AeroDiffusion: Real-Time Quadrotor Trajectory Planning in Dynamic Environments via Conditional Diffusion

Abstract

In this paper, we propose AeroDiffusion, a real-time closed-loop diffusion planning framework for quadrotor navigation in dynamic environments. The core idea is to turn conditional diffusion from a single-shot trajectory generator into an onboard receding-horizon planner: temporal depth-state conditioning models multi-modal future 3D trajectories, preference-guided batch selection converts these modes into an executable safe trajectory, and warm-start denoising reuses the previous plan to meet real-time replanning latency. This design allows the planner to react to moving obstacles from onboard perception while maintaining temporally consistent commands. Together, the simulation and indoor flight experiments show effective obstacle avoidance and real-time onboard replanning in the tested scenarios.