Safer Trajectory Planning with CBF-guided Diffusion Model for Unmanned Aerial Vehicles

• URL: http://arxiv.org/abs/2604.17527v2
• Date: Sun, 26 Apr 2026 12:06:17 GMT
• Title: Safer Trajectory Planning with CBF-guided Diffusion Model for Unmanned Aerial Vehicles
• Abstract summary: AeroTrajGen is a novel framework for diffusion-based trajectory generation for unmanned aerial vehicles.<n>It incorporates control barrier function (CBF)-guided sampling during inference, specifically designed for UAVs.<n>We show that CBF-guided sampling reduces collision rates by 94.7% compared to unguided diffusion baselines.
• Score: 9.178828168133206
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Safe and agile trajectory planning is essential for autonomous systems, especially during complex aerobatic maneuvers. Motivated by the recent success of diffusion models in generative tasks, this paper introduces AeroTrajGen, a novel framework for diffusion-based trajectory generation that incorporates control barrier function (CBF)-guided sampling during inference, specifically designed for unmanned aerial vehicles (UAVs). The proposed CBF-guided sampling addresses two critical challenges: (1) mitigating the inherent unpredictability and potential safety violations of diffusion models, and (2) reducing reliance on extensively safety-verified training data. During the reverse diffusion process, CBF-based guidance ensures collision-free trajectories by seamlessly integrating safety constraint gradients with the diffusion model's score function. The model features an obstacle-aware diffusion transformer architecture with multi-modal conditioning, including trajectory history, obstacles, maneuver styles, and goal, enabling the generation of smooth, highly agile trajectories across 14 distinct aerobatic maneuvers. Trained on a dataset of 2,000 expert demonstrations, AeroTrajGen is rigorously evaluated in simulation under multi-obstacle environments. Simulation results demonstrate that CBF-guided sampling reduces collision rates by 94.7% compared to unguided diffusion baselines, while preserving trajectory agility and diversity. Our code is open-sourced at https://github.com/RoboticsPolyu/CBF-DMP.

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