用时空迭代优化生成安全快速的无人机轨迹
STORM: Spatial-Temporal Iterative Optimization for Reliable Multicopter Trajectory Generation
- 用B样条表示轨迹，通过控制点约束保证安全
- 分解为二次规划与线性规划子问题，提升计算效率
- 支持多场景应用，适合实时无人机路径规划
高效且安全的轨迹规划在四旋翼无人机应用中至关重要。当前无人机轨迹优化中约束满足与计算效率之间的固有权衡尚未得到充分解决。为此，我们提出一种时空迭代优化框架：首先使用B样条表示无人机轨迹，并通过严格约束控制点确保安全性；随后通过时空解耦与约束线性化，推导出一组二次规划-线性规划（QP-LP）子问题；最后采用包含引导梯度的迭代优化策略，在不同场景下生成高性能轨迹。仿真与真实实验结果验证了该框架在生成安全、快速轨迹方面的高效性与优越性能。源码将公开于 https://hitsz-mas.github.io/STORM。
原文摘要 · Abstract (English)
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
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