle operators are not concerned with manual control of the vehicle; instead they are concerned with the overall mission. In order for this vision of high-level mission operators working with fleets of vehicles to come to fruition, many human factors related challenges must be investigated and solved. First, the interface between the human operator and the autonomous agent must be at a level that the operator needs and the agents can understand. This paper details the natural language human factors e orts that NASA Langley's Autonomy Incubator is focusing on. In particular these e orts focus on allowing the operator to interact with the system using speech and gestures rather than a mouse and keyboard. With this ability of the system to understand both speech and gestures, operators not familiar with the vehicle dynamics will be able to easily plan, initiate, and change missions using a language familiar to them rather than having to learn and converse in the vehicle's language. This will foster better teaming between the operator and the autonomous agent which will help lower workload, increase situation awareness, and improve performance of the system as a whole.

The paper idea is designing system for autonomous unmanned aerial vehicles (UAVs) based on inertial guidance system. The mission of the UAV determining the position and the speed of UAV's from an initial position to a final position in a partially Known 3-D environment. To achieve this project in the right way, many steps must be executed. First, making stable design and it must be applicable for the aerodynamics equations. The avionics section that include the control system of inertial guidance system with gyroscope and accelerometer. The Gyroscope used to measuring the roll, yaw and pitch angle for determining the position. For achieving this technique the trapezoidal method for integration used with combining of the digital control for getting the actual position and comparing it with desired, hence the error will be eliminated. The velocity UAV'S measurement achieved by accelerometer, using the equation of the motion for getting the velocity of the vehicle. Besides that, UAV will equip by GPS for getting the longitude and latitude of UAV For the bypass state the UAV is controlled by RC FUTABA at the aviation pretest. The mission of UAV will plan a path from its current position to The terminal point, then follow such path for certain mission, communicate with human mind for secure data transmission. KeywordsUnmanned aerial vehicle, Gyroscope L3G4200D, Accelerometer MEMSIC 2125, Arduino Uno, Inertial Guidance Algorithm, Inertial measurement units.

A successful flight test and training campaign of the NASA Flying Controls Testbed was conducted at Naval Outlying Field, Webster Field, MD during 2008. Both the prop and jet-powered versions of the subscale, remotely piloted testbeds were used to test representative experimental flight controllers. These testbeds were developed by the Subsonic Fixed Wing Project s emphasis on new flight test techniques. The Subsonic Fixed Wing Project is under the Fundamental Aeronautics Program of NASA's Aeronautics Research Mission Directorate (ARMD). The purpose of these testbeds is to quickly and inexpensively evaluate advanced concepts and experimental flight controls, with applications to adaptive control, system identification, novel control effectors, correlation of subscale flight tests with wind tunnel results, and autonomous operations. Flight tests and operator training were conducted during four separate series of tests during April, May, June and August 2008. Experimental controllers were engaged and disengaged during fully autonomous flight in the designated test area. Flaps and landing gear were deployed by commands from the ground control station as unanticipated disturbances. The flight tests were performed NASA personnel with support from the Maritime Unmanned Development and Operations (MUDO) team of the Naval Air Warfare Center, Aircraft Division

A broad overview of current adaptive flight control research efforts at NASA is presented, as well as some more detailed discussion of selected specific approaches. The stated objective of the Integrated Resilient Aircraft Control Project, one of NASA s Aviation Safety programs, is to advance the state-of-the-art of adaptive controls as a design option to provide enhanced stability and maneuverability margins for safe landing in the presence of adverse conditions such as actuator or sensor failures. Under this project, a number of adaptive control approaches are being pursued, including neural networks and multiple models. Validation of all the adaptive control approaches will use not only traditional methods such as simulation, wind tunnel testing and manned flight tests, but will be augmented with recently developed capabilities in unmanned flight testing.

In this paper, a multiple discrete quasi-sliding mode (QSM) control scheme is proposed for a general class of nonlinear discrete time systems with unknown dynamical equations, provided that input–output data is available for system identification. The self-organizing map (SOM) is employed to divide the state space into local regions such that it associates the operating region where a local linear model is the winner with a local quasi-sliding mode controller (QSMC). Switching of the controllers is done synchronously with the active local linear model that tracks the different operating conditions. The simulation results show that the proposed controller outperforms tracking the desired trajectory in noisy environments either with a global controller or simpler controllers based on multiple models.

