STORM: Spatial-Temporal Iterative Optimization for Reliable Multicopter Trajectory Generation
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866915183032532992 |
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| author | Zhang, Jinhao Zhou, Zhexuan Xia, Wenlong Gong, Youmin Mei, Jie |
| author_facet | Zhang, Jinhao Zhou, Zhexuan Xia, Wenlong Gong, Youmin Mei, Jie |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_03252 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | STORM: Spatial-Temporal Iterative Optimization for Reliable Multicopter Trajectory Generation Zhang, Jinhao Zhou, Zhexuan Xia, Wenlong Gong, Youmin Mei, Jie Robotics 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 |
| title | STORM: Spatial-Temporal Iterative Optimization for Reliable Multicopter Trajectory Generation |
| topic | Robotics |
| url | https://arxiv.org/abs/2503.03252 |