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Human sensory-motor interactions during performance of manual control tasks using high inertia tracking systems - optimum display and control systems for tracking missiles
The motion sensors of the vestibular system are studied to determine their role in human dynamic space orientation and manual vehicle control. The investigation yielded control models for the sensors, descriptions of the subsystems for eye stabilization, and demonstrations of the effects of motion cues on closed loop manual control. Experiments on the abilities of subjects to perceive a variety of linear motions provided data on the dynamic characteristics of the otoliths, the linear motion sensors. Angular acceleration threshold measurements supplemented knowledge of the semicircular canals, the angular motion sensors. Mathematical models are presented to describe the known control characteristics of the vestibular sensors, relating subjective perception of motion to objective motion of a vehicle. The vestibular system, the neck rotation proprioceptors and the visual system form part of the control system which maintains the eye stationary relative to a target or a reference. The contribution of each of these systems was identified through experiments involving head and body rotations about a vertical axis. Compensatory eye movements in response to neck rotation were demonstrated and their dynamic characteristics described by a lag-lead model. The eye motions attributable to neck rotations and vestibular stimulation obey superposition when both systems are active. Human operator compensatory tracking is investigated in simple vehicle orientation control system with stable and unstable controlled elements. Control of vehicle orientation to a reference is simulated in three modes: visual, motion and combined. Motion cues sensed by the vestibular system through tactile sensation enable the operator to generate more lead compensation than in fixed base simulation with only visual input. The tracking performance of the human in an unstable control system near the limits of controllability is shown to depend heavily upon the rate information provided by the vestibular sensors.
Space Policy Directive-1 has led to NASA partnerships with commercial entities on procurement which includes the development of the Human Landing System (HLS) [1]. With the goal of delivering human crew to the lunar surface by 2024, system uncertainties become an important obstacle to the maturation of multiple new, driving technologies and mission concepts of the HLS program. As unmitigated uncertainties have previously led to failed development programs, these risks and their impacts must be understood and handled to ensure program success [2]. Sources of uncertainty include novel engine designs and configurations, increased reliance on cryogenic fluid management(CFM), and refueling technologies—which propagate as high-level performance metrics such as overall propellant mass and engine performance. Also, the occurrence of operational uncertainties—e.g. launch conditions or need to abort during the mission—can cause cascading effects on the rest of the mission that are difficult to definitively quantify, and are outside the scope of control. These concrete examples and other occurrences can be categorized as either epistemic or aleatory uncertainties.Epistemic uncertainty arises due to a lack of knowledge and can be alleviated with design and program maturation. Aleatory uncertainty is due to the inherent randomness of the system and cannot be directly reduced, unlike epistemic uncertainty. Robust design and probabilistic methods can compensate for aleatory effects. A taxonomy of uncertainty is referred to for this work [3]. In this paper, a probabilistic methodology to handle uncertainties has been demonstrated on a three-element HLS concept [1, 4], which allows tracking of current best estimates of the concept and assessment of concept design robustness against uncertainties. A sample case has been completed for this abstract, and an expansion on the methodology will be included in the final paper. This methodology has two key parts: first, the creation of a dynamic architecture model of a three-element HLS concept; and second, its use with surrogate modeling and range estimating techniques to capture and propagate uncertainties. This abstract will cover the basics of the approach used, and further details and justifications will be in the final paper.The mission profile associated with this three-element concept (Fig 1) was modeled as a set of mission events that facilitated mass changes, idles, or spacecraft maneuvers. The mission profile scope starts with each element’s NRHO orbit insertion and aggregation and ends at post-sortie rendezvous with Orion. More detail on the mission profile will be in the final paper. The DYnamic Rocket EQuation Tool (DYREQT), a space systems synthesis and sizing framework used by NASA, was used as the physics framework to model the HLS architecture for applying the probabilistic methodology [5, 6]. Specifically, a parametric representation of the lander, ascent, and transfer elements and