STITCHER: Real-Time Trajectory Planning with Motion Primitive Search
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866918246974750720 |
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| author | Levy, Helene J. Lopez, Brett T. |
| author_facet | Levy, Helene J. Lopez, Brett T. |
| contents | Autonomous high-speed navigation through large, complex environments requires real-time generation of agile trajectories that are dynamically feasible, collision-free, and satisfy constraints. Most modern trajectory planning techniques rely on numerical optimization because high-quality, expressive trajectories that satisfy constraints can be systematically computed. However, strict requirements on computation time and the risk of numerical instability can limit the use of optimization-based planners in safety-critical situations. This work presents an optimization-free planning framework called STITCHER that leverages graph search to generate long-range trajectories by stitching short trajectory segments together in real time. STITCHER is shown to outperform modern optimization-based planners through its innovative planning architecture and several algorithmic developments that make real-time planning possible. Simulation results show safe trajectories through complex environments can be generated in milliseconds that cover tens of meters. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_21180 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | STITCHER: Real-Time Trajectory Planning with Motion Primitive Search Levy, Helene J. Lopez, Brett T. Robotics Autonomous high-speed navigation through large, complex environments requires real-time generation of agile trajectories that are dynamically feasible, collision-free, and satisfy constraints. Most modern trajectory planning techniques rely on numerical optimization because high-quality, expressive trajectories that satisfy constraints can be systematically computed. However, strict requirements on computation time and the risk of numerical instability can limit the use of optimization-based planners in safety-critical situations. This work presents an optimization-free planning framework called STITCHER that leverages graph search to generate long-range trajectories by stitching short trajectory segments together in real time. STITCHER is shown to outperform modern optimization-based planners through its innovative planning architecture and several algorithmic developments that make real-time planning possible. Simulation results show safe trajectories through complex environments can be generated in milliseconds that cover tens of meters. |
| title | STITCHER: Real-Time Trajectory Planning with Motion Primitive Search |
| topic | Robotics |
| url | https://arxiv.org/abs/2412.21180 |