Results for “Training Time” — Page 36

NASA NTRS2022

DataSet: Aerial Object Trajectory Classification by Training on Flight Controller Data and Testing on RADAR Generated

Onboard collision avoidance is needed to enable safe, autonomous flight operations for NASA projects such as Advanced Air Mobility (AAM), as well as many commercial applications. Real-time aerial object classification will improve onboard collision avoidance algorithm decision making and may reduce unnecessary activation of avoidance systems. This work trains an aircraft trajectory classifier using trajectories from flight controller logs and tests the classifier using RADAR collected trajectories during air to air experiments and ground to air experiments. In contrast to RADAR data, these flight controller logs are relatively abundant, which makes the possibility of substituting flight data for RADAR data an attractive, cost-effective option. The SVM model developed in this work achieved a 79.7% classification accuracy on the first second of radar trajectories of GA, multirotor sUAS, and fixed wing sUAS. Findings from this e that it is feasible to classify sensor collected trajectories using a classifier trained flight controller data.

Chester V Dolph↗

NASA NTRS1993

Interactive displays for trajectory planning and proximity operations

Rendezvous, docking, and other Space Station proximity operations (PROX OPS) will be conducted routinely in space. Real-time interactive visual aids and planning tools will be helpful, if not necessary, for future missions both in preflight training and on orbit. Two such displays, eivaN and Navie, are currently available for examination and human factors testing. A study was conducted in which data were collected from eight test subjects. Solution times for both devices decreased rapidly with experience. Neither fuel usage nor the number of waypoints (burns) decreased with experience. With Navie, medians of solution time and fuel consumption totaled over all subjects peaked at one of two starting points above the V-bar with monotonically decreasing values in both directions. This pattern did not appear with eivaN values. Since the docking tasks were fundamentally different with each device, and because Navie imposed more constraints on the users than eivaN did, the orbital mechanics effects had a more pronounced effect on the Navie results than on the eivaN data.

Brody, Adam R.↗

NASA NTRS1996

Behavioral analysis of signals that guide learned changes in the amplitude and dynamics of the vestibulo-ocular reflex

We characterized the dependence of motor learning in the monkey vestibulo-ocular reflex (VOR) on the duration, frequency, and relative timing of the visual and vestibular stimuli used to induce learning. The amplitude of the VOR was decreased or increased through training with paired head and visual stimulus motion in the same or opposite directions, respectively. For training stimuli that consisted of simultaneous pulses of head and target velocity 80-1000 msec in duration, brief stimuli caused small changes in the amplitude of the VOR, whereas long stimuli caused larger changes in amplitude as well as changes in the dynamics of the reflex. When the relative timing of the visual and vestibular stimuli was varied, brief image motion paired with the beginning of a longer vestibular stimulus caused changes in the amplitude of the reflex alone, but the same image motion paired with a later time in the vestibular stimulus caused changes in the dynamics as well as the amplitude of the VOR. For training stimuli that consisted of sinusoidal head and visual stimulus motion, low-frequency training stimuli induced frequency-selective changes in the VOR, as reported previously, whereas high-frequency training stimuli induced changes in the amplitude of the VOR that were more similar across test frequency. The results suggest that there are at least two distinguishable components of motor learning in the VOR. One component is induced by short-duration or high-frequency stimuli and involves changes in only the amplitude of the reflex. A second component is induced by long-duration or low-frequency stimuli and involves changes in the amplitude and dynamics of the VOR.

NASA Discipline Neuroscience↗

NASA NTRS2017

Updates and Overview of Spaceflight Medical Support in Russia and Kazakhstan

This panel presents recent updates to and a comprehensive overview of the operational medical support provided to ISS crewmembers in Star City, Russia and Kazakhstan as part of UTMB/KBRwyle's Human Health & Performance contract. With the current Soyuz training flow, physician support is required for nominal training evolutions involving pressure changes or other potential physical risks detailed in this presentation. In addition, full-time physician presence in Star City helps to address the disparity in access to health care in these relatively remote practice areas, while also developing and maintaining relationships with host nation resources. A unique part of standard training in Russia also involves survival training in both winter and water environments; logistic details and medical impacts of each of these training scenarios will be discussed. Following support of a successful training flow, UTMB/KBRwyle's Star City Medical Support Group (SCMSG) is also responsible for configuring medical packs in support of Soyuz launches and landings; we will present the rationale for current pack contents within the context of specific operational needs. With respect to contingency events, the group will describe their preparedness to respond appropriately by activating both local and global resources as necessary, detailing a specialized subset of the group who continually work and update these assets, given changes in international infrastructure and other impacts.

