Results for “complex task performance” — Page 7

NASA NTRSDate not supplied

The Cognition Battery: Developing a Normative Database for Spaceflight and Examining the Impact of Prolonged Isolation and Confinement on Cognitive Performance

INTRODUCTION: Astronauts on future long duration space exploration will be required to execute complex tasks in which even minor errors could have devastating consequences. Intact cognition is critical to maintain exceptional performance standards and it is possible that variability even at the highest ends of the performance spectrum will impact operational tasks. Exposure to spaceflight hazards could compromise cognitive performance, and decrements have been documented under conditions of altered gravity and radiation exposure. The cognitive impacts of prolonged isolation and confinement remain relatively unknown. Traditional neuropsychological assessments cannot support earth independent monitoring, do not have normative comparisons for high functioning individuals, and most are not sensitive enough to detect small performance decrements. The Cognition Battery was developed to address these limitations but currently lacks comprehensive normative data. In this project, we aimed to 1) develop a preliminary normative database for astronauts and astronaut surrogates using the Cognition Battery, and 2) characterize differences in cognitive performance after short and long duration analog missions. PARTICIPANTS & METHODS: We assessed baseline cognitive performance by administering the Cognition Battery to 97 astronaut and astronaut surrogates recruited to approximate astronaut demographics (mean age: 39.38, SD=7.62; 35.1% female; 91.7% advanced degrees). For aim 1, we calculated speed and accuracy outcomes for each subtest and summarized the data with descriptive statistics. We examined the relationship between age and performance with Pearsons’s correlations and the relationship between gender and performance with independent samples t-tests. A subset of individuals on short duration (n=48; mean age: 38.18, SD=7.14; 37.5% female; 91.7% advanced degrees) missions of 45 days and long duration (n=34; mean age: 42.12, SD=8.39; 38.2% female; 94.1% advanced degrees) missions ranging from 4-8 months were administered the Cognition Battery during and after their respective missions. For aim 2, we accounted for practice effects using published corrections, and z-transformed post-mission scores using the full sample’s baseline scores. One-way analysis of covariance tests determined the main effect of analog duration on post-mission performance, after accounting for age and gender. The False Discovery Rate method was applied to adjust for multiple comparisons. RESULTS: In the full normative sample, older age was associated with slower reaction times on a processing speed task (r=.39, p=.02), and men were more accurate on processing speed (t=-2.19, p=.03) and faster on sustained attention (t=2.02, p=.049) and risk-taking tasks (t=2.49, p=.02). We observed a main effect of duration on performance on tasks of visual memory (f(1)=5.62, p=.04), processing speed (f(1)=5.48, p=.03), and sensorimotor functioning (f(1)=39.52, p < .001), such that slower performance was observed after long duration missions relative to short. Only sensorimotor functioning was significant after adjustments for multiple comparisons (adjusted p<.001). DISCUSSION: The full sample represents the largest dataset of cognitive performance assessed by the Cognition Battery available and can be used for further research in spaceflight and high-performance populations. Relationships between performance and key demographic variables suggest future research that include age and gender stratification is needed. Our results also show minimal changes in cognitive performance between longer and shorter periods of isolation and confinement.

S I Dev↗

NASA NTRS1984

Intelligent interfaces for tactical airborne platforms

Enhanced capabilities of tactical airborne platforms have resulted in increased number of aircrew tasks, greater task complexity, and increased time-stress in task performance. Embedded intelligence in the aircrew-vehicle interface (AVI) can help alleviate aircrew workload and enhance aircrew performance by: (1) optimizing the exchange of information between the aircrew and the onboard automation; and (2) adaptively allocating functions between aircrew and automation in response to situational demands. Intelligent interface issues are addressed in this report such as: (1) how to ensure that the aircrew can cope with the information influx; (2) how to present/portray both situational and internal status information; (3) how to allocate functions between the aircrew and the onboard automation; and (4) how to explain reasoning processes employed by onboard intelligence to the aircrew.

Madni, A.↗

NASA NTRS1987

Trends in Space Station telemetry applications

Spacecraft telemetry systems have evolved from simple hardware devices to complex computer applications performing data acquisition and formatting tasks. This paper reviews the role of spacecraft computers in performing telemetry functions and examines computer-based telemetry systems being considered for use on the NASA Space Station.

