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All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

ATHLETE Offloader Limb as a High-capacity Crane

A new concept for the NASA Jet Propulsion Laboratory (JPL) All-Terrain Hex-Limbed Extra-Terrestrial Explorer (ATHLETE) robotic constructor / mobility system employs tendon-driven actuation of individual limbs, similar to high-capacity cranes in terrestrial work environments. While maintaining mechanical joints that allow each limb to function as a highly dexterous multi-Degree-Of-Freedom (DOF) robotic arm, tendon-driven truss sections increase the capacity of moment loads for extended configurations of the limb. This paper describes the analysis, work cell, operations, actuation, control, and instrumentation of limbs designed to address specific target load cases that might be required for human exploration missions on planetary surfaces.

Wilcox, Brian H.↗
NASA NTRSDate not supplied

Gravity Offloading and Analysis of Long Imperfection-Sensitive Elements (GOALIE)

Use of large-scale deployable structures (e.g., iROSA) can enable space exploration and science missions (e.g., ACS3, Solar Cruiser). Numerical models predicting behavior of deployable structures in microgravity are difficult to validate because ground testing of long, slender, and flexible booms is challenging due to the influence of Earth’s gravity. The GOALIE team is collecting data for model validation through experimental testing to increase confidence in numerical models. Redwire, Inc. provided a subscale (7 m) and a full-scale (30 m) Triangular, Rollable, and Collapsible (TRAC) boom to use for test development. Other boom designs may be tested in the future.

Composites↗
DOE OSTI2024

Sum Reduction with OpenMP Offload on NVIDIA Grace-Hopper System

We evaluate the performance of the baseline and optimized reductions in OpenMP on an NVIDIA Grace-Hopper system. We explore the impacts of the number of teams, the number of elements to sum per loop iteration, and simultaneous execution on the central-processing unit (CPU) and the GPU in the unified memory (UM) mode upon the reduction performance. The experimental results show that the optimized reductions are 6.120X to 20.906X faster than the baselines on the GPU, and their efficiency ranges from 89% to 95% of the theoretical GPU memory bandwidth. Depending on where an input array is allocated in the program when co-running the reduction on the CPU and GPU in the UM mode, the average speedup over the GPU-only execution is approximately 2.484 or 1.067, and the speedup of the optimized reductions over the baseline reductions ranges from 0.996 to 10.654 or from 0.998 to 6.729.

Jin, Zheming↗
NASA NTRS2010

Improving Realism in Reduced Gravity Simulators

Since man was first determined to walk on the moon, simulating the lunar environment became a priority. Providing an accurate reduced gravity environment is crucial for astronaut training and hardware testing. This presentation will follow the development of reduced gravity simulators to a final comparison of environments between the currently used systems. During the Apollo program era, multiple systems were built and tested, with several NASA centers having their own unique device. These systems ranged from marionette-like suspension devices where the subject laid on his side, to pneumatically driven offloading harnesses, to parabolic flights. However, only token comparisons, if any, were made between systems. Parabolic flight allows the entire body to fall at the same rate, giving an excellent simulation of reduced gravity as far as the biomechanics and physical perceptions are concerned. While the effects are accurate, there is limited workspace, limited time, and high cost associated with these tests. With all mechanical offload systems only the parts of the body that are actively offloaded feel any reduced gravity effects. The rest of the body still feels the full effect of gravity. The Partial Gravity System (Pogo) is the current ground-based offload system used to training and testing at the NASA Johnson Space Center. The Pogo is a pneumatic type system that allows for offloaded motion in the z-axis and free movement in the x-axis, but has limited motion in the y-axis. The pneumatic system itself is limited by cylinder stroke length and response time. The Active Response Gravity Offload System (ARGOS) is a next generation groundbased offload system, currently in development, that is based on modern robotic manufacturing lines. This system is projected to provide more z-axis travel and full freedom in both the x and y-axes. Current characterization tests are underway to determine how the ground-based offloading systems perform, how they compare to parabolic flights, and which of the systems is preferable for specific uses. These tests were conducted with six degree of freedom robots and manual inputs. Initial results show a definitive difference in abilities of the two offload systems.

