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Daniel Guggenheim School of Aerospace Engineering

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search.filters.applied.f.advisor: Feigh, Karen M. × End date: 2019 × Start date: 2010 × search.filters.applied.f.isOrgUnitOfPublicationTitle: College of Engineering ×

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Now showing 1 - 10 of 13
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Computational simulation of adaptation of work strategies in human-robot teams

2019-07-22 , IJtsma, Martijn

Human-robot teams operating in complex work domains, such as space operations, need to adapt to maintain performance under a wide variety of work conditions. This thesis argues that from the start team design needs to establish team structures that allow flexibility in strategies for conducting the team’s collective work. In addition, team design needs to facilitate fluent coordination of work, fostering the interweaving of team members’ dependent actions in ways that accounts for the dynamic characteristics of the work and the work environment. This thesis establishes a methodology to analyze a team’s strategies based on computational modeling of a team’s collective work, including the teamwork required to coordinate dependent work between multiple team members. This approach consists of the systematic identification of feasible work strategies and the simulation of work models to address the dynamic and emergent nature of a team’s work. It provides a formative analysis tool to help designers predict and understand the effects of their design choices on a team’s feasible work strategies. Two case studies on space operations demonstrate how this approach can predict how work allocation and human-robot interaction modes can foster and/or limit the availability of appropriate work strategies.

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Decision support system development for human extravehicular activity

2017-08-24 , Miller, Matthew James

Human spaceflight is arguably one of mankind's most challenging engineering feats, requiring carefully crafted synergy between human and technological capabilities. One critical component of human spaceflight pertains to the activity conducted outside the safe confines of the spacecraft, known as Extravehicular Activity (EVA). Successful execution of EVAs requires significant effort and real-time communication between astronauts who perform the EVA and the ground personnel who provide real-time support. As NASA extends human presence into deep space, the time delay associated with communication between the flight crew and Earth-bound support crew will cause a shift from real-time to delayed communication. A decision support system (DSS) is one possible solution to enhance astronauts’ capability to identify, diagnose, and recover from time critical irregularities during EVAs without relying on real-time ground support. The contributions of this thesis are two fold. The first is domain specific and addresses the known deficiencies that will impact future human EVA operations. The second is methodological and generalizable across many domains. This thesis demonstrates that Cognitive Work Analysis (CWA) can be applied to yield design insight in the form of high level design requirements amenable to traditional systems engineering. Beginning with the first two phases of CWA, a broad work domain analysis of EVA is made to identify the system constraints on EVA operations. Subsequently, Control Task Analysis models were developed that yielded a set of DSS design requirements in the form of cognitive work and information relationship requirements which reflect the underlying states of knowledge associated with supporting EVA operations. Furthermore, this thesis demonstrates how a subset of those requirements, along side envisioning and testing within a future work context, can yield prototype DSS designs suitable for supporting future EVA operations. Finally, this thesis included a human-subject study to evaluate the resultant prototypes against the requirements to demonstrate both validity of the requirements and the verification of the design. As a result, this thesis contributes the underlying science needed to design a DSS within the EVA work domain for future mission operations.

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An IPPD approach providing a modular framework to closing the capability gap and preparing a 21st century workforce

2014-04-09 , Zender, Fabian

The United States are facing a critical workforce challenge, even though current unemployment is around 6.7%, employers find it difficult to find applicants that can satisfy all job requirements. This problem is especially pronounced in the manufacturing sector where a critical skills gap has developed, a problem that is exasperated by workforce demographics. A large number of employees across the various manufacturing sub-disciplines are eligible to retire now or in the near future. This gray tsunami requires swift action as well as long lasting change resulting in a workforce pipeline that can provide Science, Technology, Engineering, and Mathematics (STEM) majors in sufficient quantity and quality to satisfy not only the needs of STEM industries, but also of those companies outside of the STEM sector that hire STEM graduates. The research shown here will identify overt symptoms describing the capability gap, will identify specific skills describing the gap, educational causes why the gaps has not yet been addressed or is difficult to address, and lastly educational remedies that can contribute to closing the capability gap. A significant body of literature focusing on engineering in higher education has been evaluated and findings will be presented here. A multidisciplinary, collaborative capstone program will be described which implements some of the findings from this study in an active learning environment for students working on distributed teams across the US. Preliminary findings regarding the impact of these measures on the quantity of engineers to the US economy will be evaluated.

