Urban searches demand rapid, defensible decisions and sustained physical effort under high cognitive and situational load. Incident commanders must plan, coordinate, and document time-critical operations, while field searchers execute evolving tasks in uncertain environments. With recent advances in technology, ground-robot fleets paired with computer-vision-based situational awareness and LLM-powered interfaces offer the potential to ease these operational burdens. However, no dedicated studies have examined how public safety professionals perceive such technologies or envision their integration into existing practices, risking building technically sophisticated yet impractical solutions. To address this gap, we conducted focus-group sessions with eight police officers across five local departments in Virginia. Our findings show that ground robots could reduce professionals' reliance on paper references, mental calculations, and ad-hoc coordination, alleviating cognitive and physical strain in four key challenge areas: (1) partitioning the workforce across multiple search hypotheses, (2) retaining group awareness and situational awareness, (3) building route planning that fits the lost-person profile, and (4) managing cognitive and physical fatigue under uncertainty. We further identify four design opportunities and requirements for future ground-robot fleet integration in public-safety operations: (1) scalable multi-robot planning and control interfaces, (2) agency-specific route optimization, (3) real-time replanning informed by debrief updates, and (4) vision-assisted cueing that preserves operational trust while reducing cognitive workload. We conclude with design implications for deployable, accountable, and human-centered urban-search support systems

翻译：城市搜救任务需要在较高的认知与情境负荷下，做出快速且可验证的决策，并维持持续的体力投入。事故指挥官必须对时间紧迫的行动进行规划、协调与记录，而现场搜救人员则需在不断变化的不确定环境中执行任务。随着技术的进步，地面机器人集群与基于计算机视觉的情境感知系统及大语言模型驱动的界面相结合，有望减轻这些操作负担。然而，目前尚无专门研究探讨公共安全专业人员如何看待此类技术，或如何设想将其整合到现有工作流程中，这可能导致开发出技术先进但不切实际的解决方案。为填补这一空白，我们与弗吉尼亚州五个地方部门的八名警官进行了焦点小组讨论。研究发现，地面机器人可减少专业人员对纸质参考资料、心算及临时协调的依赖，从而在四个关键挑战领域缓解认知与体力压力：(1) 在多个搜救假设间分配人力，(2) 保持团队态势感知与环境态势感知，(3) 构建符合失踪人员特征的路线规划，以及(4) 在不确定性下管理认知与体力疲劳。我们进一步提出了未来地面机器人集群融入公共安全行动的四个设计机遇与需求：(1) 可扩展的多机器人规划与控制界面，(2) 机构定制化的路线优化，(3) 基于任务简报更新的实时重规划，以及(4) 在降低认知负荷的同时保持操作信任的视觉辅助提示。最后，我们总结了可部署、可问责且以人为本的城市搜救支持系统的设计启示。