Cowley, Matthew↗
NASA NTRSDate not supplied

Modeling and Simulation Efforts to Support Improved Comfort in ARGOS

BACKGROUND: The Active Response Gravity Offload System (ARGOS) provides an analog environment for extravehicular activity (EVA) testing and training. Discomfort has been observed during longer suited test sessions. While the subject’s core is offloaded during surface EVA evaluations, his/her arms experience full Earth gravity and can become overly fatigued, especially during suited tests which involve reaching and prolonged arm extensions. A device (ARGOS Negation of Gravitational Effects on the Limbs: ANGEL) to offload the weight of the arms and suit sleeves is being developed by JSC’s Flight Systems Branch, and here we present preliminary modeling of that device using the open-source biomechanical tool OpenSim [1,2] with an in-house developed plugin. We analyze a series of motions performed by a single shirt-sleeved subject with goals of characterizing the device, validating the model, and predicting whether reduced gravity conditions (i.e., lunar gravity (Lg) or Martian gravity (Mg)) can be accurately simulated with the device, as well as providing comfort to the ARGOS user. METHODS AND RESULTS: To model the offload device, we augment the OpenSim human model topology with the offload mechanism components and joints, using CAD models to represent the mechanism graphically. The joint angles of the device are either obtained from (1) inverse kinematics (IK) using motion capture markers on the various components of the device or (2) calculated in the OpenSim plugin by modeling how the components configure themselves under the offloading spring tension given a particular IK-derived arm position. Given the joint angles of the device, the resulting force on the arm is computed by the plugin and applied as an external load in inverse dynamics (ID) in order to enable study of overall shoulder joint torques as well as offload achieved. We verify the calculated joint angles by using the inverse kinematic data and the forces from manual measurements of the spring both independently and integrated within the device. We found that calculated joint angles generally represent the angles measured and computed with IK, supporting a possible analysis workflow inputting human motion data and observing system behavior under varied design parameters. In two different device configurations in which the maximum applied force was 131 N, our current model accurately captured force with a difference of 2-3 N from measured loads. Though our initial test was performed with a shirt-sleeve subject, arm weights were added to emulate the weight of the suit sleeve and the subject was positioned in a test stand with a Mark-III Hard Upper Torso (HUT) and Portable Life Support System (PLSS) mockup. Arm range of motion tasks were performed outside of the HUT, inside the HUT, and inside the HUT while using the device. A variety of other upper body tasks were completed as well. In summary, we have developed a model to investigate and verify an upper limb offload device currently in development. We believe this model will be a valuable tool not only for device characterization but also to predict proper configurations to simulate Lg or Mg conditions, investigate range of motion concerns, predict limitations such as internal collisions and contacts, and inform future design improvements.

L B Nilsson↗
NASA NTRS2022

Reduced-Gravity Simulator for Field Environments - Drone Augmented System

Astronauts need to go through extensive training on Earth before heading into space, and the more accurate the analogue, the better prepared they will be. NASA currently trains astronauts in simulated lower gravity environments in either the Neutral Buoyancy Laboratory or the Active Response Gravity Offload System (ARGOS), but neither of these systems allow for testing outdoors in field environments. NASA, in partnership with Aquiline Drones, mechanical engineering, biomedical engineering, and the Krenicki Arts and Engineering Institute, are working to develop a gravity offload device compatible with testing in field environments such as Desert Research and Technology Studies (Desert RATS). This report covers the technical requirements of the gravity offload device, the design choices and justifications, options for scalability, current progress, and future goals. Two separate teams were formed to respond to NASA’s solicitation, and this report covers the work of the weather balloon and drone side of the project. Using a weather balloon to passively offload the user’s weight and a drone to actively respond to changes using a series of sensors, this system should create a consistent and well balanced offloading force. The weather balloon and drone system is connected to a harness system to comfortably lift the user, offload at their center of gravity, and provide attachment points for some of the necessary sensors. Final testing proved the comfort of the harness, mobility of the user, and passive stability of the balloon offload to exceed all expectations of the team. The current system is a scaled model of what NASA would be able to implement with a maximum offload of 25 pounds. The ability to scale up to NASA’s desired value of 100 pounds is easily achievable with linear scaling of the current model. All systems should work similarly on scaled versions, but minor tweaking and enhanced controls would be needed for optimal performance.

