Modeling Real-Time Human-Automation Collaborative Scheduling of Unmanned Vehicles.

摘要

Recent advances in autonomy have enabled a future vision of single operator control of multiple heterogeneous Unmanned Vehicles (UVs). Real-time scheduling for multiple UVs in uncertain environments will require the computational ability of optimization algorithms combined with the judgment and adaptability of human supervisors. Automated Schedulers (AS), while faster and more accurate than humans at complex computation, are notoriously 'brittle' in that they can only take into account those quantifiable variables, parameters, objectives, and constraints identified in the design stages that were deemed to be critical. Previous research has shown that when human operators collaborate with AS in real-time operations, inappropriate levels of operator trust, high operator workload, and a lack of goal alignment between the operator and AS can cause lower system performance and costly or deadly errors. Currently, designers trying to address these issues test different system components, training methods, and interaction modalities through costly human-in-the-loop testing. Thus, the objective of this thesis was to develop and validate a computational model of real-time human-automation collaborative scheduling of multiple UVs. First, attributes that are important to consider when modeling real-time human-automation collaborative scheduling were identified, providing a theoretical basis for the model proposed in this thesis. Second, a Collaborative Human-Automation Scheduling (CHAS) model was developed using system dynamics modeling techniques, enabling the model to capture non-linear human behavior and performance patterns, latencies and feedback interactions in the system, and qualitative variables such as human trust in automation. The CHAS model can aid a designer of future UV systems by simulating the impact of changes in system design and operator training on human and system performance.