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Daniel Guggenheim School of Aerospace Engineering

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https://hdl.handle.net/1853/70742

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College of Engineering

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Aerospace Design Group

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Aerospace Systems Design Laboratory (ASDL)

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Air Transportation Laboratory

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Cognitive Engineering Center

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Space Systems Design Laboratory (SSDL)

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https://finding-aids.library.gatech.edu/agents/corporate_entities/106

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Publication Search Results

Now showing 1 - 10 of 17

Understanding and Supporting Decision Making in Denied and Degraded Environments

(Georgia Institute of Technology, 2023-07-25) Sealy, William I.N.

Decision making is not guaranteed to occur in well-structured environments with perfect information. Tasks in the research most often focus on decisions made with complete information in an unlimited time-frame, and in cases where information is missing or uncertain, the current research stops short of addressing the effect of the distribution of the missing information in the environment. This dissertation seeks specifically to understand how these distributions of information affect decision makers under time pressure, and how best to support decision making in imperfect environments across a range of decision strategies. The contributions of the work are three fold. First, results showed that three studied factors of information distributions (namely Total Information, Complete Attribute Pairs, and Information Imbalance) were significant predictors of decision accuracy in six separate human subject studies featuring varying information complexity and decision strategy biases. Second, this dissertation has highlighted key differences in expert and novice behavior through the lens of information estimation and predecisional information search which further explained individual differences in performance under uncertainty and provided novel design considerations for decision support systems (DSS) in these environments. Finally, the application of both information modification and option prediction DSS showed significant increases in accuracy and reduction in response times across performance groups in both heuristic and analytically-biased environments.
Augmented reality cueing methodologies for rotorcraft shipboard landings

(Georgia Institute of Technology, 2022-06-22) Walters, Robert

Augmented Reality Cueing Methodologies For Rotorcraft Shipboard Landings Robert Walters 159 Pages The helicopter-ship interface is one of the most challenging flight regimes in which pilots operate. Several factors make this flight regime complicated, such as the relative motion between the aircraft, the ship, and the sea, and also the air wake turbulence and the confined nature of the landing zone. Degraded visuals conditions such as sea spray, adverse weather, and poor lighting conditions compound the other difficulties. The high pilot workload from these factors can lead to a loss of situational awareness which can result in catastrophic aircraft accidents. Currently fielded cueing systems are not up to this challenge. To reduce pilot workload and improve situational awareness and performance, better pilot cueing is required. This dissertation investigated the extent to which augmented reality cueing utilizing modern rendering techniques reduces pilot workload and improves situational awareness and performance. This was done by supporting a ‘head-up, eyes-out’ ego-centric interface philosophy. The cueing systems sought to incorporate common pilot mission task elements into the design. Changes to both the path preview and trajectory prediction were studied. The visual elements of the cues were displayed as if they were comprised of three dimensional physical objects. Operational flexibility in high workload environments is key to pilot task accomplishment. The ability to dynamically generate on demand flight trajectories that pilots could manually fly was another goal of this dissertation. The mathematical framework of Bézier curves was utilized for trajectory planning to ensure the paths satisfy the needs of the pilot, the certification authorities, and the specific mission task element. Four different cueing paradigms were programmed into the Georgia Tech reconfigurable rotorcraft flight simulator. These paradigms were; a 2D Head Up Display (HUD), a Flight Lead Cueing System (FLCS), a Tunnel In the Sky (TIS), and a 3D Flight Path Marker (FPM). The cues were then evaluated using objective measures and pilot workload surveys in a series of Pilot-in-the-Loop (PIL) studies. A total of twenty pilots took part in the study. Seven pilots participated in phase 1, three in phase 2, and ten in phase 4. Phase 3 included only data flown by the author and LTC Joe Davis due to pandemic related travel restrictions preventing the use of additional external pilots. Most PIL studies have a relatively low number of participants, in the range of two to six. In order to gain statistical significance from a relatively low number of participants the participants are asked to repeat the task several times. For example the pilots in phase 4 each flew a total of 54 approaches. The central limit theorem, states that a distribution will be approximately normally for large sample sizes, where a sample size over 30 is considered large. Consequently even when the data is divided to look at a specific cueing condition or starting location the large sample size criteria is met and we can gain statistical insight. Bézier curves provide a feasible method to dynamically generate landing trajectories for pilots to fly by hand. The methods are numerically stable and execute fast enough that there is minimal perceptual latency to the pilot. The pilots were able to follow the generated trajectories with sufficient accuracy both laterally and vertically. The paradigm shift of using 3D AR cueing which the pilots mentally process as signals instead of signs or symbols resulted in reduced workload and had performance that was the same or better than traditional cueing methods.
USING A HANDS-ON ROBOTICS PROJECT TO AFFECT SKILL DEVELOPMENT IN A CONTROL ANALYSIS COURSE

(Georgia Institute of Technology, 2021-05-05) Inghilleri, Niccolo

This study aims to assess the impact on skill development of a hands-on experimentation and learning device within the undergraduate aerospace control analysis curriculum at Georgia Institute of Technology. The Transportable Rotorcraft Electronics Control System (TRECS) take-home lab kit was used as a hands-on learning treatment on 37.5% (n=24) of the Fall 2020 Control Analysis course taught by Chance McColl. The other students (n=40) in the course were taken as a control group. A Likert scale skill evaluation survey was performed to determine which skills are developed while using the TRECS. The response distributions and an accompanying Mann Whitney U-test can be found in the results section. On the topic of optimal control algorithms, which are extensively covered in the course lecture material and applied in the TRECS project, Users and Nonusers reported significantly (p=0.10) increased response and Users were found to have significantly (p=0.10) improved beyond Nonusers. Response distributions for topics including PID control, embedded software, and other electronics were not found to change significantly throughout the course, despite the application of the TRECS treatment or the presence of the topic in the course curriculum. The other goal of this research was to propose an improved study which addresses the limitations to this dataset such as small sample sizes, self-reports, sole focus on development of course-specific subject matter and selection bias from the lack of random assignment of the treatment. The recommendations for a future study are aimed to improve trustworthiness, increase transferability, and incorporate multiple verification elements including the development of a new skill assessment that could evaluate students’ application-level understanding of course concepts.
Computational simulation of adaptation of work strategies in human-robot teams

(Georgia Institute of Technology, 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.
Humans teaching intelligent agents with verbal instruction

(Georgia Institute of Technology, 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.
Furthering human-robot teaming, interaction, and metrics through computational methods and analysis

(Georgia Institute of Technology, 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.
Decision support system development for human extravehicular activity

(Georgia Institute of Technology, 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.
Decision making with incomplete information

(Georgia Institute of Technology, 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.
Supporting general aviation pilots during rerouting process due to sudden weather changes

(Georgia Institute of Technology, 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.
An IPPD approach providing a modular framework to closing the capability gap and preparing a 21st century workforce

(Georgia Institute of Technology, 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.