Chough, Natacha↗

NASA NTRS1961

Post University On-the-Job Training for Engineers

Our national need for qualified scientists and engineers is greater now than at any other time in our history. Fortunately, we can point with pride to this need as a measure of the impact of science and technology on our way of life. In effect, we have made such rapid strides In advancing established sciences and in opening new technological fields that we have proved the value of the scientist and engineer to society, and, as a-result, have created an expanding demand for their services which we must now attempt to satisfy. This demand we face is also due to the changing skills and high degree of specialization required to perform in these new technological fields. The colleges and universities are doing their part to provide current graduates with a modern technical foundation, but we cannot afford to ignore the thousands of experienced engineers and scientists already employed by private industry and government. As employers, we have an obligation to these men and women to see that they are provided with an understanding of the latest advances that modern technology has to offer; that we develop them in particular specialty areas characteristic of a given field of work; and, equally important, that we assist them in the transition from one field to another as the technological emphasis shifts. Practically all technological industries have experienced and continue to experience rapid changes in their activities. The aerospace business, in particular, has been characterized by extremely rapid, in fact revolutionary, changes during the relatively short period of its existence0 At the National Aeronautics and Space Administration, successor to the National Advisory Committee for Aeronautics, for example, we have encountered the fun impact of a changing science and technology. Indeed, as a research organization, we have undoubtedly contributed, in some measure, to this change. Within the NASAs Lewis Research Center, we have approximately 800 research scientists and engineers who have matured professionally in an environment which is essentially one of continuous learning - an experience which comes close to being a form of post graduate training in itself. This environment, in addition to providing continuous evolutionary changes, has also provided two major revolutions which have made this development picture more complex. We will describe these environmental changes which have occurred at the Lewis Research Center and discuss the various techniques and programs we have employed to provide for the professional development of our staff. The Lewis Research Center has had an Interesting and exciting l8-year history of aerospace propulsion research and development. It began during the early years of World War II as an expansion of the Power Plant Division of the NCA Langley Center with the mission of conducting research required for the development of improved reciprocating engines and to study the associated problems of subsonic propulsion aerodynamics, It was only a few years later, however, that turbojet and ramjet propulsion and supersonic flight research became our main concern. This transition to jet type engines and higher speeds was our first major technological change. The aerodynamics of propellers became the aerodynamics of high speed turbine and compressor blades; the fuel ignition and carbon deposition problems were transferred from a cyclical or Intermittent high compression combustion chamber to a continuous combustion zone within a thin-walled metal shell; aerodynamics problems were thrust into the supersonic range; and high temperature materials began to play an increasingly critical role. Although this transition still required the same basic knowledge and principles as before, the new engine types did involve a different emphasis and variety of consideration not generally familiar to our scientists and engineers.

Manganiello, Eugene J.↗

NASA NTRSDate not supplied

Aerial Object Trajectory Classification by Training on Flight Controller Data and Testing on RADAR Generated Tracks

Onboard collision avoidance is needed to enable safe, autonomous flight operations for NASA projects such as Advanced Air Mobility (AAM), as well as many commercial applications. Real-time aerial object classification will improve onboard collision avoidance algorithm decision making and may reduce unnecessary activation of avoidance systems. This work trains an aircraft trajectory classifier using trajectories from flight controller logs and tests the classifier using RADAR collected trajectories during air to air experiments and ground to air experiments. In contrast to RADAR data, these flight controller logs are relatively abundant, which makes the possibility of substituting flight data for RADAR data an attractive, cost-effective option. The SVM model developed in this work achieved a 79.7% classification accuracy on the first second of radar trajectories of GA, multirotor sUAS, and fixed wing sUAS. Findings from this work indicate that it is feasible to classify sensor collected trajectories using a classifier trained on flight controller data.