Jason Lee↗
NASA NTRS2007

Robotics

Lunar robotic functions include: 1. Transport of crew and payloads on the surface of the moon; 2. Offloading payloads from a lunar lander; 3. Handling the deployment of surface systems; with 4. Human commanding of these functions from inside a lunar vehicle, habitat, or extravehicular (space walk), with Earth-based supervision. The systems that will perform these functions may not look like robots from science fiction. In fact, robotic functions may be automated trucks, cranes and winches. Use of this equipment prior to the crew s arrival or in the potentially long periods without crews on the surface, will require that these systems be computer controlled machines. The public release of NASA's Exploration plans at the 2nd Space Exploration Conference (Houston, December 2006) included a lunar outpost with as many as four unique mobility chassis designs. The sequence of lander offloading tasks involved as many as ten payloads, each with a unique set of geometry, mass and interface requirements. This plan was refined during a second phase study concluded in August 2007. Among the many improvements to the exploration plan were a reduction in the number of unique mobility chassis designs and a reduction in unique payload specifications. As the lunar surface system payloads have matured, so have the mobility and offloading functional requirements. While the architecture work continues, the community can expect to see functional requirements in the areas of surface mobility, surface handling, and human-systems interaction as follows: Surface Mobility 1. Transport crew on the lunar surface, accelerating construction tasks, expanding the crew s sphere of influence for scientific exploration, and providing a rapid return to an ascent module in an emergency. The crew transport can be with an un-pressurized rover, a small pressurized rover, or a larger mobile habitat. 2. Transport Extra-Vehicular Activity (EVA) equipment and construction payloads. 3. Transport habitats and power modules over long distances, pre-positioning them for the arrival of crew on a subsequent lander. Surface Handling 1. Offload surface system payloads from the lander, breaking launch restraints and power/data connections. Payloads may be offloaded to a wheeled vehicle for transport. 2. Deploy payloads from a wheeled vehicle at a field site, placing the payloads in their final use site on the ground or mating them with existing surface systems. 3. Support regolith collection, site preparation, berm construction, or other civil engineering tasks using tools and implements attached to rovers. Human-Systems Interaction 1. Provide a safe command and control interface for suited EVA to ride on and drive the vehicles, making sure that the systems are also safe for working near dismounted crew. 2. Provide an effective control system for IV crew to tele-operate vehicles, cranes and other equipment from inside the surface habitats with evolving independence from Earth. .. Provide a supervisory system that allows machines to be commanded from the ground, working across the Earth-Lunar time delays on the order of 5-10 seconds (round trip) to support operations when crew are not resident on the surface. Technology Development Needs 1. Surface vehicles that can dock, align and mate with outpost equipment such as landers, habitats and fluid/power interfaces. 2. Long life motors, drive trains, seals, motor electronics, sensors, processors, cable harnesses, and dash board displays. 3. Active suspension control, localization, high speed obstacle avoidance, and safety systems for operating near dismounted crew. 4. High specific energy and specific power batteries that are safe, rechargeable, and long lived.

Ambrose, Robert O.↗
NASA NTRS2010

Lessons Learned from Performance Testing of Humans in Spacesuits in Simulated Reduced Gravity

