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Daniel Guggenheim School of Aerospace Engineering
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ItemDecision Making with Incomplete Information Dataset(Georgia Institute of Technology, 2014-07-09) Feigh, Karen M. ; Canellas, Marc ; Chua, Zarrin K.Decision makers are often required to make decisions with incomplete information. In order to design decision support systems (DSSs) utilizing restrictiveness and guidance to assist decision makers in these situations, it is essential to understand how certain decision making strategies are affected by incomplete information. This paper presents the results of a simulation measuring the accuracy and effort of take-the-best (TTB) and Tallying alongside two normative-rational decision making strategies, weighted-additive (WADD) and equal-weighting (EW) in scenarios with varying levels of total information, information imbalance, dispersion, and dominance. The results show there is significant variability in the effort requirements of heuristic strategies which may diminish the arguments for effort-accuracy trade-offs. Additionally, heuristic strategies were shown to be closest in accuracy to normative-rational strategies when context features matched dynamic decision settings. Ultimately, methods for restrictiveness and guidance based on trade-offs between total information and information imbalance were shown to enable reductions in total information that actually increased the accuracy of heuristics.
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ItemCognitive Process Model, Validation Data, Initial Modeling Results(Georgia Institute of Technology, 2013-07-30) Chua, Zarrin K.These are the model files for the cognitive process model (moderate, Apollo-like function allocations) and four landing areas on the South Pole of the moon. With this data set, the user should be able to visualize the chosen landing sites for each user in the August 2012 human in the loop experiment conducted with the NASA astronaut office, validation of the cognitive model, and a set of randomly generated data points used for initial results.
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ItemSystem design considerations for human-automation function allocation during lunar landing(Georgia Institute of Technology, 2013-07-08) Chua, Zarrin K.A desire to advance humanity's presence in space prompts the need for improved technology to send crew to places such as the Moon, Mars, and nearby asteroids. Safely placing a crewed vehicle on and in any landing condition requires a design decision regarding the distribution of responsibilities between the crew and automation. In this thesis, a cognitive process model is used to determine the necessary automated functionality to support astronaut decision making. Current literature lacks sufficient detailed knowledge regarding astronaut decision making during this task and observations of astronauts landing on the Moon are not readily available. Therefore, a series of human-in-the-loop experiments, one of which was conducted with the NASA Astronaut Office at Johnson Space Center, have been conducted to examine the changes in performance due to differing function allocations, trajectory profiles, and scenario operations. The data collected in the human-in-the-loop study has provided empirical data that has informed the cognitive process model, the requirements analysis, and provided insight regarding cockpit display usage and information needs. The proposed system requirements include design guidance for assisting astronauts during both nominal and off-nominal landing scenarios.
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ItemSystem Level Impact of Landing Point Redesignation for High-Mass Mars Missions(Georgia Institute of Technology, 2011-09) Chua, Zarrin K. ; Steinfeldt, Bradley A. ; Kelly, Jenny R. ; Clark, Ian G.This work presents a preliminary system level assessment of the payload mass change due to landing point redesignation of representative high-mass Mars systems (systems with entry masses greater than 20 t). An optimal propulsive descent guidance law which minimizes the control effort during the descent is used in order to assess the range of feasible landing sites as well as the mass impact on the payload of the system. It is shown that either increasing the entry mass or delaying the time of redesignating the landing site decreases the payload capability of reaching the surface as well as reduces the number of reachable landing sites. In addition, it is shown that the payloads associated with supersonic retropropulsion are more sensitive to the landing point redesignation time than systems using inflatable aerodynamic decelerators.
