研究机器人在紧急疏散中如何主动让位，提升人类心理安全感。
Beyond Collision Avoidance: Multi-Robot Yielding and Spatial Affordance in Emergency Evacuations
- 设计四种让位策略，通过虚拟实验对比人类反应。
- 主动避让（Hide）最被接受，冻结或抢道会引发心理不适。
- 利用环境空间可提高舒适感，忽略明显避让区会引发预期落空。
随着移动服务机器人与行人共存日益普遍，在封闭空间紧急疏散中实现被动安全行为至关重要。现有多机器人让路策略通常仅关注碰撞规避和宏观流体优化，忽视了环境可用性及人类空间预期。为弥合宏观理论与微观感知之间的差距，我们开展了一项基于游戏的虚拟疏散实验（N=56），研究四种多机器人让路策略（Hide、LineEscape、Freeze、ShortestPath）在有无避难凹槽的狭窄走廊中的个体心理反应。结果确立了明确的偏好顺序（Hide > LineEscape > Freeze > ShortestPath），表明主动空间让位显著优于静止或效率优先策略。关键发现是：环境可用性深刻影响认知预期。积极利用可用凹槽可增强主动让位的心理舒适度；反之，未使用明显避难区（如执行LineEscape）可能引发预期违背，导致感知认知延迟显著上升，尽管实际路径无障碍。此外，先前机器人交互经验有助于用户理解复杂社交意图。最终，本研究证明紧急情况下人机安全交互需从单纯轨迹优化转向语义感知导航。未来工作将拓展该框架，研究机器人集群与人群间的复杂互动。
原文摘要 · Abstract (English)
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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