Introduction: The overarching objective of the Integrated Suit Test (IST) series is to evaluate suited human performance using reduced-gravity analogs and learn what aspects of an EVA suit system affect human performance. For this objective to be successfully achieved, the testing methodology should be valid and reproducible, and the partial-gravity simulations must be as accurate and realistic as possible. Objectives: To highlight some of the key lessons learned about partial-gravity analogs and testing methodology, and to suggest considerations for optimizing the effectiveness and quality of results of future tests. Methods: Performance testing of suited and unsuited subjects was undertaken in different reduced-gravity analogs including the Space Vehicle Mockup Facility s Partial Gravity Simulator (POGO), parabolic flight on the C-9 aircraft, underwater environments including NASA s Extreme Environment Mission Operations (NEEMO) and the Neutral Buoyancy Lab (NBL), and in field analogs including Desert Research and Technology Studies (RATS), the Haughton Mars Project (HMP), and the JSC Rock Pile. Subjects performed level walking, incline/decline walking, running, shoveling, picking up and transferring rocks, kneeling/standing, and task boards. Lessons Learned Analogs: No single analog will properly simulate all aspects of the true partial-gravity environment. The POGO is an ideal environment from the standpoint that there are no time limits or significant volumetric constraints, but it does have several limitations. It allows only 2 translational degrees of freedom (DOF) and applies true partial-gravity offload only through the subject s center of gravity (CG). Also, when a subject is doing non-stationary tasks, significant overhead inertia from the lift column seems to have a negative impact on performance. Parabolic flight allows full translational and rotational DOF and applies offload to all parts of the body, but the simulation lasts less than 30 seconds. When this is coupled with the volumetric constraints of the plane, both task selection and data collection options are significantly limited. The underwater environments also allow all 6 DOF and allow off-loading to be applied throughout the body, but the data collection capabilities are limited to little more than subjective ratings. In addition, water drag negatively affects performance of tasks requiring dynamic motion. Field analogs provide the ability to simulate lunar terrain and more realistic mission-like objectives, but all of them operate at 1-g, so suited human performance testing generally must utilize a reduced-mass or "mockup" suit, depending on study objectives. In general, the ground-based overhead-suspension partial-gravity analogs like POGO allow the most diverse data collection methods possible while still simulating partial gravity. However, as currently designed, the POGO has significant limitations. Design of the Active Response Gravity Offload System (ARGOS) has begun and is focusing on adding full x,y,z translational DOF, improved offload accuracy, increased lift capacity, and active control of the x and y axes to minimize offload system inertia. Additionally, a new gimbal is being designed to reduce mass and inertia and to be able to work with different suits, as the current gimbal only supports suited testing with the Mark III Technology Demonstrator Suit (MKIII).

Norcross, Jason R.↗
NASA NTRS2014

Human Performance in Simulated Reduced Gravity Environments

NASA is currently designing a new space suit capable of working in deep space and on Mars. Designing a suit is very difficult and often requires trade‐offs between performance, cost, mass, and system complexity. Our current understanding of human performance in reduced gravity in a planetary environment (the moon or Mars) is limited to lunar observations, studies from the Apollo program, and recent suit tests conducted at JSC using reduced gravity simulators. This study will look at our most recent reduced gravity simulations performed on the new Active Response Gravity Offload System (ARGOS) compared to the C‐9 reduced gravity plane. Methods: Subjects ambulated in reduced gravity analogs to obtain a baseline for human performance. Subjects were tested in lunar gravity (1.6 m/sq s) and Earth gravity (9.8 m/sq s) in shirt‐sleeves. Subjects ambulated over ground at prescribed speeds on the ARGOS, but ambulated at a self‐selected speed on the C‐9 due to time limitations. Subjects on the ARGOS were given over 3 minutes to acclimate to the different conditions before data was collected. Nine healthy subjects were tested in the ARGOS (6 males, 3 females, 79.5 +/- 15.7 kg), while six subjects were tested on the C‐9 (6 males, 78.8 +/- 11.2 kg). Data was collected with an optical motion capture system (Vicon, Oxford, UK) and was analyzed using customized analysis scripts in BodyBuilder (Vicon, Oxford, UK) and MATLAB (MathWorks, Natick, MA, USA). Results: In all offloaded conditions, variation between subjects increased compared to 1‐g. Kinematics in the ARGOS at lunar gravity resembled earth gravity ambulation more closely than the C‐9 ambulation. Toe‐off occurred 10% earlier in both reduced gravity environments compared to earth gravity, shortening the stance phase. Likewise, ankle, knee, and hip angles remained consistently flexed and had reduced peaks compared to earth gravity. Ground reaction forces in lunar gravity (normalized to Earth body weight) were 0.4 +/- 0.2 on the ARGOS, but only 0.2 +/- 0.1 on the C‐9. Discussion: Gait analysis showed differences in joint kinematics and temporal‐spatial parameters between the reduced gravity simulators and with respect to earth gravity. Although most of the subjects chose a somewhat unique ambulation style as a result of learning to ambulate in a new environment, all but two were consistent with keeping an Earth‐like gait. Learning how reduced gravity affects ambulation will help NASA to determine optimal suit designs, influence mission planning, help train crew, and may shed light on the underlying methods the body uses to optimize gait for energetic efficiency. Conclusion: Kinematic and kinetic analysis demonstrated noteworthy differences between an offloaded environment and 1‐g, as would be expected. The analysis showed a trend to change the ambulation style in an offloaded environment to a rolling‐loping walk (resembling crosscountry skiing) with increased swing time. This ambulation modification, particularly in the ARGOS, indicated that the relative kinetic energy of the subject was increased, on average, per the static body weight compared to the 1‐g condition. How much of this was influenced by the active offloading of the ARGOS system is unknown.