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ItemDeveloping a Prototype ALHAT Human System Interface for Landing(Georgia Institute of Technology, 2011-03) Hirsh, Robert L. ; Chua, Zarrin K. ; Heino, Todd A. ; Strahan, Al ; Major, Laura ; Duda, KevinNASA’s Autonomous Landing and Hazard Avoidance Technology (ALHAT) project is developing technologies for safe landing anytime/anywhere on planetary surfaces. Minimizing time, thus minimizing fuel consumption, is critical during landing, so ALHAT displays must convey information efficiently to operators. The ALHAT Human System Interface (HSI) team developed prototype displays, explored methods of providing situation awareness, and modeled the cognitive task and information requirement for landing site selection. Input from NASA astronauts and mission controllers was solicited to refine ALHAT display concepts in a series of evaluations. This paper discusses the evolution of ALHAT displays and future plans for ALHAT HSI.
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ItemQuantitative Assessment of Human Control on Landing Trajectory Design(Georgia Institute of Technology, 2010-12-02) Chua, Zarrin K.An increased thirst for scientific knowledge and a desire to advance humanity's presence in space prompts the need for improved technology to send crewed vehicles to places such as the Moon, Mars, and nearby passing asteroids. Landing at any of these locations will require vehicle capabilities greater than that previously used during the Apollo program or those applied in Low Earth Orbit. In particular, the vehicle and the on-board crew must be capable of executing precision landing in sub-optimal landing conditions during time-critical, high-stakes mission scenarios, such as Landing Point Designation (LPD) , or the critical phase of determining the vehicle's final touchdown point. Most proposed solutions involve automated control of landing vehicles, accepting no input from the on-board crew - effectively relegating them to payload. While this method is satisfactory for some missions, an automation-only approach during this critical mission phase may be placing the system at a disadvantage by neglecting the human capability of [what?]. Therefore, the landing system may result in a lack of dynamic flexibility to unexpected landing terrain or in-flight events. It is likely that executing LPD will require an ideal distribution of authority between the on-board crew and an automated landing system. However, this distribution is application-specific and not easily calculated. Current science does not provide enough detailed or explicit theories regarding allocation of automation, and the advantages provided by biological and digital pilots (either acting as the sole authoritarian or as a coordinated team) are difficult to describe in quantitative measures. Despite previous experience in piloting vehicles on the Moon, few cognitive models describing the decision-making process exist. The specialization of the pilot and the application pose significant practical challenges in regular observations in the target environment. The lack of quantitative knowledge results in predominantly qualitative design trade-offs during pre-mission planning. While qualitative analyses have proven to be useful to the mission designer, an understanding founded on quantitative metrics regarding the relationship between human control and mission design will provide the sufficient supplementary information necessary for overall success. In particular, increased knowledge of the impact of human control on landing trajectory design would allow for more efficient and thorough conceptual mission planning. This knowledge would allow visualization of the flight envelope possible for various degrees of human control and help establish conceptual estimations of critical mission parameters such as fuel consumption or task completion time. This report details an experiment undertaken to further understanding of the impact of moderate degrees of human control on landing trajectory design or vice versa during LPD. This report briefly summarizes current understanding and modeling of moderate control during LPD and similar applications, reviews previous and current efforts in implementing LPD, examines the pilot study to observe subjects in a simulated LPD task, and discusses the significance of findings from the pilot study.
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ItemExamination of Human Performance During Lunar Landing(Georgia Institute of Technology, 2010-03) Chua, Zarrin K. ; Feigh, Karen M. ; Braun, Robert D.Experimentally derived data was extrapolated to compare the lunar landing performance of human pilots to that of an automated landing system.12 The results of this investigation are presented. Overall, the pilots performed equal to or better than the automated system in 18% of the relevant cases, but required more fuel. Pilot site selections were further investigated as a function of the time to complete. Each hypothetical case was compared to the automated system, across a range of performance criteria weighting distributions. This performance criteria is threefold – proximity to point of interest, safety of the site, and fuel consumed. In general, the pilots perform better than the automated system in terms of safety and proximity to points of interest criteria. However, as the priority of fuel conservation increases, the tradeoff between using an autonomous landing system versus a human-in-command system favors the automation, especially if the pilot is not able to make the proper decision within a performance criteria specific threshold.