Henry Holbrook↗

NASA NTRS2005

BEAM Technology Flight Demonstration

As technologies advance, their growing complexity makes them harder to maintain. Detection methods for isolating and identifying impending problems are needed to balance this complexity. Through comparison of signal pairs from onboard sensors, the Beacon-based Exception Analysis For Multimissions (BEAM) algorithm can identify and help classify deviations in system operation from a data-trained statistical model. The goal of this task is to mature BEAM and validate its performance on a flying test bed. A series of F-18 flight demonstrations with BEAM monitoring engine parameters in real time was used to demonstrate in-the-field readiness. Captured F-18 and simulated F-18 engine data were used in model creation and training. The algorithm was then ported to the embedded system with a data buffering, file writing, and data-time-stamp monitoring shell to reduce the impact of embedded system faults on BEAM'S ability to correctly identify engine faults. Embedded system testing identified hardware related restrictions and contributed to iterative improvements in the code's runtime performance. The system was flown with forced engine flameouts and other pilot induced faults to simulate operation out of the norm. Successful detection of these faults, confirmed through post-flight data analysis, helped BEAM achieve TRL6.

Wang, David↗

NASA NTRS1994

Neural Network Classifies Teleoperation Data

Prototype artificial neural network, implemented in software, identifies phases of telemanipulator tasks in real time by analyzing feedback signals from force sensors on manipulator hand. Prototype is early, subsystem-level product of continuing effort to develop automated system that assists in training and supervising human control operator: provides symbolic feedback (e.g., warnings of impending collisions or evaluations of performance) to operator in real time during successive executions of same task. Also simplifies transition between teleoperation and autonomous modes of telerobotic system.

Fiorini, Paolo↗

NASA NTRSDate not supplied

Crew Time Requirements in Future Space Greenhouses - What Can We Infer from Current Analog and Space Missions?

Efficient crop production will be required to advance humanity’s presence in space, and for this, accurate predictions of crew time in future space greenhouse modules will be crucial to design and operate these plant growth systems, and schedule crop production. Crew time estimates will also be critical for deciding priorities of automating different aspects of space crop production. Because it is difficult to capture in operational environments, crew time for plant cultivation has only been sporadically recorded in past analog and space missions. We propose a methodology for efficient categorizing and reporting of crew time in space plant growth systems: first identify the different tasks needed to operate the greenhouse module, second define a representative time period for data collection, third accurately report crew time for individual tasks - and their occurrence, and fourth use collected data to improve greenhouse modules and plant growth system designs. Using data from various analog facilities and from the Veggie hardware on ISS, and assumptions for different mission scenarios, we discuss how crew time for plant cultivation can be reduced with adequate choices of crops, automation, artificial intelligence (AI) and virtual assistants, and sufficient crew training. This has major implications for the design of future space greenhouse modules. For example, missions on future space stations or during interplanetary travel would save significant crew time by including leafy greens and microgreens for astronaut’s diet supplement, with automated watering, health and environmental checks, as well as AI managing maintenance schedules, and a virtual assistant for repair activities. This work was funded by NASA Space Biology through NASA postdoctoral program / USRA, by NASA’s Space Biology and Human Research Programs, and by the European Union Horizon 2020 program via the COMPET-07-2014 - Space exploration – Life-support subprogram (reference number: 636501).

Lucie Poulet↗

NASA NTRSDate not supplied

Crew Time Requirements in Future Space Greenhouses: What Can We Infer from Current Analog and Space Missions?