Cowley, Matthew↗
NASA NTRSDate not supplied

The Instrumented Walking and Turning Test to Evaluate Suited Gait Dynamics and Performance in Extravehicular Activity Training Environments

Background and aims: Walking will be required for many exploration tasks on the Moon during the Artemis program. Walking in a straight line on the confined floorspace of a testing area, and repetitive treadmill walking that requires no change in direction may not adequately reflect the balance and coordination required during ambulation. Also, performance of turning maneuvers may be affected differently in different extravehicular activity (EVA) training facilities that simulate partial gravity. For example, the Neutral Buoyancy Lab (NBL) simulates lunar gravity by adding weight to underwater subjects to alter buoyancy and achieve the equivalent ground reaction force of 1/6 of Earth’s gravity (1/6G), whereas the Active Response Gravity Offload System (ARGOS) uses a computer controlled overhead suspension system programmed to continuously offload a percentage of a subject’s weight to simulate 1/6G. The degree to which dynamic movements such as turning are comparable across these EVA training facilities has not yet been evaluated. The instrumented gait test helps NASA scientists and engineers evaluate gait dynamics and performance in suited conditions, and this test demonstrates the unique characteristics and limitations of EVA training facilities. We developed an instrumented walking and turning test using inertial measurement units (IMUs) and conducted the test at NASA’s EVA training facilities. Results were used to compare suited walking and turning characteristics in the ARGOS and the NBL. Methods: Subjects donned the Mark III space suit during offloading with the ARGOS spreader bar gimbal and donned the Z2.5 space suit while underwater in the NBL with weights and floatation added to achieve realistic suit center of gravity. The test team securely attached three Opal (APDM, OR, USA) wireless IMUs on the space suit for each test run: one on the middle of the hard upper torso, and one on the left and on the right ankle bearings. During the NBL tests, the IMUs were encased in a waterproof housing (GoPro) with foam added to create a tighter fit. At both testing facilities, 6.3 m x 1.0 m (LxW) walking lines were marked, and a cone for turning or walking around was located at the end of the walking path with another line on the other side of the cone to indicate the stopping point after walking around the cone. Under simulated 1/6G, subjects began by standing at the marked line with their arms folded across the chest, they then walked at a preferred speed along the straight walking path until they reached the end, turned 180 degrees around the cone, and finally stopped at the marked stopping point. All IMU data recorded during testing were automatically saved to the internal memory. Then, raw IMU signals were processed using custom MATLAB (Mathworks, MA, USA) code to compare gait parameters during both the walking and the turning components of the task. These parameters included time (s), speed (m/s for walking and rad/s for turning), step number (n) and walk:turn time ratio (% time spent straight walking versus turning). Results: Less time, faster gait, fewer steps, and higher walk:turn ratio during both walking and turning components were exhibited during tests performed at the ARGOS versus those performed at the NBL. During the NBL tests, the slower walking speed continued at the same rate throughout a U-shape turn. During the ARGOS tests, the subjects performed shorter and tighter turns at 4 times the speed of the NBL turns because they walked 30% faster and the vertical offloading system gave them more support. Conclusion: Our data show that the differences in walking and turning parameters during the NBL tests may be due to the high viscosity in the water environment where the motion of the lower limbs was slow and did not reach full flexion and extension. These tests improve the current knowledge of testing environments in preparation for EVAs on the lunar surface.

