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

Jinhao Zhang, Zhexuan Zhou, Wenlong Xia, Youmin Gong, Jie Mei

TL;DR

The paper addresses the challenge of real-time, safe UAV trajectory optimization by balancing strict feasibility with computational efficiency. It introduces STORM, a spatial-temporal iterative framework that parameterizes trajectories with non-uniform B-splines $C(t)$ and enforces safety via convex-hull properties inside Safe Flight Corridors $\mathcal{F}$, while decoupling spatial and temporal optimization into QP-LP subproblems. Guided by gradient information, an iterative scheme coordinates updates to yield high-performance, collision-free trajectories, achieving real-time performance (≈50 Hz) on standard hardware and outperforming state-of-the-art baselines like MINCO in both safety adherence and temporal efficiency. Extensive simulations and real-world experiments demonstrate improved constraint satisfaction and shorter flight times, validating STORM's practical impact for reliable multicopter trajectory generation.

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 codes will be released for community reference at https://hitsz-mas.github.io/STORM

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

TL;DR

The paper addresses the challenge of real-time, safe UAV trajectory optimization by balancing strict feasibility with computational efficiency. It introduces STORM, a spatial-temporal iterative framework that parameterizes trajectories with non-uniform B-splines and enforces safety via convex-hull properties inside Safe Flight Corridors , while decoupling spatial and temporal optimization into QP-LP subproblems. Guided by gradient information, an iterative scheme coordinates updates to yield high-performance, collision-free trajectories, achieving real-time performance (≈50 Hz) on standard hardware and outperforming state-of-the-art baselines like MINCO in both safety adherence and temporal efficiency. Extensive simulations and real-world experiments demonstrate improved constraint satisfaction and shorter flight times, validating STORM's practical impact for reliable multicopter trajectory generation.

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 codes will be released for community reference at https://hitsz-mas.github.io/STORM

Paper Structure

This paper contains 17 sections, 1 theorem, 30 equations, 7 figures, 2 tables, 2 algorithms.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORKS
  3. PROBLEM FORMULATION
  4. Spatial-Temporal Iterative Optimization
  5. Matrix Representations for Non-Uniform B-Splines
  6. Spatial-Temporal Decoupling
  7. Constraint Handling
  8. Spatial Constraints
  9. Temporal Constraints
  10. Guidance Gradient
  11. Iterative Optimization Framework
  12. EXPERIMENTS
  13. Simulation Experiment Setup
  14. Ablation Study
  15. Trajectory Performance
  16. ...and 2 more sections

Key Result

Proposition 1

Let $\mathbf{M}^1_i = $, then the basic matrix can be recursively calculated as follows where $d^0_j=\frac{t_i-t_j}{t_{j+p}-t_j}$ and $d^1_j=\frac{t_{i+1}-t_i}{t_{j+p}-t_j}$, with the convention $0/0=0$.

Figures (7)

  • Figure 1: Real UAV Flight and Simulation
  • Figure 2: The trajectory generation procedure of proposed method. The optimizer acquires initial control points and safe flight corridor constraints from front-end path searching results, derives dynamic constraints from the UAV dynamic model, and iteratively generates non-uniform B-spline trajectory.
  • Figure 3: Ablation Experiment Results
  • Figure 4: Effect of Guidance Gradient
  • Figure 5: Trajectory Comparision
  • ...and 2 more figures

Theorems & Definitions (1)

  • Proposition 1