RoMAN 24 Human(s) On The Loop Demand Aware Robot Scheduling: A Mixed Reality based User Study

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Scheduling tasks and subtasks for multiple heterogeneous robots poses a significant challenge, particularly in scenarios where human supervision is essential. The complexity is further compounded when considering human factors in the scheduling process, especially in demand-aware task scheduling environments(tasks are generated based on external demand). The primary goal of this research is to understand human decision-making and how it affects their scheduling process, and we undertook two studies. In the first study, we employed a mixed reality based user study to explore how human perception of the scheduling environment influences task
scheduling and facilitates personalized resource allocation. Our findings indicate that human task schedulers exhibit enhanced performance when assisted by autonomous agents, compared to scenarios with limited autonomy in robotic systems. To explore the impact of robot planning on human decision-making and its effects on task scheduling and collision-aware scheduling capabilities, we conducted the second study. This study employed a mixed reality-based warehouse environment, where two users
controlled different robots with shared objectives. The findings suggest that human operators exhibited improved collision-aware scheduling without compromising their demand-aware scheduling capabilities when visual aids such as collision cones were incorporated.