the mission profile of each element was established, with vehicle and mission parameters available as inputs to allow for a dynamic model. Each vehicle stage was modeled with high-level performance metrics, using Isp and propellant mass fraction (PMF) to remain parametric. For the probabilistic analysis, uncertainties of interest within the HLS concept were enumerated and represented as parameters within the DYREQT model as inputs for vehicle stages or mission profile events. These parameters were frozen at their nominal values for the purposes of baselining architecture performance and sizing the vehicle appropriately based on reference documentation [1]. Range estimating—a probabilistic method that combines Monte Carlo sampling, focus on critical parameters, and heuristics to assess risk and opportunities—is traditionally used with Mass Equipment Lists (MELs), but has been adapted with operational parameters as well as vehicle parameters in theDYREQT model to capture mission uncertainty alongside vehicle uncertainty [7, 3]. This method was selected due to its application and insight on a system from a bottom-up perspective, independence from historical rules of thumb, and ability to generate sensitivities based on design decisions and uncertainties. As a sample case for the abstract, the boiloff rates of the vehicle elements and the loiter times during the mission (simulating launch time variations and changing window of opportunities) were used with range estimating to provide preliminary results. To perform the range estimation portion of this methodology (depicted in Fig. 3, further details in final paper), the DYREQT model was sampled using a Design of Experiments (DoE) to efficiently explore the architecture design space with respect to the sample set of uncertainty parameters; 5,000 cases via Latin Hypercube Sampling were computed on the DYREQT architecture model. Then, the results were used to create surrogate models, multivariate regressions that can visualize hypercube trends in the design space, of the architecture with respect to the uncertainty parameters. Range estimating was applied to the surrogates instead of the actual models, which saves computational expense due to the bulk of cases needed for the Monte Carlo simulation as part of range estimating. Uncertainty parameters were sampled independently from triangular distributions using the DoE ranges as ‘min’ and ‘max’, and the nominal value as ‘most likely’. Based engineering intuition, some uncertainty parameters are correlated—e.g. if the main propellant has a high boil-off rate, the oxidizer should follow suit as both are related to CFM technology.While a Monte Carlo simulation samples all inputs as independent, the results would show model correlations; thus, it is efficient to sample the inputs as correlated. Using a correlation matrix constructed for the uncertainty parameters, previously independent samples were transformed to perform a Correlated Monte Carlo. A table for the DoE ranges and probability distribution parameters is shown in Table 1, and more details on Correlated Monte Carlo Simulations will be discussed in the final paper. The model’s resulting DoE showed that multivariate polynomial equations fit via least squares method captured its behavior accurately for the sample case. For the Correlated Monte Carlo Simulation, a positive correlation between fuel and oxidizer boiloff rates was used as a demonstration. 10,000 cases were computed with the surrogates and the launched masses for each vehicle element was collated. The results can be displayed in a probability density function (PDF), showing the impact of the uncertainty parameters chosen. Integrating the PDFs will yield a cumulative distribution function (CDF) that shows the cumulative probability of a given value on the x-axis. For the sample case, the elements’ launch mass margin was calculated and represented in as CDFs, as a demonstrated representation of figures of merit for the HLS concept. For the lander and ascent elements, the NRHO mass insertion limit is 16t; the transfer element has a limit of 30t [1]. It can be seen with Figure 2 that this probabilistic methodology can provide insight into mass margin with respect to the uncertainties being modeled. Currently, the results show that the lander (descent) vehicle element has the most restrictive design space; it is the only element to show a 10% probability of negative margin. Further analysis on the Monte Carlo results will show sensitivities for driving constraints and parameters for architecture feasibility, which can lead to establishing potential mission rules.The combination of range estimating with a parametric architecture model for HLS demonstrated the capability of this probabilistic methodology in a sample case. As the HLS development progresses, this methodology has the potential for keeping current best estimates of architecture performance for awarded concepts due to the flexibility in DYREQT’s modeling framework and its parametric nature. Concept maturation and increased epistemic knowledge can be injected into the model probabilistic modeling, and thus continue to track probability of mission success.