Efficient crop production will be required to advance humanity’s presence in space, and for this, accurate predictions of crew time in future space greenhouse modules will be crucial to design and operate these plant growth systems, and schedule crop production. Crew time estimates will also be critical for deciding priorities of automating different aspects of space crop production. Because it is difficult to capture in operational environments, crew time for plant cultivation has only been sporadically recorded in past analog and space missions. We propose a methodology for efficient categorizing and reporting of crew time in space plant growth systems: first identify the different tasks needed to operate the greenhouse module, second define a representative time period for data collection, third accurately report crew time for individual tasks - and their occurrence, and fourth use collected data to improve greenhouse modules and plant growth system designs. Using data from various analog facilities and from the Veggie hardware on ISS, and assumptions for different mission scenarios, we discuss how crew time for plant cultivation can be reduced with adequate choices of crops, automation, artificial intelligence (AI) and virtual assistants, and sufficient crew training. This has major implications for the design of future space greenhouse modules. For example, missions on future space stations or during interplanetary travel would save significant crew time by including leafy greens and microgreens for astronaut’s diet supplement, with automated watering, health and environmental checks, as well as AI managing maintenance schedules, and a virtual assistant for repair activities. This work was funded by NASA Space Biology through NASA postdoctoral program / USRA, by NASA’s Space Biology and Human Research Programs, and by the European Union Horizon 2020 program via the COMPET-07-2014 - Space exploration – Life-support subprogram (reference number: 636501).

Veggie↗

NASA NTRSDate not supplied

Achieving Resilient In-Flight Performance for Advanced Air Mobility through Simplified Vehicle Operations

A research and development (R&D) approach is proposed for developing and validating concepts and technologies to achieve vehicle autonomy goals of Advanced Air Mobility (AAM) through Simplified Vehicle Operations (SVO). The approach applies resilience-engineering and human-automation teaming (HAT) principles to a framework for defining vehicle-based functions for the management of missions and flight trajectories, focusing initially on the en route flight domain. To achieve the SVO goal of reducing pilot training requirements and thereby increasing the pilot pool for AAM, while at the same time promoting ever-safer operations, a framework for identifying essential functions is proposed. In this framework, functions are first categorized by high-level functional purpose (mission management, flightpath management, tactical operations, and vehicle control) and then subcategorized by attributes of resilient-performing systems (abilities to monitor, respond, learn, and anticipate). The categorization by functional purpose provides structure within which HAT designs can be holistically explored and total levels of human vs. automation responsibility can be varied. The subcategorization by resilient-system attributes provides a mechanism for capturing safety-critical functions that may not be codified in current operational procedures and training curricula, particularly those where humans proactively enhance safety in currently undocumented ways. An R&D approach consisting of seven strategies is proposed in which automation engineering and human-factors communities can collaborate in the research, development, and design of an SVO roadmap to enable the ambitious objectives of AAM.

AAM↗

NASA NTRS2014

Virtual Reality as a Medium for Sensorimotor Adaptation Training and Spaceflight Countermeasures

Astronauts experience a profound sensorimotor adaptation during transition to and from the microgravity environment of space. With the upcoming shift to extra‐long duration missions (upwards of 1 year) aboard the International Space Station, the immediate risks to astronauts during these transitory periods become more important than ever to understand and prepare for. Recent advances in virtual reality technology enable everyday adoption of these tools for entertainment and use in training. Embedding an individual in a virtual environment (VE) allows the ability to change the perception of visual flow, elicit automatic motor behavior and produce sensorimotor adaptation, not unlike those required during long duration microgravity exposure. The overall goal of this study is to determine the feasibility of present head mounted display technology (HMD) to produce reliable visual flow information and the expected adaptation associated with virtual environment manipulation to be used in future sensorimotor adaptability countermeasures. To further understand the influence of visual flow on gait adaptation during treadmill walking, a series of discordant visual flow manipulations in a virtual environment are proposed. Six healthy participants (3 male and 3 female) will observe visual flow information via HMD (Oculus Rift DK2) while walking on an instrumented treadmill at their preferred walking speed. Participants will be immersed in a series of VE's resembling infinite hallways with different visual characteristics: an office hallway, a hallway with pillars and the hallway of a fictional spacecraft. Participants will perform three trials of 10 min. each, which include walking on the treadmill while receiving congruent or incongruent visual information via the HMD. In the first trial, participants will experience congruent visual information (baseline) where the hallway is perceived to move at the same rate as their walking speed. The final two trials will be randomized among participants where the hallway is perceived to move at either half (0.5x) or twice (2.0x) their preferred walking speed. Participants will remain on the treadmill between trials and will not be warned of the upcoming change to visual flow to minimize preparatory adjustments. Stride length, step frequency and dual‐support time will be quantified during each trial. We hypothesize that participants will experience a rapid modification in gait performance during periods of adaptive change, expressed as a decrease in step length, an increase in step frequency and an increase in dual‐support time, followed by a period of adaptation where these movement parameters will return to near‐baseline levels. As stride length, step frequency and dual support times return to baseline values, an adaptation time constant will be derived to establish individual time‐to‐adapt (TTA). HMD technology represents a paradigm shift in sensorimotor adaptation training where gait adaptability can be stressed using off‐the‐shelf consumer products and minimal experimental equipment, allowing for greater training flexibility in astronaut and terrestrial applications alike.