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ItemAnalysis of Human-System Interaction For Landing Point Redesignation(Georgia Institute of Technology, 2009-05-26) Chua, Zarrin K.Despite two decades of manned spaceflight development, the recent thrust for increased human exploration places significant demands on current technology. More information is needed in understanding how human control affects mission performance and most importantly, how to design support systems that aid in human-system collaboration. This information on the general human-system relationship is difficult to ascertain due to the limitations of human performance modeling and the breadth of human actions in a particular situation. However, cognitive performance can be modeled in limited, well-defined scenarios of human control and the resulting analysis on these models can provide preliminary information with regard to the human-system relationship. This investigation examines the critical case of lunar Landing Point Redesignation (LPR) as a case study to further knowledge of the human-system relationship and to improve the design of support systems to assist astronauts during this task. To achieve these objectives, both theoretical and experimental practices are used to develop a task execution time model and subsequently inform this model with observations of simulated astronaut behavior. The experimental results have established several major conclusions. First, the method of LPR task execution is not necessarily linear, with tasks performed in parallel or neglected entirely. Second, the time to complete the LPR task and the overall accuracy of the landing site is generally robust to environmental and scenario factors such as number of points of interest, number of identifiable terrain markers, and terrain expectancy. Lastly, the examination of the overall tradespace between the three main criteria of fuel consumption, proximity to points of interest, and safety when comparing human and analogous automated behavior illustrates that humans outperform automation in missions where safety and nearness to points of interest are the main objectives, but perform poorly when fuel is the most critical measure of performance. Improvements to the fidelity of the model can be made by transgressing from a deterministic to probablistic model and incorporating such a model into a six degree-of-freedom trajectory simulator. This paper briefly summarizes recent technological developments for manned spaceflight, reviews previous and current efforts in implementing LPR, examines the experimental setup necessary to test the LPR task modeling, discusses the significance of findings from the experiment, and also comments on the extensibility of the LPR task and experiment results to human Mars spaceflight.
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ItemModeling Cockpit Interface Usage During Lunar Landing Redesignation(Georgia Institute of Technology, 2009-04) Chua, Zarrin K. ; Major, Laura M. ; Feigh, Karen M.Fulfilling NASA’s space exploration objectives requires precision landing to reach lunar sites of interest. During the approach and landing stages, a landing point redesignation (LPR) display will provide information to the crew regarding the characteristics of alternate touchdown points. Building on a previous study which examined crew tasks during LPR but did not account for the specialized behavior of experts, this investigation will present a new task sequence model, specific to expert decision-making. This analysis furthers the development of a predictive task execution model, which is used to test the efficacy of alternate information display and operator actuator design concepts. The task model and cockpit display recommendations presented in this study provide a significant improvement in LPR task execution time. This paper examines the task sequence during lunar landing, describes the predictive task execution process model, and recommends cockpit display requirements for effective decision making.
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ItemTask Modeling for Lunar Landing Redesignation(Georgia Institute of Technology, 2009-04) Chua, Zarrin K. ; Major, Laura M.Man's return to the Moon requires advancement in landing technology to achieve safe and precise landing. The Autonomous Landing and Hazard Avoidance Technology project is developing an autonomous flight manager (AFM) to provide the capability of assisting the crew during critical landing phases, beyond the standard guidance, navigation, and control. One such phase is landing point redesignation (LPR), where the crew must select a safe landing aim point. A task model is created to analyze the functions required for the LPR task, the allocation of functions between crew and automation, and the information needed by the crew. Three bottlenecks are found in the LPR task: the inability to rapidly compare alternative aim points, the time penalty associated with changing internal mission objectives, and the hindrance of communicating such a change to the AFM. The LPR task model predicts a task execution time of 25 seconds for the best scenario, but implies design changes are necessary to improve a task execution of 5 minutes in the worst scenario. Implementation of the changes suggested in this paper will reduce crew workload and stress during lunar landing, and increase overall system risk and reliability.