Muratore, John F.↗

NASA NTRS2010

Handling Qualities of a Capsule Spacecraft During Atmospheric Entry

A piloted simulation was conducted to study handling qualities for capsule spacecraft entering the Earth s atmosphere. Eight evaluation pilots, including six pilot astronauts, provided Cooper-Harper ratings, workload ratings, and qualitative comments. The simulation began after descending through the atmospheric entry interface point and continued until the drogue parachutes deployed. There were two categories of piloting tasks, both of which required bank angle control. In one task category, the pilot followed a closed-loop bank angle command computed by the backup guidance system to manage g-loads during entry. In the other task category, the pilot used intuitive rules to determine the desired bank angle independently, based on an open-loop schedule of vertical speed, Mach, and total energy specified at several range-to-target gates along the entry trajectory. Pilots were able to accurately track the bank angle guidance commands and steered the capsule toward the recovery site with essentially the same range error as the benchmark autopilot trajectory albeit with substantially higher propellant usage, and the handling qualities for this task were satisfactory. Another key result was that the complex piloting task of atmospheric entry could be performed satisfactorily, even in the presence of large dispersions, by controlling bank angle to follow a simple open-loop schedule.

Bilimoria, Karl D.↗

NASA NTRS2005

GO/NO-GO - When is medical hazard mitigation acceptable for launch?

Medical support of spaceflight missions is composed of complex tasks and decisions that dedicated to maintaining the health and performance of the crew and the completion of mission objectives. Spacecraft represent one of the most complex vehicles built by humans, and are built to very rigorous design specifications. In the course of a Flight Readiness Review (FRR) or a mission itself, the flight surgeon must be able to understand the impact of hazards and risks that may not be completely mitigated by design alone. Some hazards are not mitigated because they are never actually identified. When a hazard is identified, it must be reduced or waivered. Hazards that cannot be designed out of the vehicle or mission, are usually mitigated through other means to bring the residual risk to an acceptable level. This is possible in most engineered systems because failure modes are usually predictable and analysis can include taking these systems to failure. Medical support of space missions is complicated by the inability of flight surgeons to provide "exact" hazard and risk numbers to the NASA engineering community. Taking humans to failure is not an option. Furthermore, medical dogma is mostly comprised of "medical prevention" strategies that mitigate risk by examining the behaviour of a cohort of humans similar to astronauts. Unfortunately, this approach does not lend itself well for predicting the effect of a hazard in the unique environment of space. This presentation will discuss how Medical Operations uses an evidence-based approach to decide if hazard mitigation strategies are adequate to reduce mission risk to acceptable levels. Case studies to be discussed will include: 1. Risk of electrocution risk during EVA 2. Risk of cardiac event risk during long and short duration missions 3. Degraded cabin environmental monitoring on the ISS. Learning Objectives 1.) The audience will understand the challenges of mitigating medical risk caused by nominal and off-nominal mission events. 2.) The audience will understand the process by which medical hazards are identified and mitigated before launch. 3.) The audience will understand the roles and responsibilities of all the other flight control positions in participating in the process of reducing hazards and reducing medical risk to an acceptable level.

Hamilton, Douglas R.↗

NASA NTRS2015

Activity Planning for a Lunar Orbital Mission

This paper describes a challenging, real-world planning problem within the context of a NASA mission called LADEE (Lunar Atmospheric Dust Environment Explorer). LADEEs science phase was performed in an equatorial, retrograde orbit around the Moon. The science observations were constrained with respect to key points in the spacecrafts orbit. We present the approach taken to reduce the complexity of the activity planning task in order to effectively perform it within the time pressures imposed by the mission requirements. One key aspect of this approach is the design of the activity planning process based on principles of problem decomposition and planning abstraction levels. The second key aspect is the mixed-initiative system developed for this task, called LASS (LADEE Activity Scheduling System). The primary challenge for LASS was representing and managing the orbit-based science constraints, given their dynamic nature due to the continually updated orbit determination solution.

Bresina, John L.↗

NASA NTRS1987

A task-based metric for telerobotic performance assessment

A methodology is described for developing a task complexity index based on combining the six basic motion primitives (three translation, three orientation) with force control and accuracy requirements. The result of this development is a set of complexity values that can be assigned to the high-level task primitives derived from a relatively shallow top-down mission analysis. These values are then averaged to arrive at total average mission complexities, such as for the mission of exchanging the Hubble Space Telescope (HST) battery modules. Application of this metric to a candidate set of NASA Flight Telerobotic Servicer evaluation tasks is discussed using the HST battery module mission for an in-depth example.

Barnes, J. F.↗

NASA NTRS2015

Understanding Community Needs and Desires

This panel exchange is focused on the Integration Test phase within the development lifecycle with special emphasis and dedicated discussion on integration and testing of complex systems. Layout and scheduling of integration tasks come from the verification of interface and performance requirements. However, planning for integration activities of complex systems is inherently different from traditional systems engineering integration planning activities. Decisions about the systems under development have to consider not only the technical and programmatic viewpoints but also the political, societal, operational, and economic viewpoints. Definition of performance measures, found intrinsic in the plan, with trans-disciplinary implications will be discussed. A scenario of integration of UAS in the NAS will be used as a benchmark of current views and lifecycle challenges.

