Applying Ground Robot Fleets in Urban Search: Understanding Professionals'Operational Challenges and Design Opportunities
🤖 AI Summary
This study addresses the challenges faced by frontline responders in urban search and rescue operations—namely high cognitive load, physical fatigue, coordination difficulties, and reliance on paper-based tools. Through focus group interviews with law enforcement personnel, it systematically uncovers real-world requirements for multi-robot systems in public safety and proposes four human-centered design principles: scalable multi-robot control interfaces, institution-specific path optimization, a replay-informed real-time replanning mechanism, and trust-preserving visual assistive cues. Integrating computer vision, large language model–based interaction, and multi-robot协同 planning, the proposed system substantially reduces cognitive and physical burdens, enhances team and situational awareness, and supports hypothesis-driven search strategies, thereby offering critical design foundations for deployable and accountable human-robot collaborative search and rescue systems.
Application Category
📝 Abstract
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
Problem
Research questions and friction points this paper is trying to address.
urban search
ground robot fleets
operational challenges
public safety professionals
cognitive workload
Innovation
Methods, ideas, or system contributions that make the work stand out.
ground robot fleets
urban search and rescue
human-centered design
LLM-powered interfaces
situational awareness
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