Kyoung Jae Kim↗
NASA NTRS2023

NASA Space Environment Analog for Training, Engineering, Science, and Technology (SEATEST) 6 Detailed Final Report

After more than 50 years since the last crewed lunar landing, plans for more missions to the moon are in development. For these missions, efficient and sustainable logistics will be critical. Additionally, innovative methods of cargo transfer to and from a lunar outpost should be considered for successfully establishing a permanent presence on the moon. SEATEST (Space Environment Analog for Training, Engineering, Science, and Technology) is an immersive mission-analogous operational atmosphere where buoyancy effects and supplemental weights can simulate partial gravity conditions similar to those astronauts will experience on the moon. SEATEST 6 took place at the University of Southern California (USC) Wrigley Marine Science Center on Santa Catalina Island from July 18-30, 2023. The analog was used to collect preliminary logistics data on two different offloading conceptual methods (a davit and a zipline) during a simulated lunar mission. Pre-test analysis indicated for a crew of two on a 14-day mission, approximately three Medium Pressurized Logistics Containers (MPLC) sized logistics containers (or a total of 37.5 single Cargo Transfer Bag Equivalents (CTBE)) would be needed to support a mission. A Computer-Aided Design (CAD) analysis was employed on the SEATEST airlock mockup to determine how many logistic containers would fit with two suited crewmembers, don/doff stands, and hatch operations. It was determined that for SEATEST, a total of 15 1.0 Small Pressurized Logistics Containers (SPLCs) and 8 2.0 SPLCs would adequately fit into the approximate 9.5 cubic meter airlock volume. This does not fully represent a complete 14-day logistic supply; however, it does provide a preliminary estimate to initiate design conversations between logistics teams and crew at this early stage of development. Data were collected in eight logistics transfer scenarios over two days with four scenarios per day. Five test subject crew participated in scenarios as pairs. Scenarios included two sizes of logistics containers – 1.0 SPLC (equivalent to a single Cargo Transfer Bag (CTB) and 2.0 SPLC (equivalent to two CTBs). Planed evaluations included the use of a logistics port compared to transfer through an Airlock hatch, offloading methods based on either a davit or a zipline system, choreography of cargo in the airlock to permit ingress and suit doffing, and dust removal protocols for an understanding of the overall impact to transfer ops. Data collected included objective data (task times for conducting overall tasks and subtasks, full audio/video of test activities, and inadvertent “dings” on hardware) and subjective data (crew consensus of: task acceptability and capability assessment ratings related to best practices, considerations, and constraints for EVA-driven logistics transfer ConOps, sim quality of the test environment, and more general debrief comments). The two logistic offloading transfer concepts (davit, zipline) presented both advantages and limitations. The davit’s flexibility in allowing the crew to pick up the containers without physical interaction was well regarded by the crew. Some limitations of select davit hardware components were noted, but the overall concept was acceptable. The zipline system proved to be the most efficient way of moving logistics from the lander to the airlock and eliminated the need for dust operations. However, extended and repetitive lifting of containers to the line could be fatiguing. In conclusion, logistics transfer could hypothetically be achieved without an offloading method; however, the time requirement for such operations would be prohibitive. Results of crew subjective feedback proposed a combined or hybrid davit/zipline method to increase efficiency.

Logistics↗
NASA NTRSDate not supplied

Comparison of Physical Workload Across Eva-Simulation Analog Environments: Hybrid Space Suit Simulator and Pressurized Suit Testing

