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The paper introduces HumanDiffusion, an image-conditioned diffusion-based trajectory planner for UAVs in search and rescue, enabling navigation towards humans detected via YOLO-V3. By predicting trajectories directly in pixel space from RGB images, the system avoids reliance on pre-existing maps or computationally expensive planning. Experiments in simulation and real-world scenarios demonstrate a mean squared error of 0.02 in pixel-space trajectory reconstruction and an 80% mission success rate, highlighting the method's effectiveness for human-aware navigation.
Ditch the maps and heavy planning pipelines: HumanDiffusion lets UAVs navigate directly to humans in need using only a camera and a lightweight diffusion model.
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.