Madansingh, S.↗

NASA NTRS2008

Vertical Jump Height is more Strongly Associated with Velocity and Work Performed Prior to Take-off

Vertical jump (VJ) height is commonly used as a measure of athletic capability in strength and power sports. Although VJ has been shown to be a predictor of athletic performance, it is not clear which kinetic ground reaction force (GRF) variables, such as peak force (PF), peak power (PP), peak velocity (PV), total work (TW) or impulse (Imp) are the best correlates. To determine which kinetic variables (PF, PP, PV, TW, and Imp) best correlate with VJ height. Twenty subjects (14 males, 6 females) performed three maximal countermovement VJs on a force platform (Advanced Mechanical Technology, Inc., Watertown, MA, USA). VJ jump height was calculated as the difference between standing reach and the highest reach point measured using a Vertec. PF, PP, PV, TW, and Imp were calculated using the vertical GRF data sampled at 1000 Hz from the lowest point in the countermovement through the concentric portion until take-off. GRF data were normalized to body mass measured using a standard scale (Detecto, Webb City, MO, USA). Correlation coefficients were computed between each GRF variable and VJ height using a Pearson correlation. VJ height (43.4 plus or minus 9.1 cm) was significantly correlated (p less than 0.001) with PF (998 plus or minus 321 N; r=0.51), PP (1997 plus or minus 772 W; r=0.69), PV (2.66 plus or minus 0.40 m (raised dot) s(sup -1); r=0.85), TW (259 plus or minus 93.0 kJ; r=0.82), and Imp (204 plus or minus 51.1 N(raised dot)s; r=0.67). Although all variables were correlated to VJ height, PV and TW were more strongly correlated to VJ height than PF, PP, and Imp. Therefore, since TW is equal to force times displacement, the relative displacement of the center of mass along with the forces applied during the upward movement of the jump are critical determinants of VJ height. PV and TW are key determinants of VJ height, and therefore successful training programs to increase VJ height should focus on rapid movement (PV) and TW by increasing power over time rather than focusing on PF alone.

Bentley, J. R.↗

NASA NTRSDate not supplied

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

J Lemery↗

NASA NTRS2024

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

Medical Operations↗

NASA NTRS2010

Developing Collective Training for Small Unmanned Aerial Systems Employment

The projected use of small unmanned aerial systems (SUAS) in military operations will produce training requirements which go beyond current capabilities. The paper describes the development of prototype training procedures and accompanying research simulations to address this need. We initially constructed a testbed to develop simulation-based training for an SUAS operator equipped with a simulated vertical-lift and land SUAS. However, the required training will go beyond merely training an operator how to pilot an SUAS. In addition to tactics, techniques, and procedures for employment of SUASs, collective training methods must be trained. Moreover, the leader of a unit equipped with SUAS will need to learn how to plan missions which incorporate the SUAS, and take into account air space and frequency management considerations. The demands of the task require the leader to allocate personnel to the SUAS mission, communicate and coordinate with those personnel during the mission, and make use of the information provided. To help address these training issues, we expanded our research testbed to include a command and control node (C2 node), to enable communications between a leader and the SUAS operator. In addition, we added a virtual environment in which dismounted infantry missions can be conducted. This virtual environment provides the opportunity for interactions among human-controlled avatars and non-player characters (NPCs), plus authoring tools to construct scenarios. Using these NPCs, a collective exercise involving friendly, enemy, and civilian personnel can be conducted without the need for a human role-player for every entity. We will describe the results of our first experiment, which examined the ability of players to negotiate use of the C2 node and the virtual environment at the same time, in order to see if this is a feasible combination of tools for training development.

