超越避障：紧急疏散中的多机器人让路与空间可得性
Beyond Collision Avoidance: Multi-Robot Yielding and Spatial Affordance in Emergency Evacuations
AI总结：
研究通过虚拟疏散实验探讨多机器人让路策略对人类空间期望的影响，发现主动让路优于冻结和效率优先策略，并揭示环境可得性对认知预期的塑造作用。
摘要：
随着移动服务机器人与行人共存，确保紧急疏散期间的被动安全行为至关重要。现有多机器人让路策略往往仅关注碰撞避障和宏观流优化，忽视环境可得性和人类空间期望。为弥合宏观理论与微观感知间的差距，我们进行了基于游戏的虚拟疏散实验（N=56）。我们研究了四种多机器人让路策略（Hide, LineEscape, Freeze, ShortestPath）在有无避难所的狭窄走廊中的个体心理反应。结果建立了一个稳健的偏好等级（Hide > LineEscape > Freeze > ShortestPath），表明主动空间让路显著优于冻结和效率优先方法。关键发现是环境可得性深刻塑造了认知预期。积极利用可用避难所增强了主动让路的心理舒适度（Hide）。相反，未能利用明显避难所（如执行LineEscape）可能触发预期违反。这体现在感知认知延迟显著增加，尽管客观轨迹未受阻碍。此外，先前的机器人交互经验有助于用户解读复杂的社会意图。最终，本研究证明紧急情况中的人机交互安全必须从纯轨迹优化发展到语义感知导航。未来工作将扩展该框架以研究机器人群与行人群体之间的复杂交互。
英文摘要：
As mobile service robots increasingly coexist with pedestrians, ensuring passively safe behaviour during confined emergency evacuations is critical. Existing multi-robot yielding strategies often focus solely on collision avoidance and macroscopic flow optimisation, overlooking environmental affordances and human spatial expectations. To bridge the gap between macroscopic theory and micro-level perception, we conducted a game-based virtual evacuation experiment (N=56). We investigated individual psychological responses to four multi-robot yielding strategies (Hide, LineEscape, Freeze, ShortestPath) across confined corridors with and without refuge niches. Our results establish a robust preference hierarchy (Hide > LineEscape > Freeze > ShortestPath), demonstrating that proactive space-yielding significantly outperforms freezing and efficiency-first approaches. Crucially, we found that environmental affordances heavily shape cognitive expectations. Actively utilising available niches amplifies the psychological comfort of proactive yielding (Hide). Conversely, failing to use an obvious niche (e.g., executing LineEscape) may trigger Expectation Violation. This is reflected in a drastically increased perceived cognitive delay, despite objectively unimpeded trajectories. Furthermore, prior robot interaction experience helps users decode complex social intents. Ultimately, this research demonstrates that safe human-robot interaction during emergencies must evolve from pure trajectory optimisation to semantically aware navigation. Future work will extend this framework to investigate complex interactions between robot swarms and pedestrian crowds.
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