Effectiveness of Human-Autonomy Teams in UAV Operations
McGuire, Mollie R.
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Optimal effectiveness in human-autonomy teams can only be achieved by understanding the strengths and weaknesses of the human and autonomous team members. In an ever-evolving technological landscape this becomes an increasingly complex task. As unmanned vehicles (UxV) and autonomy evolves, the ratio of humans to unmanned vehicles will decrease, so that only one human is needed for multiple UxVs. It is important to understand how many UxVs one human can support, and when does exception handling become overwhelming for the human. One important question then is how many UxVs one person can handle successfully. This question requires several other questions to be considered, such as how much human involvement is needed under normal and exceptional circumstances; and how much cognitive load is added per UxV during normal execution and during exception handling. As technology progresses, the exception handling will be resolved by both the human and supervisory autonomy. When more autonomy is introduced to aid human performance, it requires an understanding of the human-machine dynamic that would lead to the most effective team. The current proposal aims to build on understanding the variables that are important in creating an optimal team in the near future when autonomy becomes less of a tool and more of an active teammate. The proposed research seeks to observe and provide analyses on studies aimed at identifying targeted variables that affect the effectiveness of a human-autonomy team. While the variables that make an effective team are numerous, the focus on this research will be on operator experience and trust in UxV operation with and without a supervisory autonomy agent. Operators in the studies will have different levels of operator experience, with novices being those that just finished training, to experts who have 5+ years of experience. Additionally, observation will include that of operators in various adversarial conditions with and without a supervisory agent. The deliverables will be information furthering the understanding of the dynamics that are important in creating an effective human-autonomy team in multi-UxV support.
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