Respiratory-rate (RR) monitoring is a critical component of remote triage and victim assessment in emergency response, disaster recovery, and infectious-disease scenarios, where minimizing physical contact can reduce responder risk and improve operational safety. However, field deployment of contactless RR monitoring remains challenging due to variable illumination, posture changes, platform heterogeneity, and the impracticality of wearable sensors in hazardous environments. In this paper, we present a modality-adaptive contactless RR monitoring framework for heterogeneous mobile robots with onboard edge computing. The proposed system combines brightness-adaptive sensor selection across RGB, thermal, near-infrared (NIR), and low-light cameras, keypoint-guided chest ROI extraction for posture-robust monitoring, and a signal-quality-index (SQI)-based filtering mechanism for reliable respiratory estimation. We implement and evaluate the framework on three robotic platforms spanning quadruped and wheeled locomotion and multiple edge-computing architectures. Experiments conducted across diverse lighting conditions, subject poses, and robot-to-subject distances demonstrate that the framework generalizes across platforms without per-platform algorithmic retuning, while revealing modality-specific operational boundaries. RGB provides the broadest coverage up to 8m, NIR remains effective up to 6m, thermal is reliable only at short range, and low-light sensing supports monitoring in complete darkness up to 8m. Overall, the results demonstrate the feasibility of multimodal contactless RR monitoring on mobile robots and support its use as a foundation for autonomous triage and victim assessment in hazardous search-and-rescue settings.

翻译：呼吸频率（RR）监测是紧急响应、灾难恢复和传染病场景中远程分诊与伤员评估的关键环节，减少物理接触可降低救援人员风险并提升作业安全性。然而，现场部署非接触式RR监测仍面临光照变化、姿态多样性、平台异构性及危险环境中可穿戴传感器不适用等挑战。本文提出一种面向异构移动机器人的模态自适应非接触式RR监测框架，该框架搭载板载边缘计算系统。所提方案融合了跨RGB、热成像、近红外（NIR）及低光照相机的亮度自适应传感器选择策略、基于关键点的胸部ROI提取方法（用于姿态鲁棒监测）以及基于信号质量指数（SQI）的滤波机制（实现可靠呼吸估计）。我们在三种机器人平台（涵盖四足与轮式运动）及多种边缘计算架构上实施并评估该框架。在不同光照条件、受试者姿态及机器人与受试者距离下的实验表明：该框架无需针对各平台进行算法重调即可实现跨平台泛化，同时揭示了模态特定工作边界——RGB在8米范围内覆盖最广，NIR在6米内保持有效，热成像仅短距离可靠，而低光照传感可在完全黑暗环境中支持8米内监测。总体而言，结果验证了移动机器人多模态非接触式RR监测的可行性，并支持将其作为危险搜救场景中自主分诊与伤员评估的基础方案。