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Model performance index /PI/ providing criterion for approximating one dynamic flight control system by another based on geometrical representation of linear autonomous systems
Model performance index /Pi/ providing criterion for approximating one dynamic flight control system by another based on geometrical representation of linear autonomous systems
Space motion sickness (SMS) affects 50 percent of all people during early days of spaceflight. This study describes the results of two Shuttle flight experiments in which autogenic-feedback training (AFT), a physiological conditioning method, was tested as a treatment for this disorder. Of the six who were designated as flight subjects (two women and four men), three were given treatment and three served as controls (i.e., no AFT). Treatment subjects were given 6 hours of preflight AFT. Preflight results showed that AFT produced a significant increase in tolerance to rotating chair motion sickness tests. Further, this increased tolerance was associated with changes in specific physiological responses and reports of reduced malaise. Flight results showed that two of the three control subjects experienced repeated vomiting on the first mission day, while one subject experienced only moderate malaise. Of the three treatment subjects, one experienced mild discomfort, one moderate discomfort, and one severe motion sickness. Only the three control subjects took medication for symptom suppression. Measures of cardiac function reflective of vagal control were shown to be affected especially strongly on the first day of space flight. AFT given for control of heart rate, respiration, and other autonomic activity influenced both the vagal control measures and SMS. These data suggest that AFT may be an effective treatment for space motion sickness; however, this cannot be demonstrated conclusively with the small number of subjects described.
The Earth Observing System Terra spacecraft was launched on December 18, 1999, to provide data for the characterization of the terrestrial and oceanic surfaces, clouds, radiation, aerosols, and radiative balance. The Tracking and Data Relay Satellite System (TDRSS) Onboard Navigation System (ONS) (TONS) flying on Terra provides the spacecraft with an operational real-time navigation solution. TONS is a passive system that makes judicious use of Terra's communication and computer subsystems. An objective of the ONS developed by NASA's Goddard Space Flight Center (GSFC) Guidance, Navigation and Control Center is to provide autonomous navigation with minimal power, weight, and volume impact on the user spacecraft. TONS relies on extracting tracking measurements onboard from a TDRSS forward-link communication signal and processing these measurements in an onboard extended Kalman filter to estimate Terra's current state. Terra is the first NASA low Earth orbiting mission to fly autonomous navigation which produces accurate results. The science orbital accuracy requirements for Terra are 150 meters (m) (3sigma) per axis with a goal of 5m (1 sigma) RSS which TONS is expected to meet. The TONS solutions are telemetered in real-time to the mission scientists along with their science data for immediate processing. Once set in the operational mode, TONS eliminates the need for ground orbit determination and allows for a smooth flow from the spacecraft telemetry to planning products for the mission team. This paper will present the preliminary results of the operational TONS solution available from Terra.
Substantial progress has been made recently towards design building and test-flying remotely piloted Micro Air Vehicle's (MAVs). We seek to complement this progress in overcoming the aerodynamic obstacles to.flight at very small scales with a vision stability and autonomy system. The developed system based on a robust horizon detection algorithm which we discuss in greater detail in a companion paper. In this paper, we first motivate the use of computer vision for MAV autonomy arguing that given current sensor technology, vision may he the only practical approach to the problem. We then briefly review our statistical vision-based horizon detection algorithm, which has been demonstrated at 30Hz with over 99.9% correct horizon identification. Next we develop robust schemes for the detection of extreme MAV attitudes, where no horizon is visible, and for the detection of horizon estimation errors, due to external factors such as video transmission noise. Finally, we discuss our feed-back controller for self-stabilized flight, and report results on vision autonomous flights of duration exceeding ten minutes.
Timeliner has been developed as a tool to automate procedural tasks. These tasks may be sequential tasks that would typically be performed by a human operator, or precisely ordered sequencing tasks that allow autonomous execution of a control process. The Timeliner system includes elements for compiling and executing sequences that are defined in the Timeliner language. The Timeliner language was specifically designed to allow easy definition of scripts that provide sequencing and control of complex systems. The execution environment provides real-time monitoring and control based on the commands and conditions defined in the Timeliner language. The Timeliner sequence control may be preprogrammed, compiled from Timeliner "scripts," or it may consist of real-time, interactive inputs from system operators. In general, the Timeliner system lowers the workload for mission or process control operations. In a mission environment, scripts can be used to automate spacecraft operations including autonomous or interactive vehicle control, performance of preflight and post-flight subsystem checkouts, or handling of failure detection and recovery. Timeliner may also be used for mission payload operations, such as stepping through pre-defined procedures of a scientific experiment.
