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

2025-03-05

AI Summary

To address the challenge of simultaneously satisfying hard constraints and ensuring real-time performance in quadrotor trajectory optimization, this paper proposes a spatial-temporal decoupled iterative optimization framework. Methodologically, trajectories are represented using B-splines, and a novel control-point-level strict constraint enforcement mechanism is introduced; a guidance-gradient-driven alternating QP-LP solving strategy, combined with constraint linearization, ensures efficient convergence. The key contribution lies in breaking the inherent trade-off between safety and computational efficiency: the method achieves millisecond-scale generation of safe, high-speed trajectories in both simulation and real-world flight experiments—significantly outperforming state-of-the-art approaches (e.g., CHOMP, TrajOpt). The implementation is open-sourced to facilitate reproducibility.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Constraint Optimization
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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

Problem

• Addresses trade-off between constraint compliance and computational efficiency in UAV trajectory optimization.
• Proposes a spatial-temporal iterative optimization framework for UAV trajectory generation.
• Ensures safety and efficiency in trajectory planning through B-splines and iterative optimization.

Innovation

• B-splines for UAV trajectory representation
• QP-LP subproblems via spatial-temporal decoupling
• Iterative optimization with guidance gradients