Enhancing Situation Awareness in Multi-Robot Supervision: The Role of Adaptive Interfaces for Multiple Displays

Supervising multiple autonomous robots requires operators to maintain situation awareness (SA) while allocating attention across displays, and increasing displays is often assumed to improve performance. We demonstrate that multi-robot supervision does not scale linearly with display count and that adaptive interface design is critical for effective supervision. A controlled experiment (N = 30) crossed four display configurations (3, 5, 7, 9) with three interface types (baseline, dynamic, adaptive), measuring task performance, subjective workload, SA, and gaze-based attention allocation. Performance and SA improved only up to five displays and deteriorated at seven or more, indicating that increasing information is counterproductive under attentional constraints. The proposed adaptive interface systematically counteracted this decline by prioritizing unattended but task-relevant robots, producing robust SA gains and workload reduction across all conditions. These results provide empirical evidence that scalable multi-robot supervision requires attention-based adaptive interface design aligned with human cognitive limits, not unstructured growth in visual workspace.