研究警员使用地面机器人搜寻时的痛点，提出四类设计改进方向。
Applying Ground Robot Fleets in Urban Search: Understanding Professionals' Operational Challenges and Design Opportunities
- 通过民警访谈发现机器人可减轻认知与体力负担
- 四项核心挑战：任务分配、态势感知、路径规划、疲劳管理
- 提出可扩展的多机控制与实时重规划等设计需求
城市搜救需在高认知负荷下快速做出可辩护决策并持续体能投入。指挥官须规划、协调和记录关键行动，现场搜寻人员则在不确定环境中执行动态任务。近年来，结合计算机视觉与大语言模型的地面机器人集群有望缓解这些压力。然而，尚无专门研究探讨公共安全人员对这类技术的看法或其在现有流程中的整合设想，可能导致开发出技术先进但不实用的系统。为此，我们在弗吉尼亚州五家警察部门开展焦点小组访谈，共收集八名警员反馈。结果表明，地面机器人可减少对纸质资料、心算及临时协作的依赖，在四大核心挑战中发挥作用：（1）在多个搜寻假设间合理分配人力；（2）维持团队与环境态势感知；（3）制定符合走失者特征的路线规划；（4）在不确定性下管理认知与身体疲劳。据此识别出四个设计机会：（1）可扩展的多机器人规划与控制界面；（2）机构定制化的路径优化；（3）基于事后复盘更新的实时重规划；（4）视觉辅助提示机制，在保持操作信任的同时降低认知负荷。研究最后提出面向可部署、可问责、以人为本的城市搜救支持系统的设计启示。
原文摘要 · Abstract (English)
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
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