Murphy, James R.↗

NASA NTRSDate not supplied

Validation of Fitness for Duty Standards Using Pre- and Post-Flight Capsule Egress and Suited Functional Performance Tasks in Simulated Reduced Gravity

The transition between gravity environments will involve one of the most complex, high-risk phases of exploration missions. The reduced functional capacity caused by physiological deconditioning adaptations in microgravity coupled with the stressors of re-entry into partial gravity environments will increase risks to crew, even with rigorous adherence to inflight countermeasures. Specifically, two high-risk scenarios may be required to be performed soon after gravity transitions: 1) nominal and/or emergency unassisted capsule egress task after return to Earth, and 2) planetary extravehicular activity (EVA) soon after landing on Mars or the Moon. Quantification of crewmember’s functional performance after long-duration spaceflight is necessary to inform concepts of operations for future exploration missions. The overarching aim of this study is to quantify post-landing functional performance with deconditioning after long-duration ISS missions. This study is broken down into two phases. Phase 1 includes a pilot study to assess the overall feasibility and demonstrate the capability to perform mission-like tasks shortly after landing. Phase 2, the Egress Fitness study, which is part of the Complement of Integrated Protocols for Human Exploration Research (CIPHER), uses a task-based approach to characterize functional performance in long-duration ISS crewmembers before flight and shortly after return to Earth. The pilot and full Egress Fitness study includes pre-flight and post-flight testing of simulated emergency egress out of a functional capsule mockup and a Mars gravity EVA simulation at the Active Response Gravity Offload System (ARGOS) facility. The EVA simulation tasks include suit donning, hatch egress, ladder descent, task board cable operations, baggage transfer over sand/rocky regolith, alignment with a rear entry port, and suit egress. The post-flight simulated capsule egress test occurs 1–4 h after landing and the planetary EVA simulation occurs 18–36 h after landing. The full CIPHER Egress Fitness study has additional pre-flight sessions, longer EVA tasks that include traverse and geology sampling, and post-flight sessions on R+1, 4, and 8 to characterize the timeframe of recovery. The Pilot Egress Fitness study has completed baseline and post-flight testing on four crewmembers. All subjects were able to complete the post-flight simulated planetary EVA; three subjects were able to complete the postflight capsule egress simulation. CIPHER study data collection is ongoing. This study will quantify post-landing functional performance in operationally relevant simulations to help inform future planetary concepts of operations shortly after landing.

egress↗

NASA NTRSDate not supplied

Validation of Fitness for Duty Standards Using Pre- and Post-Flight Capsule Egress and Suited Functional Performance Tasks in Simulated Reduced Gravity

The transition between gravity environments will involve one of the most complex, high-risk phases of any mission. For example, the reduced functional capacity caused by physiological deconditioning adaptations in microgravity coupled with the stressors of re-entry into partial gravity environments will increase risks to crew, even with rigorous adherence to inflight countermeasures. Quantification of the astronauts’ post-landing functional performance is necessary to design ConOps for exploration missions. Specifically, these two high-risk scenarios may be required to be performed soon after gravity transitions: • Nominal and/or emergency unassisted capsule egress task after return to Earth • Planetary extravehicular activity (EVA) soon after landing on Mars or the Moon This study is broken down into two phases. Phase 1: A feasibility study that will assess the overall feasibility and demonstrate the capability to do these tasks shortly after landing. Phase 2: the full Egress Fitness study, which is part of the CIPHER complement. This study uses a task-based approach to characterize functional performance of these high-risk scenarios in long-duration ISS crewmembers before flight and shortly after return to Earth. Prior to any testing, each astronaut subject completes a suit fit check to ensure adequate sizing and a mobility assessment to confirm completion of the EVA tasks. Pilot Egress Fitness pre-flight and post-flight testing includes an Earth-based emergency egress out of a functional capsule mockup and a short Mars gravity EVA simulation including suit donning, hatch egress, ladder descent, task board cable operations, baggage transfer over sand/rocky regolith, alignment with a rear entry port, and suit egress. Pre-flight testing will occur at any time point prior to flight. Post-flight assessment is much more critical with the capsule egress test that will occur 1–4 hours after landing and the planetary EVA approximately 18–36 hours after landing. The CIPHER study will incorporate additional pre-flight sessions, longer EVA tasks that include traverse and geology sampling, and post-flight sessions on R+1, 4, and 8 to characterize the timeframe of recovery. Data collected for both tasks include task completion time, photo, and video. The EVA portion also includes collection of metabolic and heart rate. Pilot Egress Fitness study has completed both baseline and post-flight testing on four astronaut subjects, with all four able to complete post-flight EVA testing and three able to complete postflight capsule egress testing. Preliminary results observe individual physiological variations, which were to be expected but also suggest the need to carefully track the timeline from undock to landing to testing. Furthermore, some task performance instructions and equipment may need to be adjusted to ensure results are primarily physiological. These and other lessons learned will be addressed with minor protocol changes in the full CIPHER Egress Fitness study.

