HumanDiffusion: A Vision-Based Diffusion Trajectory Planner with Human-Conditioned Goals for Search and Rescue UAV
Faryal Batool, Iana Zhura, Valerii Serpiva, Roohan Ahmed Khan, Ivan Valuev, Issatay Tokmurziyev, Dzmitry Tsetserukou

TL;DR
HumanDiffusion introduces a novel image-based diffusion planning method enabling UAVs to generate human-aware navigation trajectories directly from RGB images, improving safety and efficiency in emergency search and rescue operations.
Contribution
The paper presents HumanDiffusion, a lightweight diffusion-based trajectory planner that integrates human detection with direct RGB image conditioning for UAV navigation in rescue scenarios.
Findings
Achieves 0.02 pixel-space trajectory reconstruction error.
Demonstrates 80% success rate in real-world rescue tasks.
Operates without prior maps or heavy computation.
Abstract
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…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · Robotic Path Planning Algorithms · UAV Applications and Optimization
