发表机构
Sogang University（西江大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种考虑人类因素的贪婪任务分配方法，动态调节监督能力以平衡心理工作量，防止超负荷，提升多人类多机器人监督场景中的团队绩效。
AI 中文摘要
我们提出了一种考虑人类因素的方法，用于将机器人监督任务分配给多名人类操作员。在多个操作员偶尔远程操作多个机器人以帮助机器人克服困难的场景中，将监督控制任务分配给人类需要考虑各个操作员的实时认知状态。然而，大多数现有方法假设每个操作员具有固定的监督能力，并忽视了工作量和疲劳等人类因素的波动。因此，工作量分布可能不平衡，一些操作员超负荷，而另一些则未被充分利用。我们的方法动态调节监督能力，并以保持心理工作量平衡、防止超负荷和提高整体团队绩效的方式分配任务。该分配方法采用贪婪策略，通过任务优先级最小化估计的操作员工作量。机器人根据操作员当前的监督能力进行分配，所需工作量取决于任务类型。在用户研究中，跨预定时间间隔的分析表明，与不考虑人类因素的基线方法相比，所提出的方法始终实现更高的性能和更低的行为疲劳迹象。这些结果突显了自适应能力调整作为在长时间、高需求环境中维持操作员绩效的有效预防机制。
英文摘要
We propose a human-factor-aware method of allocating robot supervision tasks to multiple human operators. In scenarios where multiple operators occasionally teleoperate multiple robots to help the robots overcome difficulties, the allocation of the supervisory control tasks to humans needs to consider the real-time cognitive states of individual operators. However, most existing methods assume fixed supervisory capacity per operator and overlook fluctuations in the human factors such as workload and fatigue. As a result, workload distribution can be unbalanced where some operators become overloaded while the others remain underused. Our method dynamically regulates supervisory capacity and allocates tasks in a way that maintains balanced mental workload, prevents overload, and improves overall team performance. The allocation method uses a greedy strategy that minimizes estimated operator workloads with task prioritization. Robots are assigned to operators by reflecting their current supervisory capacity where the required effort depends on the types of tasks. In the user study, the analysis across predefined time intervals shows that the proposed method consistently achieves higher performance and lower behavioral signs of fatigue compared to a baseline method that does not consider human factors. These results highlight adaptive capacity adjustment as an effective preventive mechanism for sustaining operator performance in long-duration, high-demand settings.
Comments10 pages, 7 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible
