STORM: Spatial-Temporal Iterative Optimization for Reliable Multicopter Trajectory Generation
Jinhao Zhang, Zhexuan Zhou, Wenlong Xia, Youmin Gong, Jie Mei
Abstract
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 code will be released for community reference. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
BibTeX
@inproceedings{iros2025_stormspatialtemp,
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},
booktitle = {IROS 2025},
year = {2025}
}