This study investigates First Responders' (FRs) attitudes toward the use of semantic information and Situational Awareness (SA) in robotic systems during emergency operations. A structured questionnaire was administered to 22 FRs across eight countries, capturing their demographic profiles, general attitudes toward robots, and experiences with semantics-enhanced SA. Results show that most FRs expressed positive attitudes toward robots, and rated the usefulness of semantic information for building SA at an average of 3.6 out of 5. Semantic information was also valued for its role in predicting unforeseen emergencies (mean 3.9). Participants reported requiring an average of 74.6\% accuracy to trust semantic outputs and 67.8\% for them to be considered useful, revealing a willingness to use imperfect but informative AI support tools. To the best of our knowledge, this study offers novel insights by being one of the first to directly survey FRs on semantic-based SA in a cross-national context. It reveals the types of semantic information most valued in the field, such as object identity, spatial relationships, and risk context-and connects these preferences to the respondents' roles, experience, and education levels. The findings also expose a critical gap between lab-based robotics capabilities and the realities of field deployment, highlighting the need for more meaningful collaboration between FRs and robotics researchers. These insights contribute to the development of more user-aligned and situationally aware robotic systems for emergency response.

翻译：本研究调查了一线救援人员在紧急行动中对机器人系统使用语义信息与情境感知的态度。通过向来自八个国家的22名一线救援人员发放结构化问卷，收集了其人口统计特征、对机器人的总体态度以及语义增强情境感知的使用体验。结果显示，大多数救援人员对机器人持积极态度，对语义信息在构建情境感知方面的有用性平均评分为3.6分（满分5分）。语义信息在预测突发紧急情况方面的作用也获得重视（平均分3.9）。参与者表示需要平均74.6%的准确率才能信任语义输出，67.8%的准确率可视为有用，这表明他们愿意使用不完美但具有信息价值的AI辅助工具。据我们所知，本研究首次在跨国背景下直接调研一线救援人员对基于语义的情境感知的看法，提供了新颖的见解。研究揭示了现场最受重视的语义信息类型（如物体识别、空间关系和风险情境），并将这些偏好与受访者的职责、经验和教育水平相关联。研究结果同时暴露了实验室机器人能力与现场部署现实之间的关键差距，强调了一线救援人员与机器人研究人员之间需要建立更具实质意义的协作。这些发现有助于开发更符合用户需求、具备情境感知能力的应急响应机器人系统。