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Comparison of model checking and simulation to examine aircraft system behavior

2013-07-15 , Gelman, Gabriel E.

Automation surprises are examples of poor Human-Machine Interaction (HMI) where pilots were surprised by actions of the automation, which lead to dangerous situations during which pilots had to counteract the autopilot. To be able to identify problems that may arise between pilots and automation before implementation, methods are needed that can uncover potentially dangerous HMI early in the design process. In this work, two such methods, simulation and model checking, have been combined and compared to leverage the benefits of both. In the past, model checking has been successful at uncovering known automation surprises. Simulation, on the other hand, has been successful in the aviation domain and human factor issues. To be able to compare these two approaches, this work focused on a common case study involving a known automation surprise. The automation surprise that was examined, is linked to the former Airbus speed protection logic that caused aircraft on approach to change the flight mode, resulting in a sudden climb. The results provided by the model checking with SAL (Symbolic Analysis Laboratory) in a previous work, have been used to provide input for simulation. In this work, this automation surprise was simulated with the simulation platform WMC (Work Models that Compute) and compared to the corresponding results from SAL. By using the case study, this work provides a method to examine system behavior, such as automation surprises, using model checking and simulation in conjunction to leverage the benefits of both.

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Humans teaching intelligent agents with verbal instruction

2019-04-15 , Krening, Samantha

The widespread integration of robotics into everyday life requires significant improvement in the underlying machine learning (ML) agents to make them more accessible, customizable, and intuitive for ordinary individuals to interact with. As part of a larger field of interactive machine learning (IML), this dissertation aims to create intelligent agents that can easily be taught by individuals with no specialized training, using an intuitive teaching method such as critique, demonstrations, or explanations. It is imperative for researchers to be aware of how design decisions affect the human’s experience because individuals who experience frustration while interacting with a robot are unlikely to continue or repeat the interaction in the future. Instead of asking how to train a person to use software, this research asks how to design software agents so they can be easily trained by people. When creating a robotic system, designers must make numerous decisions concerning the mobility, morphology, intelligence, and interaction of the robot. This dissertation focuses on the design of the interaction between a human and intelligent agent, specifically an agent that learns from a human’s verbal instructions. Most research concerning interaction algorithms aims to improve the traditional ML metrics of the agent, such as cumulative reward and training time, while neglecting the human experience. My work demonstrates that decisions made during the design of interaction algorithms impact the human’s satisfaction with the ML agent. I propose a series of design recommendations that researchers should consider when creating IML algorithms. This dissertation makes the following contributions to the field of Interactive Machine Learning: (1) design recommendations for IML algorithms to allow researchers to create algorithms with a positive human-agent interaction; (2) two new IML algorithms to foster a pleasant user-experience; (3) a 3-step design and verification process for IML algorithms using human factors; and (4) new methods for the application of NLP tools to IML.

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Decision making with incomplete information

2017-05-09 , Canellas, Marc Christopher

Decision makers are continuously required to make choices in environments with incomplete information. This dissertation sought to understand and, ultimately, support the wide range of decision making strategies used in environments with incomplete information. The results showed that the standard measure of incomplete information as total information, is insufficient for understanding and supporting decision makers faced with incomplete information. The distribution of information was shown to often be a more important determinant of decision making performance. Two new measures of the distribution of incomplete information were introduced (option imbalance and cue balance) and tested across three computer simulations of 18 variations of decision making strategies within hundreds of environments and millions of decision tasks with incomplete information, and one human-subjects study. The simulations were powered by a new general linear model of decision making which can efficiently and transparently model a wide range of strategies beyond the traditional set in the literature. Of the many potential mediators of the relationship between the distributions of incomplete information and performance, only the strategies' estimates of missing information were significant in the computational studies. Accurate estimates resulted in total information being the only meaningful determinant of accuracy while inaccurate estimates resulted in low option imbalance and high cue balance causing high accuracy. The simulation results were partially contradicted by a study in which human decision makers with accurate estimates were affected by option imbalance and cue balance in the same manner as inaccurate estimates – suggesting that some distributions might simply be difficult regardless of the estimates. These results argued that decision support should modify the presentation of information away from difficult distributions. These arguments were codified as heuristic information acquisition and restriction rules which, when tested, increased accuracy without probability and cue weight information.