J R Norcross↗

NASA NTRS2004

Balanced Expertise Distribution in Remote Ultrasound Imaging Aboard The International Space Station (ISS)

Astronaut training for ISS operations usually ensures independent performance. With small crew size same crews also conduct all science work onboard. With diverse backgrounds, a good "match" between the existing and required skills can only be anecdotal. Furthermore, full proficiency in most of the complex tasks can be attained only through long training and practice, which may not be justified and may be impossible given the scarcity of training time. To enable a number of operational and science advancements, authors have developed a new approach to expertise distribution in time and among the space and ground personnel. Methods: As part of NASA Operational Ultrasound Project (1998-2003) and the NASA-solicited experiment "Advanced Diagnostic Ultrasound in Microgravity-ADUM" (P.I. -S.D., ongoing), the authors have created a "Balanced Expertise Distribution" approach to perform complex ultrasound imaging tasks on ISS for both operational and science use. The four components of expertise are a) any pre-existing pertinent expertise; b) limited preflight training c) adaptive onboard proficiency enhancement tools; d) real-time ' guidance from the ground. Throughout the pre-flight training and flight time preceding the experiments, the four components are shaped in a dynamic fashion to meet in an optimum combination during the experiment sessions. Results: Procedure validation sessions and feasibility studies have given encouraging results. While several successful real-time remote guidance sessions have been conducted on ISS, Expedition 8 is the first to use an "on-orbit proficiency enhancement" tool. Conclusions: In spite of severely limited training time, daring peer-reviewed research and operational enhancements are feasible through a balanced distribution of expertise in time, as well as among the crewmembers and ground personnel. This approach shows great promise for biomedical research, but may be applicable for other areas of micro gravity-based science

Sargsyan, Ashot↗

NASA NTRS2007

Filling the Assurance Gap on Complex Electronics

Many of the methods used to develop software bare a close resemblance to Complex Electronics (CE) development. CE are now programmed to perform tasks that were previously handled by software, such as communication protocols. For example, the James Webb Space Telescope will use Field Programmable Gate Arrays (FPGAs), which can have over a million logic gates, to send telemetry. System-on-chip (SoC) devices, another type of complex electronics, can combine a microprocessor, input and output channels, and sometimes an FPGA for programmability. With this increased intricacy, the possibility of software-like bugs such as incorrect design, logic, and unexpected interactions within the logic is great. Since CE devices are obscuring the hardware/software boundary, mature software methodologies have been proposed, with slight modifications, to develop these devices. By using standardized S/W Engineering methods such as checklists, missing requirements and bugs can be detected earlier in the development cycle, thus creating a development process for CE that can be easily maintained and configurable based on the device used.

Plastow, Richard A.↗

NASA NTRS1967

A review of quasi-linear pilot models.

Review of quasilinear pilot models

TASK COMPLEXITY↗

NASA NTRS1992

Fault detection and fault tolerance in robotics

Robots are used in inaccessible or hazardous environments in order to alleviate some of the time, cost and risk involved in preparing men to endure these conditions. In order to perform their expected tasks, the robots are often quite complex, thus increasing their potential for failures. If men must be sent into these environments to repair each component failure in the robot, the advantages of using the robot are quickly lost. Fault tolerant robots are needed which can effectively cope with failures and continue their tasks until repairs can be realistically scheduled. Before fault tolerant capabilities can be created, methods of detecting and pinpointing failures must be perfected. This paper develops a basic fault tree analysis of a robot in order to obtain a better understanding of where failures can occur and how they contribute to other failures in the robot. The resulting failure flow chart can also be used to analyze the resiliency of the robot in the presence of specific faults. By simulating robot failures and fault detection schemes, the problems involved in detecting failures for robots are explored in more depth.

