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 at https://hitsz-mas.github.io/STORM

翻译：高效安全的轨迹规划在四旋翼无人机应用中具有关键作用。当前，无人机轨迹优化问题中约束满足与计算效率提升之间的固有权衡尚未得到充分解决。为提升无人机轨迹优化性能，本文提出一种空间-时间迭代优化框架。首先，采用B样条曲线表征无人机轨迹，通过对控制点施加严格约束实现可靠的安全保障。随后，通过时空解耦与约束线性化推导出一组QP-LP子问题。最后，采用融合引导梯度的迭代优化策略，在不同场景中获得高性能无人机轨迹。仿真与实物实验结果共同验证了所提优化框架在生成安全快速轨迹方面的高效性与优越性能。我们的源代码将通过https://hitsz-mas.github.io/STORM 开源供学术界参考。