Exploration-class and International Space Station (ISS) missions have significantly different levels of associated medical risk for crewmembers. The Integrated Medical Model (IMM), the probabilistic risk assessment tool used for ISS medical trade-space analyses, uses a Functional Impairment (FI) metric to determine quality time lost should a crewmember be afflicted with a medical condition. While IMM-based FI has been successful for ISS operations, it has limitations when applied to exploration-class missions. As the National Aeronautics and Space Administration (NASA) looks ahead to Gateway, Artemis, and Martian missions, a novel, dynamic, and mission-appropriate impairment paradigm is necessary for accurate contingency planning. This paradigm, Task Impairment (TI), fulfills that need by utilizing mission-specific tasks. TI will allow future capability for a loss of mission metric, previously not available with IMM. TI serves as a replacement metric for FI within IMPACT, the next-generation trade-space analysis tool suite created to replace the IMM. IMPACT significantly increases the fidelity of probabilistic risk assessment for exploration-class missions. The Human Exploration of Mars Preliminary List of Crew Tasks (MTL, a source of 1100+ exploration-class mission-specific tasks for crewmembers) was used to calculate TI. Using the Task List, 18 individual Human System Categories were identified as being required to perform each task (e.g., Cardiopulmonary, Cognitive, etc.). Each of the 1200+ tasks were mapped to the Human System categories listed in the Task List. The total number of tasks in each category were tallied to determine how many tasks required each Human System. Lastly, each of the 120 medical conditions from the IMPACT condition list were mapped to the Human System categories to complete the TI calculation. NASA subject matter experts across five medical specialties established consensus for the mapping of each condition. The resulting total tasks impaired by each condition were used to calculate discrete TI values. This process was repeated for each of the four severity/resource utilization variants of each condition, and for each of the three phases of clinical care. Through this process, discrete TI values were calculated for each of the 120 IMPACT medical conditions and their variants. The Task Impairment metric provides higher fidelity, dynamic utility, and quicker analysis of medical impairment compared to the previous Functional Impairment metric used for the Integrated Medical Model. TI will be used for higher fidelity medical impairment analysis and contingency planning during Lunar, Martian, and other long-term exploration-class missions.
Crew factors include a wide range of concerns relating to the human system and its role in a Mars mission. There are two important areas which will play a large part in determining the crew for a Mars mission. The first relates to the goals and priorities determined for such a vast endeavor. The second is the design of the vehicle for the journey. The human system cannot be separated from the other systems in that vehicle. In fact it will be the human system which drives the development of many of the technical breakthroughs necessary to make a Mars mission successful. As much as possible, the engineering systems must adapt to the needs of the human system and its individual components.
For more than a decade, efforts have been ongoing at NASA’s Marshall Space Flight Center (MSFC) in Huntsville, Alabama, to land humans and cargo on planetary bodies like the Moon and Mars and today this work continues under the Center’s Lander Programs (LP) office. In August of 2019, NASA stood up the Human Landing System (HLS) program to be responsible for spacecrafts that will land astronauts on the Moon under Artemis. Work is well underway with the historic Artemis III mission to land the first Americans on the lunar surface in more than 50 years through the Appendix H Option A [1] contract with SpaceX. In November, NASA awarded SpaceX an Option B modification to its existing HLS Appendix H contract, which will further develop its Starship HLS to meet NASA’s sustaining lander requirements for lunar missions beyond Artemis III. In September of 2022, NASA issued an HLS Sustaining Lunar Development solicitation under the NextSTEP-2 Appendix P Broad Agency Announcement [2] asking a second provider, in addition to SpaceX, to develop and demonstrate a lander that meets the program’s extended set of requirements for missions beyond Artemis III. Under the umbrella of Sustaining Lunar Development [3], these requirements will meet NASA’s needs for recurring, long-term access to the lunar surface. Proposals were received from industry late last year and NASA is planning to award an Appendix P contract in the summer of 2023. This paper will provide an update of the Lander Program office’s progress and will discuss how the program is bridging from the initial demonstration phase of development for the Human Landing System program to the Sustaining Lunar Development phase. The paper will include publicly available information on SpaceX’s Starship HLS design as well as near-term and future milestones for HLS and the Artemis program.
Human exploration missions to Mars and other destinations beyond low Earth orbit require highly robust, reliable, and maintainable life support systems that maximize recycling of water and oxygen. In order to meet this requirement, NASA has continued the development of a Series-Bosch System, a two stage reactor process that reduces carbon dioxide (CO2) with hydrogen (H2) to produce water and solid carbon. Theoretically, the Bosch process can recover 100% of the oxygen (O2) from CO2 in the form of water, making it an attractive option for long duration missions. The Series Bosch system includes a reverse water gas shift (RWGS) reactor, a carbon formation reactor (CFR), an H2 extraction membrane, and a CO2 extraction membrane. In 2016, the results of integrated testing of the Series Bosch system showed great promise and resulted in design modifications to the CFR to further improve performance. This year, integrated testing was conducted with the modified reactor to evaluate its performance and compare it with the performance of the previous configuration. Additionally, a CFR with the capability to load new catalyst and remove spent catalyst in-situ was built. Flow demonstrations were performed to evaluate both the catalyst loading and removal process and the hardware performance. The results of the integrated testing with the modified CFR as well as the flow demonstrations are discussed in this paper.
