Reliable human--robot collaboration in emergency scenarios requires autonomous systems that can detect humans, infer navigation goals, and operate safely in dynamic environments. This paper presents HumanDiffusion, a lightweight image-conditioned diffusion planner that generates human-aware navigation trajectories directly from RGB imagery. The system combines YOLO-11--based human detection with diffusion-driven trajectory generation, enabling a quadrotor to approach a target person and deliver medical assistance without relying on prior maps or computationally intensive planning pipelines. Trajectories are predicted in pixel space, ensuring smooth motion and a consistent safety margin around humans. We evaluate HumanDiffusion in simulation and real-world indoor mock-disaster scenarios. On a 300-sample test set, the model achieves a mean squared error of 0.02 in pixel-space trajectory reconstruction. Real-world experiments demonstrate an overall mission success rate of 80% across accident-response and search-and-locate tasks with partial occlusions. These results indicate that human-conditioned diffusion planning offers a practical and robust solution for human-aware UAV navigation in time-critical assistance settings.

翻译：在紧急场景中实现可靠的人机协作，需要自主系统能够检测人类、推断导航目标，并在动态环境中安全运行。本文提出HumanDiffusion，一种轻量级的图像条件扩散规划器，可直接从RGB图像生成具有人类感知的导航轨迹。该系统将基于YOLO-11的人类检测与扩散驱动的轨迹生成相结合，使四旋翼无人机能够接近目标人员并提供医疗援助，而无需依赖先验地图或计算密集型的规划流程。轨迹在像素空间中进行预测，确保了平滑的运动以及在人类周围保持一致的的安全裕度。我们在仿真和真实世界的室内模拟灾难场景中对HumanDiffusion进行了评估。在一个包含300个样本的测试集上，该模型在像素空间轨迹重建中实现了0.02的均方误差。真实世界实验表明，在存在部分遮挡的事故响应和搜索定位任务中，整体任务成功率达到80%。这些结果表明，以人为条件的扩散规划为时间紧迫的援助场景中具有人类感知的无人机导航提供了一个实用且鲁棒的解决方案。