Training is a major endeavor in all modern societies. Common training methods include training manuals, formal classes, procedural computer programs, simulations, and on-the-job training. NASA's training approach has focussed primarily on on-the-job training in a simulation environment for both crew and ground based personnel. NASA must explore new approaches to training for the 1990's and beyond. Specific autonomous training systems are described which are based on artificial intelligence technology for use by NASA astronauts, flight controllers, and ground based support personnel that show an alternative to current training systems. In addition to these specific systems, the evolution of a general architecture for autonomous intelligent training systems that integrates many of the features of traditional training programs with artificial intelligence techniques is presented. These Intelligent Computer Aided Training (ICAT) systems would provide much of the same experience that could be gained from the best on-the-job training.
The document describes the NASA Standard Reference Model (NASREM) Architecture for the Space Station Telerobot Control System. It defines the functional requirements and high level specifications of the control system for the NASA space Station document for the functional specification, and a guideline for the development of the control system architecture, of the 10C Flight Telerobot Servicer. The NASREM telerobot control system architecture defines a set of standard modules and interfaces which facilitates software design, development, validation, and test, and make possible the integration of telerobotics software from a wide variety of sources. Standard interfaces also provide the software hooks necessary to incrementally upgrade future Flight Telerobot Systems as new capabilities develop in computer science, robotics, and autonomous system control.
The Guidance, Navigation, and Control Center (GNCC) at Goddard Space Flight Center (GSFC) has successfully developed high-accuracy autonomous satellite navigation systems using the National Aeronautics and Space Administration's (NASA's) space and ground communications systems and the Global Positioning System (GPS). In addition, an autonomous navigation system that uses celestial object sensor measurements is currently under development and has been successfully tested using real Sun and Earth horizon measurements.The GNCC has developed advanced spacecraft systems that provide autonomous navigation and control of formation flyers in near-Earth, high-Earth, and libration point orbits. To support this effort, the GNCC is assessing the relative navigation accuracy achievable for proposed formations using GPS, intersatellite crosslink, ground-to-satellite Doppler, and celestial object sensor measurements. This paper evaluates the performance of these relative navigation approaches for three proposed missions with two or more vehicles maintaining relatively tight formations. High-fidelity simulations were performed to quantify the absolute and relative navigation accuracy as a function of navigation algorithm and measurement type. Realistically-simulated measurements were processed using the extended Kalman filter implemented in the GPS Enhanced Inboard Navigation System (GEONS) flight software developed by GSFC GNCC. Solutions obtained by simultaneously estimating all satellites in the formation were compared with the results obtained using a simpler approach based on differencing independently estimated state vectors.
The Traffic Aware Planner (TAP) is a cockpit-based advisory tool designed to be hosted on a Class 2 Electronic Flight Bag and developed to enable the concept of Traffic Aware Strategic Aircrew Requests (TASAR). This near-term concept provides pilots with optimized route changes that reduce fuel burn or flight time, avoids interactions with known traffic, weather and restricted airspace, and may be used by the pilots to request a trajectory change from air traffic control. TAP's internal architecture and algorithms are derived from the Autonomous Operations Planner, a flight-deck automation system developed by NASA to support research into aircraft self-separation. This paper reviews the architecture, functionality and operation of TAP.
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.
