在城市搜索中应用地面机器人队伍：理解专业人员的操作挑战与设计机会
Applying Ground Robot Fleets in Urban Search: Understanding Professionals' Operational Challenges and Design Opportunities
AI总结：
本文探讨了在城市搜索中应用地面机器人队伍对公共安全专业人员操作挑战的影响，提出了减轻认知和体力负担的设计机会。
摘要：
城市搜索要求在高认知和情境负荷下做出快速、有说服力的决定，并持续付出体力努力。事件指挥官必须计划、协调和记录时间关键性的操作，而现场搜索人员则在不确定的环境中执行不断变化的任务。随着技术的进步，地面机器人队伍结合基于计算机视觉的情报意识和基于大语言模型的接口，有潜力减轻这些操作负担。然而，没有专门研究探讨公共安全专业人员如何感知此类技术或设想其如何整合到现有实践中，这可能导致构建技术先进但不实际的解决方案。为了解决这一差距，我们与弗吉尼亚州五个地方部门的八名警察进行了焦点小组讨论。我们的发现表明，地面机器人可以减少专业人员对纸质参考资料、心理计算和临时协调的依赖，从而在四个关键挑战领域减轻认知和体力负担：（1）在多个搜索假设下分配劳动力，（2）保持群体意识和情境意识，（3）构建符合失踪人员特征的路线规划，（4）在不确定性下管理认知和体力疲劳。我们进一步识别了四个未来地面机器人队伍整合到公共安全操作中的设计机会和要求：（1）可扩展的多机器人规划和控制接口，（2）特定机构的路线优化，（3）由复盘更新信息驱动的实时重新规划，（4）基于视觉的提示辅助，这在保持操作信任的同时减少认知负荷。我们最后提出了可部署、可问责和以人类为中心的城市搜索支持系统的设计启示。
英文摘要：
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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