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

TL;DR

This paper presents AeroTrajGen, a diffusion-based trajectory planning framework for UAVs that uses control barrier functions to ensure safety and reduce collisions during complex maneuvers.

Contribution

It introduces a CBF-guided diffusion sampling method that enhances safety and agility in UAV trajectory generation, with an obstacle-aware transformer architecture.

Findings

CBF-guided sampling reduces collision rates by 94.7% in simulation.

The model generates smooth, highly agile trajectories across 14 aerobatic maneuvers.

AeroTrajGen is trained on 2,000 expert demonstrations and performs well in multi-obstacle environments.

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.