NASA is studying far-term air traffic management concepts that may increase operational efficiency through a redistribution of decisionmaking authority among airborne and ground-based elements of the air transportation system. One component of this research, En Route Free Maneuvering, allows trained pilots of equipped autonomous aircraft to assume responsibility for traffic separation. Ground-based air traffic controllers would continue to separate traffic unequipped for autonomous operations and would issue flow management constraints to all aircraft. To evaluate En Route Free Maneuvering operations, a human-in-the-loop experiment was jointly conducted by the NASA Ames and Langley Research Centers. In this experiment, test subject pilots used desktop flight simulators to resolve conflicts in cruise and descent, and to adhere to air traffic flow constraints issued by test subject controllers. Simulators at NASA Langley were equipped with a prototype Autonomous Operations Planner (AOP) flight deck toolset to assist pilots with conflict management and constraint compliance tasks. Results from the experiment are presented, focusing specifically on operations during the initial descent into the terminal area. Airborne conflict resolution performance in descent, conformance to traffic flow management constraints, and the effects of conflicting traffic on constraint conformance are all presented. Subjective data from subject pilots are also presented, showing perceived levels of workload, safety, and acceptability of autonomous arrival operations. Finally, potential AOP functionality enhancements are discussed along with suggestions to improve arrival procedures.
For the first time, a 3-D imaging Flash Lidar instrument has been used in flight to scan a lunar-like hazard field, build a 3-D Digital Elevation Map (DEM), identify a safe landing site, and, in concert with an experimental Guidance, Navigation, and Control (GN&C) system, help to guide the Morpheus autonomous, rocket-propelled, free-flying lander to that safe site on the hazard field. The flight tests served as the TRL 6 demo of the Autonomous Precision Landing and Hazard Detection and Avoidance Technology (ALHAT) system and included launch from NASA-Kennedy, a lunar-like descent trajectory from an altitude of 250m, and landing on a lunar-like hazard field of rocks, craters, hazardous slopes, and safe sites 400m down-range. The ALHAT project developed a system capable of enabling safe, precise crewed or robotic landings in challenging terrain on planetary bodies under any ambient lighting conditions. The Flash Lidar is a second generation, compact, real-time, air-cooled instrument. Based upon extensive on-ground characterization at flight ranges, the Flash Lidar was shown to be capable of imaging hazards from a slant range of 1 km with an 8 cm range precision and a range accuracy better than 35 cm, both at 1-delta. The Flash Lidar identified landing hazards as small as 30 cm from the maximum slant range which Morpheus could achieve (450 m); however, under certain wind conditions it was susceptible to scintillation arising from air heated by the rocket engine and to pre-triggering on a dust cloud created during launch and transported down-range by wind.
This study provided the first evaluation of a new training concept and technology aimed at training pilots to maintain physiological equilibrium during circumstances in an airplane cockpit. Thirty healthy subjects (16 males and 14 females) between the ages of 18 and 35 were randomized into two study groups, A and B. Subjects participated individually in a sequence of four study sessions. In the first visit, subjects were taught to operate a desktop fighter jet flight simulation program. In the three sessions that followed, subjects in group A were trained to minimize their autonomic deviation from baseline values while operating the desktop flight simulation. This was done by making their skin conductance and hand temperature deviations from baseline impair the functionality of the aircraft controls. Subjects also received auditory and visual cues about their autonomic deviation, and were instructed to keep these within pre-set limits to retain full control of the aircraft. Subjects in group B were subjected to periods of impaired aircraft functionality independent of their physiologic activity, and thus served as a control group. No statistically significant group differences were found in the flight performance scores from the three training sessions, and post-training flight performance scores of the two groups were not different. We conclude that this study did not provide clear support for this training methodology in optimizing pilot performance. However, a number of shortcomings in the current status of this training methodology may account for the lack of demonstrable training benefit to the experimental group. Suggested future modifications for research on this training methodology include: Limiting the amount of instrument impairment resulting from physiological deviations; conducting a greater number of physiological training sessions per subject; using pre-post training performance tests which invoke a greater amount of stress in subjects; and developing a more detailed performance scoring system.
The integration of uninhabited aerial vehicles (UAVs) into controlled airspace has generated a new era of autonomous technologies and challenges. Autonomous aerial refueling would enable UAVs to travel further distances and loiter for extended periods over time-critical targets. The NASA Dryden Flight Research Center recently has completed a flight research project directed at developing a dynamic hose and drogue system model to support the development of an automated aerial refueling system. A systematic dynamic model of the hose and drogue system would include the effects of various influences on the system, such as flight condition, hose and drogue type, tanker type and weight, receiver type, and tanker and receiver maneuvering. Using two NASA F/A-18 aircraft and a conventional hose and drogue aerial refueling store from the Navy, NASA has obtained flight research data that document the response of the hose and drogue system to these effects. Preliminary results, salient trends, and important lessons are presented.