Human exploration missions to Mars and other destinations beyond low Earth orbit require highly robust, reliable, and maintainable life support systems that maximize recycling of water and oxygen. In order to meet this requirement, NASA has continued the development of a Series-Bosch System, a two stage reactor process that reduces carbon dioxide (CO2) with hydrogen (H2) to produce water and solid carbon. Theoretically, the Bosch process can recover 100% of the oxygen (O2) from CO2 in the form of water, making it an attractive option for long duration missions. The Series Bosch system includes a reverse water gas shift (RWGS) reactor, a carbon formation reactor (CFR), an H2 extraction membrane, and a CO2 extraction membrane. In 2016, the results of integrated testing of the Series Bosch system showed great promise and resulted in design modifications to the CFR to further improve performance. This year, integrated testing was conducted with the modified reactor to evaluate its performance and compare it with the performance of the previous configuration. Additionally, a CFR with the capability to load new catalyst and remove spent catalyst in-situ was built. Flow demonstrations were performed to evaluate both the catalyst loading and removal process and the hardware performance. The results of the integrated testing with the modified CFR as well as the flow demonstrations are discussed in this paper.
Human motion tracking systems represent a crucial technology in the area of modeling and simulation. These systems, which allow engineers to capture human motion for study or replication in virtual environments, have broad applications in several research disciplines including human engineering, robotics, and psychology. These systems are based on several sensing paradigms, including electro-magnetic, infrared, and visual recognition. Each of these paradigms requires specialized environments and hardware configurations to optimize performance of the human motion tracking system. Ideally, these systems are used in a laboratory or other facility that was designed to accommodate the particular sensing technology. For example, electromagnetic systems are highly vulnerable to interference from metallic objects, and should be used in a specialized lab free of metal components.
This document contains five papers on coping with ice formation during the operation of aircraft. The first paper uses a pilot survey to identify the desired attributes of future information systems for aircraft icing. The second paper investigates the influence of potential remote ice-detection system features on pilot decision making. The third paper investigates the effectiveness of aviation weather forecasting along aircraft trajectories. The fourth paper applied a human-centered systems analysis to the adverse aircraft weather encounter problem in order to identify desirable functions of weather and icing information. The objective of the fifth paper, a viewgraph presentation, is to propose means to improve aviation weather information, training, and procedures based on a human-centered systems approach.
This paper presents an integrated reliability and physics-based risk modeling approach for assessing human spaceflight systems. The approach is demonstrated using an example, end-to-end risk assessment of a generic-crewed space transportation system during a reference mission to the International Space Station. The behavior of the system is modeled using analysis techniques from multiple disciplines in order to properly capture the dynamic time- and state- dependent consequences of failures encountered in different mission phases. We discuss how to combine traditional reliability analyses with Monte Carlo simulation methods and physics-based engineering models to produce loss-of- mission and loss-of-crew risk estimates supporting risk-based decision-making and requirement verification. This approach facilitates risk-informed design by providing more realistic representation of system failures and interactions; identifying key risk-driving sensitivities, dependencies, and assumptions; and tracking multiple figures of merit within a single, responsive assessment framework that can readily incorporate evolving design information throughout system development.
Human Engineering in many projects is at best a limited support function. In this Navy project the Human Engineering function is an integral component of the systems design and development process. Human Engineering is a member of the systems design organization. This ensures that people considerations are: (1) identified early in the project; (2) accounted for in the specifications; (3) incorporated into the design; and (4) the tested product meets the needs and expectations of the people while meeting the overall systems requirements. The project exemplifies achievements that can be made by the symbiosis between systems designers, engineers and Human Engineering. This approach increases Human Engineering's effectiveness and value to a project because it becomes an accepted, contributing team member. It is an approach to doing Human Engineering that should be considered for most projects. The functional and organizational issues giving this approach strength are described.
Space exploration missions will require combining human and technical subsystems into overall "crew systems" capable of performing under the rigorous conditions of outer space. This report describes substantive and conceptual relationships among humans, intelligent machines, and communication systems, and explores how these components may be combined to complement and strengthen one another. We identify key research issues in the combination of humans and technology and examine the role of individual differences, group processes, and environmental conditions. We conclude that a crew system is, in effect, a social cyborg, a living system consisting of multiple individuals whose capabilities are extended by advanced technology.