The latest version of the NASA Flying Controls Testbed (FLiC) integrates commercial-off-the-shelf components including airframe, autopilot, and a small turbine engine to provide a low cost experimental flight controls testbed capable of sustained speeds up to 200 mph. The series of flight tests leading up to the demonstrated performance of the vehicle in sustained, autopiloted 200 mph flight at NASA Wallops Flight Facility's UAV runway in August 2006 will be described. Earlier versions of the FLiC were based on a modified Army target drone, AN/FQM-117B, developed as part of a collaboration between the Aviation Applied Technology Directorate at Fort Eustis, Virginia and NASA Langley Research Center. The newer turbine powered platform (J-FLiC) builds on the successes using the relatively smaller, slower and less expensive unmanned aerial vehicle developed specifically to test highly experimental flight control approaches with the implementation of C-coded experimental controllers. Tracking video was taken during the test flights at Wallops and will be available for presentation at the conference. Analysis of flight data from both remotely piloted and autopiloted flights will be presented. Candidate experimental controllers for implementation will be discussed. It is anticipated that flight testing will resume in Spring 2007 and those results will be included, if possible.

This paper describes recent research into the advancement of small, electric powered unmanned aerial vehicle (UAV) capabilities. Specifically, topics include the improvements made in battery technology, design methodologies, avionics architectures and algorithms, materials and structural concepts, propulsion system performance prediction, and others. The results of prototype vehicle designs and flight tests are discussed in the context of their usefulness in defining and validating progress in the various technology areas. Further areas of research need are also identified. These include the need for more robust operating regimes (wind, gust, etc.), and continued improvement in payload fraction vs. endurance.

There are numerous autopilot systems that are commercially available for small (<100 lbs) UAVs. However, they all share several key disadvantages for conducting aerodynamic research, chief amongst which is the fact that most utilize older, slower, 8- or 16-bit microcontroller technologies. This paper describes the development and testing of a flight control system (FCS) for small UAV s based on a modern, high throughput, embedded processor. In addition, this FCS platform contains user-configurable hardware resources in the form of a Field Programmable Gate Array (FPGA) that can be used to implement custom, application-specific hardware. This hardware can be used to off-load routine tasks such as sensor data collection, from the FCS processor thereby further increasing the computational throughput of the system.

The NASA Flying Controls Testbed (FLiC) is a relatively small and inexpensive unmanned aerial vehicle developed specifically to test highly experimental flight control approaches. The most recent version of the FLiC is configured with 16 independent aileron segments, supports the implementation of C-coded experimental controllers, and is capable of fully autonomous flight from takeoff roll to landing, including flight test maneuvers. The test vehicle is basically a modified Army target drone, AN/FQM-117B, developed as part of a collaboration between the Aviation Applied Technology Directorate (AATD) at Fort Eustis, Virginia and NASA Langley Research Center. Several vehicles have been constructed and collectively have flown over 600 successful test flights, including a fully autonomous demonstration at the Association of Unmanned Vehicle Systems International (AUVSI) UAV Demo 2005. Simulations based on wind tunnel data are being used to further develop advanced controllers for implementation and flight test.

In the development and testing of novel structural and controls concepts, such as morphing aircraft wings, appropriate models are needed for proper system characterization. In most instances, available system models do not provide the required additional degrees of freedom for morphing structures but may be modified to some extent to achieve a compatible system. The objective of this study is to apply wind tunnel data collected for an Unmanned Air Vehicle (UAV), that implements trailing edge morphing, to create a non-linear dynamics simulator, using well defined rigid body equations of motion, where the aircraft stability derivatives change with control deflection. An analysis of this wind tunnel data, using data extraction algorithms, was performed to determine the reference aerodynamic force and moment coefficients for the aircraft. Further, non-linear influence functions were obtained for each of the aircraft's control surfaces, including the sixteen trailing edge flap segments. These non-linear controls influence functions are applied to the aircraft dynamics to produce deflection-dependent aircraft stability derivatives in a non-linear dynamics simulator. Time domain analysis of the aircraft motion, trajectory, and state histories can be performed using these non-linear dynamics and may be visualized using a 3-dimensional aircraft model. Linear system models can be extracted to facilitate frequency domain analysis of the system and for control law development. The results of this study are useful in similar projects where trailing edge morphing is employed and will be instrumental in the University of Maryland's continuing study of active wing load control.