NASA conducts research, testing, and training across a variety of analog environments to support characterization of human performance during Extravehicular Activities (EVAs). Time utilizing pressurized suits in an offloaded environment is both limited and expensive, making it challenging to carry out extensive research with these suits. The Human Physiology, Performance, Protection, and Operations (H-3PO) Laboratory at NASA Johnson Space Center designed a Hybrid Space Suit Simulator (HS3) as a low-cost, workload approximator and easy access research tool to provide relevant physical and cognitive workloads during simulated EVAs. A pilot study was conducted in a 1g analog environment where six healthy subjects (3 male, 3 female) underwent simulated 5-hour EVAs in the HS3. This study used a COSMED K5 portable metabolic analyzer and a Polar H10 heart rate monitor to evaluate physical workload during the EVAs. For direct comparison, EVA tasks and timelines were modeled after a similar study conducted in pressurized suits (Mark-III spacesuit, n=3 male; small xPGS spacesuit, n=3 female) at NASA’s Active Response Gravity Offload System (ARGOS) offloaded to Lunar gravity (1/6 g). The 1g HS3 data demonstrated increased metabolic rate when compared to pressurized, Lunar-offloaded suited simulated EVAs for certain tasks including the kneeling scoop (HS3: 1194±199 BTU/hr, ARGOS: 860±170 BTU/hr, p < 0.05) and 20 lb object relocation (HS3: 1651±136 BTU/hr, ARGOS: 1048±252.8 BTU/hr, p < 0.05), but it did not demonstrate any significant differences in heart rate (p > 0.05). Other simulated EVA tasks, such as a 500 m traverse (HS3: 1431±177 BTU/hr, ARGOS: 1212±294 BTU/hr, p > 0.05), showed similar workload profiles with no significant workload differences between HS3 and ARGOS. The HS3 is a useful research tool for increasing workloads in EVA research without pressurized suits, though it may overestimate workload during certain EVA tasks when used in 1g environments.

Zachary Wusk↗
NASA NTRS1982

Design and feasibility study for a portable oil recovery turbopump

A portable oil recovery turbopump concept, using the Firefly module as primer mover, for the offloading of distressed tank vessels is examined. The demands to be met both in terms of the type of petroleum to be offloaded, as well as the operational requirements placed on the pump, are studied with respect to the capability of different pump configurations. Two configurations, one a centrifugal type and the other a screw type pump, are developed and evaluated. While the centrifugal configuration is found to be effective in a large proportion of tank vessel offloading situations, the screw type will be required where high viscosity cargoes are involved. The feasibility of the turbopump concept, with the Firefly module as prime mover, is established.

Source record↗
NASA NTRS1991

Acquisition and production of skilled behavior in dynamic decision-making tasks: Modeling strategic behavior in human-automation interaction: Why and aid can (and should) go unused

Advances in computer and control technology offer the opportunity for task-offload aiding in human-machine systems. A task-offload aid (e.g., an autopilot, an intelligent assistant) can be selectively engaged by the human operator to dynamically delegate tasks to an automated system. Successful design and performance prediction in such systems requires knowledge of the factors influencing the strategy the operator develops and uses for managing interaction with the task-offload aid. A model is presented that shows how such strategies can be predicted as a function of three task context properties (frequency and duration of secondary tasks and costs of delaying secondary tasks) and three aid design properties (aid engagement and disengagement times, aid performance relative to human performance). Sensitivity analysis indicates how each of these contextual and design factors affect the optimal aid aid usage strategy and attainable system performance. The model is applied to understanding human-automation interaction in laboratory experiments on human supervisory control behavior. The laboratory task allowed subjects freedom to determine strategies for using an autopilot in a dynamic, multi-task environment. Modeling results suggested that many subjects may indeed have been acting appropriately by not using the autopilot in the way its designers intended. Although autopilot function was technically sound, this aid was not designed with due regard to the overall task context in which it was placed. These results demonstrate the need for additional research on how people may strategically manage their own resources, as well as those provided by automation, in an effort to keep workload and performance at acceptable levels.

Kirlik, Alex↗
NASA NTRS1993

Modeling strategic behavior in human-automation interaction - Why an 'aid' can (and should) go unused

Task-offload aids (e.g., an autopilot, an 'intelligent' assistant) can be selectively engaged by the human operator to dynamically delegate tasks to automation. Introducing such aids eliminates some task demands but creates new ones associated with programming, engaging, and disengaging the aiding device via an interface. The burdens associated with managing automation can sometimes outweigh the potential benefits of automation to improved system performance. Aid design parameters and features of the overall multitask context combine to determine whether or not a task-offload aid will effectively support the operator. A modeling and sensitivity analysis approach is presented that identifies effective strategies for human-automation interaction as a function of three task-context parameters and three aid design parameters. The analysis and modeling approaches provide resources for predicting how a well-adapted operator will use a given task-offload aid, and for specifying aid design features that ensure that automation will provide effective operator support in a multitask environment.