Visinsky, Monica↗

NASA NTRS1995

An investigation of bleed configurations and their effect on shock wave/boundary layer interactions

The design of high efficiency supersonic inlets is a complex task involving the optimization of a number of performance parameters such as pressure recovery, spillage, drag, and exit distortion profile, over the flight Mach number range. Computational techniques must be capable of accurately simulating the physics of shock/boundary layer interactions, secondary corner flows, flow separation, and bleed if they are to be useful in the design. In particular, bleed and flow separation, play an important role in inlet unstart, and the associated pressure oscillations. Numerical simulations were conducted to investigate some of the basic physical phenomena associated with bleed in oblique shock wave boundary layer interactions that affect the inlet performance.

Hamed, Awatef↗

NASA NTRSDate not supplied

Discovery and Analysis of Rare High-Impact Failure Modes using Adversarial RL-Informed Sampling

Adaptive learning agents have tremendous potential to handle critical tasks currently performed by humans. Unfortunately, due to their complexity, it can be difficult to verify that these learning agents do not have critical failure modes. Standard verification and validation methods often do not apply directly to learning agents and Monte Carlo methods have difficulty covering even a small fraction of the state space, especially in multiagent systems or over long time horizons. To overcome this difficulty, we demonstrate an adaptive stress-testing method based on reinforcement learning of correlations that raise the probability of failure. This approach has three key properties: (1) it is able to find rare failure modes with far greater sample efficiency than Monte Carlo methods, (2) it can estimate the true probability of a failure mode despite the inherent bias in the learning method, and (3) it is capable of learning and resampling compact representations of multimodal failure spaces. These properties are important in practice as we need to find disparate failure modes while accounting for their actual relevance. This is a significant advantage over traditional adaptive stress testing methods that give abstract likelihoods of particular failure instances, but cannot estimate the probability of a broader failure mode. We test our algorithm on a simple problem from the aviation domain where an autonomous aircraft lands in gusty wind conditions. The results suggest that we can find failure modes with far fewer samples than the Monte Carlo approach and simultaneously estimate the probability of failure.

reinforcement learning↗

NASA NTRSDate not supplied

Discovery and Analysis of Rare High-Impact Failure Modes using Adversarial RL-Informed Sampling

Adaptive learning agents have tremendous potential to handle critical tasks currently performed by humans. Unfortunately, due to their complexity, it can be difficult to verify that these learning agents do not have critical failure modes. Standard verification and validation methods often do not apply directly to learning agents and Monte Carlo methods have difficulty covering even a small fraction of the state space, especially in multiagent systems or over long time horizons. To overcome this difficulty, we demonstrate an adaptive stress-testing method based on reinforcement learning of correlations that raise the probability of failure. This approach has three key properties: (1) it is able to find rare failure modes with far greater sample efficiency than Monte Carlo methods, (2) it can estimate the true probability of a failure mode despite the inherent bias in the learning method, and (3) it is capable of learning and resampling compact representations of multimodal failure spaces. These properties are important in practice as we need to find disparate failure modes while accounting for their actual relevance. This is a significant advantage over traditional adaptive stress testing methods that give abstract likelihoods of particular failure instances, but cannot estimate the probability of a broader failure mode. We test our algorithm on a simple problem from the aviation domain where an autonomous aircraft lands in gusty wind conditions. The results suggest that we can find failure modes with far fewer samples than the Monte Carlo approach and simultaneously estimate the probability of failure.

Validation↗

NASA NTRSDate not supplied

Discovery and Analysis of Rare High-Impact Failure Modes using Adversarial RL-Informed Sampling

Adaptive learning agents have tremendous potential to handle critical tasks currently performed by humans. Unfortunately, due to their complexity, it can be difficult to verify that these learning agents do not have critical failure modes. Standard verification and validation methods often do not apply directly to learning agents and Monte Carlo methods have difficulty covering even a small fraction of the state space, especially in multiagent systems or over long time horizons. To overcome this difficulty, we demonstrate an adaptive stress-testing method based on reinforcement learning of correlations that raise the probability of failure. This approach has three key properties: (1) it is able to find rare failure modes with far greater sample efficiency than Monte Carlo methods, (2) it can estimate the true probability of a failure mode despite the inherent bias in the learning method, and (3) it is capable of learning and resampling compact representations of multimodal failure spaces. These properties are important in practice as we need to find disparate failure modes while accounting for their actual relevance. This is a significant advantage over traditional adaptive stress testing methods that give abstract likelihoods of particular failure instances, but cannot estimate the probability of a broader failure mode. We test our algorithm on a simple problem from the aviation domain where an autonomous aircraft lands in gusty wind conditions. The results suggest that we can find failure modes with far fewer samples than the Monte Carlo approach and simultaneously estimate the probability of failure.

Validation↗