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A computational approach to situation awareness and mental models in aviation

2013-08-06 , Mamessier, Sebastien

Although most modern, highly-computerized flight decks are known to be robust to small disturbances and failures, humans still play a crucial role in advanced decision making in off-nominal situations, and accidents still occur because of poor human-automation interaction. In addition to the physical state of the environment, operators now have to extend their awareness to the state of the automated flight systems. To guarantee the accuracy of this knowledge, humans need to know the dynamics or approximate versions of the dynamics that rule the automation. The operator's situation awareness can decline because of a deficient mental model of the aircraft and an excessive workload. This work describes the creation of a computational human agent model simulating cognitive constructs such as situation awareness and mental models known to capture the symptoms of poor human-automation interaction and provide insight into more comprehensive metrics supporting the validation of automated systems in aviation.

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Furthering human-robot teaming, interaction, and metrics through computational methods and analysis

2019-03-29 , Ma, Mingyue (Lanssie)

Human-robot teaming is a complex design trade space with dynamic aspects and particulars. In order to support future day human-robot teams and scenarios, we need to assist team designers and evaluators in understanding core teaming components. This work is centered around teams that complete space missions and operations. The central scope and theme of this work target the way users should design, evaluate, and think about human-robot teams. This work attempts to do so by defining a framework, conceptual methodology, and operationalized metrics for human-robot teams. We begin by scoping and distilling common components from human-only teaming and human-robot teaming research based in areas such as human factors, cognitive psychology, robotics, and human-robot interaction. Taking these constructs, we derive a framework that describes and organizes the factors, as well as relationships between them. This work also presents a theoretical methodology to support designers to understand the impact teaming components have on expected interaction. This methodology is implemented for four case studies of distinct team types and scenarios including moving furniture, a SWAT team operation, a rover recon, and an in-orbit maintenance mission. After assessing various existing methodologies and perspectives, we derive metrics operationalized from work allocation. To test these learnings, this work modeled and simulated human-robot teams in action, specifically in an in-orbit maintenance scenario. In addition to analyzing simulation results given different team configurations, task allocations, and teamwork modes, a HITL experiment confirmed a human perspective of robotic team members. This experiment also refines the modeling of teams and validates our performance metrics. This dissertation makes the following contributions to the field of human-robot teaming and interaction: 1) Created a new comprehensive framework for human-robot teaming by combining key components of team design and interaction, 2) Developed a method to identify distinct archetypes of interaction in human-robot teams (and showed how they fit into a universal framework), 3) Derived metrics from the HRT framework to capture the teaming elements beyond performance and efficiency; operationalized the method and metrics in a computational framework for simulation and analysis, 4) Extended existing computational framework for function allocation to include the metrics, 5) Demonstrated the sensitivity of effective teams to attributes of both teamwork and taskwork.

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Supporting general aviation pilots during rerouting process due to sudden weather changes

2015-07-24 , Tokadli, Guliz

General aviation pilots need different types of flight information in order to follow events and the changes related to the aircraft environment while flying. However, general aviation cockpits have some limitations as space to install flight displays to provide flight information beyond the basics to the pilot. Additionally, more sophisticated instrumentation is often expensive to install and maintain. With the development of the tablet-based software applications (such as ForeFlight, WingX Pro7 or Garmin Pilot applications for iPad), general aviation pilots have started to use them instead of paper documentation. These software applications provide essential flight information such as weather forecast, aviation charts, flight documents, etc. Unfortunately, the expectations for their capabilities are changing with the increased demand and popularity of these software applications. Therefore, these flight planning software applications are compared to find what is missing and what have not met the expectation of pilots. First, how the software applications support their decision-making process was described and demonstrated to choose the appropriate flight parameters to change flight path while handling with the other cockpit responsibilities. Finally, these design requirements were validated via HITL tests in a part-task flight simulator. The results provided that the suggested design requirements are found highly useful for both novice and expert general aviation pilots. Specifically, novice general aviation pilots might be able to get visualization to compare real-time weather and weather forecast, and then they might gain experience to improve their success for a in-flight re-planning. On the other side, expert pilots might prefer to use this system if they fly an airspace which they are not familiar to weather features of that region.

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Cognitive Process Model, Validation Data, Initial Modeling Results

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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