In preparation of the NextSTEP-2 Appendix H awards, NASA’s Human Landing System (HLS) Program (HLSP) went through an abbreviated Design Analysis Cycle (DAC), or miniDAC, to characterize and quantify how a storable propellant design would affect and improve overall system performance of the Government reference design. As part of that miniDAC, HLSP ran multiple trade studies and assessments, including aspects of Mission Design, Flight Mechanics (FM), and Guidance, Navigation, and Control (GN&C). The storable propellant designs were assessed through the baseline mission targeting the Near Rectilinear Halo Orbit (NRHO) and several alternate staging orbits. This paper focuses on the FM and GN&C trades and assessments completed as part of the miniDAC as well as the GN\&C elements of the miniDAC Master Equipment List (MEL) developed for the storable propellant lander configurations.
Physical properties of labyrinth fluids in human vestibular system
The complexity of cause-and-effect relationships between spaceflight hazards and resulting health conditions clouds understanding of the totality of human system risk in space. In response, NASA has introduced Directed Acyclic Graphs (causal diagrams) into the human systems risk management process. These diagrams allow for a common understanding of the mechanisms that lead from unique hazards of spaceflight to the health outcomes important to agencies and astronauts. However, the paucity of available biomedical data from spaceflight creates a need for methods of validating causal models that can accommodate data from spaceflight model analogs. Here we outline one approach utilizing open-access rodent bone datasets from the Ames Life Sciences Data Archive. The properties of directed acyclic graphs themselves can provide an epistemological and statistical framework for validation of a priori causal representations of human system risk in space flight. The assumed causal connections on the graph creates sets of logical implications: variables that – if the causal diagram is correct – should be correlated, as well as sets that should be conditionally independent. By testing these implied correlations and conditional independencies both statistically and heuristically, we can provide evidence for or against specific causal pathways on the causal diagram. In addition to validation of expert-generated causal diagrams, machine learning techniques can learn the most likely structure of a causal diagram from a given dataset. Comparison with and reconciliation between machine-learned causal diagrams and expert-generated diagrams is another technique for challenging assumptions and improving our understanding of causal mechanisms. Accurately representing complex causation is essential to systemic understanding of human health risks in space travel. Having a robust system of validating causal diagrams helps us arrive at more accurate representations of causal systems. This process will be integral to developing the countermeasures necessary for extended exploration of the moon and Mars.
This paper deals with the short-term response of the human cardiovascular system to orthostatic stresses in the context of developing a mathematical model of the overall system. It discusses the physiological issues involved and how these issues have been handled in published cardiovascular models for simulation of orthostatic response. Most of the models are stimulus specific with no demonstrated capability for simulating the responses to orthostatic stimuli of different types. A comprehensive model incorporating all known phenomena related to cardiovascular regulation would greatly help to interpret the various orthostatic responses of the system in a consistent manner and to understand the interactions among its elements. This paper provides a framework for future efforts in mathematical modeling of the entire cardiovascular system.
This paper summarizes the past and present successes of the Habitability and Human Factors Branch (HHFB) at NASA Johnson Space Center s Space Life Sciences Directorate (SLSD) in including the Human-As-A-System (HAAS) model in many NASA programs and what steps to be taken to integrate the Human-Centered Design Philosophy (HCDP) into NASA s Systems Engineering (SE) process. The HAAS model stresses systems are ultimately designed for the humans; the humans should therefore be considered as a system within the systems. Therefore, the model places strong emphasis on human factors engineering. Since 1987, the HHFB has been engaging with many major NASA programs with much success. The HHFB helped create the NASA Standard 3000 (a human factors engineering practice guide) and the Human Systems Integration Requirements document. These efforts resulted in the HAAS model being included in many NASA programs. As an example, the HAAS model has been successfully introduced into the programmatic and systems engineering structures of the International Space Station Program (ISSP). Success in the ISSP caused other NASA programs to recognize the importance of the HAAS concept. Also due to this success, the HHFB helped update NASA s Systems Engineering Handbook in December 2007 to include HAAS as a recommended practice. Nonetheless, the HAAS model has yet to become an integral part of the NASA SE process. Besides continuing in integrating HAAS into current and future NASA programs, the HHFB will investigate incorporating the Human-Centered Design Philosophy (HCDP) into the NASA SE Handbook. The HCDP goes further than the HAAS model by emphasizing a holistic and iterative human-centered systems design concept.