Durlach, Paula J.↗

NASA NTRS2010

Fast-Response-Time Shape-Memory-Effect Foam Actuators

Bulk shape memory alloys, such as Nitinol or CuAlZn, display strong recovery forces undergoing a phase transformation after being strained in their martensitic state. These recovery forces are used for actuation. As the phase transformation is thermally driven, the response time of the actuation can be slow, as the heat must be passively inserted or removed from the alloy. Shape memory alloy TiNi torque tubes have been investigated for at least 20 years and have demonstrated high actuation forces [3,000 in.-lb (approximately equal to 340 N-m) torques] and are very lightweight. However, they are not easy to attach to existing structures. Adhesives will fail in shear at low-torque loads and the TiNi is not weldable, so that mechanical crimp fits have been generally used. These are not reliable, especially in vibratory environments. The TiNi is also slow to heat up, as it can only be heated indirectly using heater and cooling must be done passively. This has restricted their use to on-off actuators where cycle times of approximately one minute is acceptable. Self-propagating high-temperature synthesis (SHS) has been used in the past to make porous TiNi metal foams. Shape Change Technologies has been able to train SHS derived TiNi to exhibit the shape memory effect. As it is an open-celled material, fast response times were observed when the material was heated using hot and cold fluids. A methodology was developed to make the open-celled porous TiNi foams as a tube with integrated hexagonal ends, which then becomes a torsional actuator with fast response times. Under processing developed independently, researchers were able to verify torques of 84 in.-lb (approximately equal to 9.5 Nm) using an actuator weighing 1.3 oz (approximately equal to 37 g) with very fast (less than 1/16th of a second) initial response times when hot and cold fluids were used to facilitate heat transfer. Integrated structural connections were added as part of the net shape process, eliminating the need for welding, adhesives, or mechanical crimping. Inexpensive net-shape processing was used, which reduces the cost of the actuator by over a factor of 10 over nonporous TiNi made by hot drawing of tube or electrical discharge machining. By forming the alloy as an open-celled foam, the surface area for heat transfer is dramatically increased, allowing for much faster response times. The technology also allows for netshape fabrication of the actuator, which allows for structural connections to be integrated into the actuator material, making these actuators significantly less expensive. Commercial applications include actuators for concepts such as the variable area chevron and nozzle in jet aircraft. Lightweight tube or rod components can be supplied to interested parties.

Jardine, Peter↗

NASA NTRS1994

Autogenic-feedback training as a treatment for airsickness in high-performance military aircraft: Two case studies

The purpose of this paper is to present a detailed description of the physiological and performance responses of two military pilots undergoing a treatment for motion sickness. The treatment used, Autogenic-Feedback Training (AFT), is an operant conditioning procedure where subjects are taught to control several of their autonomic responses and thereby suppress their motion sickness symptoms. Two male, active duty military pilots (U.S. Navy and U. S. Marine Corps), ages 30 and 35, were each given twelve 30-minute training sessions. The primary criterion for success of training was the subject's ability to tolerate rotating chair motion sickness tests for progressively longer periods of time and at higher rotational velocities. A standardized diagnostic scale was used during motion sickness to assess changes in the subject's perceived malaise. Physiological data were obtained from one pilot during tactical maneuvers in an F-18 aircraft after completion of his training. A significant increase in tolerance to laboratory-induced motion sickness tests and a reduction in autonomic nervous system (ANS) response variability was observed for both subjects after training. Both pilots were successful in applying AFT for controlling their airsickness during subsequent qualification tests on F-18 and T-38 aircraft and were returned to active duty flight status.

Cowings, Patricia S.↗