人間適応型リアルタイムタスク割り当てによる複数人・複数ロボットの監督制御
Real-Time Human-Adaptive Task Allocation for Multi-Human Multi-Robot Supervision
複数の人間オペレータが複数のロボットを監督する状況で、作業負荷や疲労といった認知状態をリアルタイムに考慮し、貪欲法でタスクを動的に割り当てる手法を提案。ユーザ研究で疲労低減と性能向上を確認。
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著者: Seabin Lee, Sujeong Park, Nayoung Kim, Sungjin Park, Haechan Jung, Changjoo Nam
分類: cs.RO
原文アブストラクト
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.