The integration of uninhabited aerial vehicles (UAVs) into controlled airspace has generated a new era of autonomous technologies and challenges. Autonomous aerial refueling would enable UAVs to travel further distances and loiter for extended periods over time-critical targets. The NASA Dryden Flight Research Center recently has completed a flight research project directed at developing a dynamic hose and drogue system model to support the development of an automated aerial refueling system. A systematic dynamic model of the hose and drogue system would include the effects of various influences on the system, such as flight condition, hose and drogue type, tanker type and weight, receiver type, and tanker and receiver maneuvering. Using two NASA F/A-18 aircraft and a conventional hose and drogue aerial refueling store from the Navy, NASA has obtained flight research data that document the response of the hose and drogue system to these effects. Preliminary results, salient trends, and important lessons are presented
NASA has long supported research on intelligent control technologies that could allow space systems to operate autonomously or with reduced human supervision. Proposed uses range from automated control of entire space vehicles to mobile robots that assist or substitute for astronauts to vehicle systems such as life support that interact with other systems in complex ways and require constant vigilance. The potential for pervasive use of such technology to extend the kinds of missions that are possible in practice is well understood, as is its potential to radically improve the robustness, safety and productivity of diverse mission systems. Despite its acknowledged potential, intelligent control capabilities are rarely used in space flight systems. Perhaps the most famous example of intelligent control on a spacecraft is the Remote Agent system flown on the Deep Space One mission (1998 - 2001). However, even in this case, the role of the intelligent control element, originally intended to have full control of the spacecraft for the duration of the mission, was reduced to having partial control for a two-week non-critical period. Even this level of mission acceptance was exceptional. In most cases, mission managers consider intelligent control systems an unacceptable source of risk and elect not to fly them. Overall, the technology is not trusted. From the standpoint of those who need to decide whether to incorporate this technology, lack of trust is easy to understand. Intelligent high-level control means allowing software io make decisions that are too complex for conventional software. The decision-making behavior of these systems is often hard to understand and inspect, and thus hard to evaluate. Moreover, such software is typically designed and implemented either as a research product or custom-built for a particular mission. In the former case, software quality is unlikely to be adequate for flight qualification and the functionality provided by the system is likely driven largely by the need to publish innovative work. In the latter case, the mission represents the first use of the system, a risky proposition even for relatively simple software.
Urban Air Mobility (UAM) has captured the imagination of the public and the aviation industry for someday soon moving people and goods through and around metropolitan areas using Unmanned Aircraft Systems (UAS) that are electrically powered, environmentally friendly, and autonomously operated. Significant investment and rapid development of vehicles for this activity is taking place, with package delivery services already beginning in some areas. However, the ability to manage thousands of these vehicles safely in a congested urban area presents a challenge unprecedented in air traffic management. Initial studies of this problem led by NASA under the UAS Traffic Management (UTM) initiative have primarily focused on geo-fencing and centralized reservation of airspace for individual flights. This paper proposes an extension of UTM using a de-centralized approach employing airborne surveillance, self-separation, and a minimized “design separation” approach to permit the optimization and safety of each flight in very high traffic densities and close proximities. The concept employs Airborne Trajectory Management (ABTM) principles and a novel new concept for variable separation criteria to manage the angular velocity of a passing vehicle, thus eliminating the "startle factor" and perceived hazard of very close operations. ABTM also accomplishes most of the services required for safe planning and execution of normal flights and recovery from abnormal or emergency operations while accommodating conventional piloted flights using the current air traffic control paradigm. The environment for UAM operations is described along with the proposed means for autonomous, tactical separation of the vehicles. Sample geometries of traffic conflicts and resolutions are shown and the airspace definitions, rules for flight within them, and additions and exemptions to the rules for these flights are listed and explained.