Piloting a commercial aircraft involves performing multiple tasks in a real-time environment that require pilot's attention and cognitive resource allocation. Due to resource limitation, pilots must perform cockpit task management (CTM) because they cannot perform all tasks that demand their attention at once. Hence, pilots must prioritize the tasks in the order of most to least important and allocate their resources according to this prioritization. Over the years, pilots have developed rules of thumb for task prioritization in facilitating CTM. A task prioritization error is simply an error made by the flight crew when they perform lower priority tasks as opposed to higher priority tasks, where priority is determined by the Aviate-Navigate-Communicate-Manage Systems (A-N-C-S) task ordering. Although the level of flight deck automation has been suggested as one factor influencing the likelihood of task prioritization errors, there has so far been just one study directed towards confirming that hypothesis. Hence the first objective of this study was to determine the effect of the level of automation on CTM performance. CTM performance was measured by looking at the number of task prioritization errors committed by pilots in different levels of automation. In addition to the level of automation, there was also reason to believe that the pilot's automation proficiency might affect CTM performance. Therefore, the second objective of this study was to determine the effect of automation proficiency on CTM performance. Nine airline transport pilots served as subjects in this study. Three flying scenarios and three levels of flight deck automation were simulated on a part-task flight simulator. Each pilot ran three different combinations of flight deck automation and flying scenario. The CTM performance for each pilot was determined by identifying the number of task prioritization errors committed in each experiment run. The average number of errors in different levels of automation and automation proficiency were compared for their effect on CTM performance using Analysis of Variance (ANOVA). It was found that the level of automation affected CTM performance depending scenarios in which phases of flight differed. However, automation proficiency, measured by glass cockpit hours, was found to have no effect on CTM performance. / Graduation date: 2000

No description available.

Thesis (M.S.)--Ohio University, March, 2004. / Title from PDF t.p. Includes bibliographical references (leaves 80-81).

In recent years, Unmanned Aerial Vehicles (UAVs) have been used extensively in military conflict situations to execute intelligence, surveillance and reconnaissance missions. However, most of the current UAV platforms have limited collaborative capabilities, and consequently they must be controlled individually by operators on the ground. The purpose of the research presented in this thesis is to derive algorithms that can enable multiple UAVs to reason about the movements of multiple ground targets and autonomously coordinate their efforts in real-time to ensure that the targets do not escape. By improving the autonomy of multivehicle systems, the workload placed on the command and control operators is reduced significantly. To derive effective adversarial control algorithms, the adversarial scenario is modeled as a multiplayer differential game. However, due to the inherent computational complexity of multiplayer differential games, three less computationally demanding differential pursuit-evasion game-based algorithms are presented. The purpose of the algorithms is to quickly derive interception strategies for a team of autonomous vehicles. The algorithms are applicable to scenarios with different base assumptions, that is, the three algorithms are meant to complement one another by addressing different types of adversarial problems.