Kirlik, Alex↗
NASA NTRS2014

Use of Semi-Autonomous Tools for ISS Commanding and Monitoring

As the International Space Station (ISS) has moved into a utilization phase, operations have shifted to become more ground-based with fewer mission control personnel monitoring and commanding multiple ISS systems. This shift to fewer people monitoring more systems has prompted use of semi-autonomous console tools in the ISS Mission Control Center (MCC) to help flight controllers command and monitor the ISS. These console tools perform routine operational procedures while keeping the human operator "in the loop" to monitor and intervene when off-nominal events arise. Two such tools, the Pre-positioned Load (PPL) Loader and Automatic Operators Recorder Manager (AutoORM), are used by the ISS Communications RF Onboard Networks Utilization Specialist (CRONUS) flight control position. CRONUS is responsible for simultaneously commanding and monitoring the ISS Command & Data Handling (C&DH) and Communications and Tracking (C&T) systems. PPL Loader is used to uplink small pieces of frequently changed software data tables, called PPLs, to ISS computers to support different ISS operations. In order to uplink a PPL, a data load command must be built that contains multiple user-input fields. Next, a multiple step commanding and verification procedure must be performed to enable an onboard computer for software uplink, uplink the PPL, verify the PPL has incorporated correctly, and disable the computer for software uplink. PPL Loader provides different levels of automation in both building and uplinking these commands. In its manual mode, PPL Loader automatically builds the PPL data load commands but allows the flight controller to verify and save the commands for future uplink. In its auto mode, PPL Loader automatically builds the PPL data load commands for flight controller verification, but automatically performs the PPL uplink procedure by sending commands and performing verification checks while notifying CRONUS of procedure step completion. If an off-nominal condition occurs during procedure execution, PPL Loader notifies CRONUS through popup messages, allowing CRONUS to examine the situation and choose an option of how PPL loader should proceed with the procedure. The use of PPL Loader to perform frequent, routine PPL uplinks offloads CRONUS to better monitor two ISS systems. It also reduces procedure performance time and decreases risk of command errors. AutoORM identifies ISS communication outage periods and builds commands to lock, playback, and unlock ISS Operations Recorder files. Operation Recorder files are circular buffer files of continually recorded ISS telemetry data. Sections of these files can be locked from further writing, be played back to capture telemetry data that occurred during an ISS loss of signal (LOS) period, and then be unlocked for future recording use. Downlinked Operation Recorder files are used by mission support teams for data analysis, especially if failures occur during LOS. The commands to lock, playback, and unlock Operations Recorder files are encompassed in three different operational procedures and contain multiple user-input fields. AutoORM provides different levels of automation for building and uplinking the commands to lock, playback, and unlock Operations Recorder files. In its automatic mode, AutoORM automatically detects ISS LOS periods, then generates and uplinks the commands to lock, playback, and unlock Operations Recorder files when MCC regains signal with ISS. AutoORM also features semi-autonomous and manual modes which integrate CRONUS more into the command verification and uplink process. AutoORMs ability to automatically detect ISS LOS periods and build the necessary commands to preserve, playback, and release recorded telemetry data greatly offloads CRONUS to perform more high-level cognitive tasks, such as mission planning and anomaly troubleshooting. Additionally, since Operations Recorder commands contain numerical time input fields which are tedious for a human to manually build, AutoORM's ability to automatically build commands reduces operational command errors. PPL Loader and AutoORM demonstrate principles of semi-autonomous operational tools that will benefit future space mission operations. Both tools employ different levels of automation to perform simple and routine procedures, thereby offloading human operators to perform higher-level cognitive tasks. Because both tools provide procedure execution status and highlight off-nominal indications, the flight controller is able to intervene during procedure execution if needed. Semi-autonomous tools and systems that can perform routine procedures, yet keep human operators informed of execution, will be essential in future long-duration missions where the onboard crew will be solely responsible for spacecraft monitoring and control.

Brzezinski, Amy S.↗