STORM: Spatial-Temporal Iterative Optimization for Reliable Multicopter Trajectory Generation

Efficient and safe trajectory planning plays a critical role in the application of quadrotor unmanned aerial vehicles. Currently, the inherent trade-off between constraint compliance and computational efficiency enhancement in UAV trajectory optimization problems has not been sufficiently addressed. To enhance the performance of UAV trajectory optimization, we propose a spatial-temporal iterative optimization framework. Firstly, B-splines are utilized to represent UAV trajectories, with rigorous safety assurance achieved through strict enforcement of constraints on control points. Subsequently, a set of QP-LP subproblems via spatial-temporal decoupling and constraint linearization is derived. Finally, an iterative optimization strategy incorporating guidance gradients is employed to obtain high-performance UAV trajectories in different scenarios. Both simulation and real-world experimental results validate the efficiency and high-performance of the proposed optimization framework in generating safe and fast trajectories. Our source codes will be released for community reference atthis https URL

@article{zhang2025_2503.03252,
  title={ STORM: Spatial-Temporal Iterative Optimization for Reliable Multicopter Trajectory Generation },
  author={ Jinhao Zhang and Zhexuan Zhou and Wenlong Xia and Youmin Gong and Jie Mei },
  journal={arXiv preprint arXiv:2503.03252},
  year={ 2025 }
}