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Daniel Guggenheim School of Aerospace Engineering
Daniel Guggenheim School of Aerospace Engineering
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ItemUnderstanding 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.
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ItemAugmented reality cueing methodologies for rotorcraft shipboard landings(Georgia Institute of Technology, 2022-06-22) Walters, RobertAugmented 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.
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ItemUSING A HANDS-ON ROBOTICS PROJECT TO AFFECT SKILL DEVELOPMENT IN A CONTROL ANALYSIS COURSE(Georgia Institute of Technology, 2021-05-05) Inghilleri, NiccoloThis 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.