Language-Conditioned Safe Trajectory Generation for Spacecraft Rendezvous
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866911634264424448 |
|---|---|
| author | Takubo, Yuji Dwivedi, Arpit Ramkumar, Sukeerth Pabon, Luis A. Gammelli, Daniele Pavone, Marco D'Amico, Simone |
| author_facet | Takubo, Yuji Dwivedi, Arpit Ramkumar, Sukeerth Pabon, Luis A. Gammelli, Daniele Pavone, Marco D'Amico, Simone |
| contents | Reliable real-time trajectory generation is essential for future autonomous spacecraft. While recent progress in nonconvex guidance and control is paving the way for onboard autonomous trajectory optimization, these methods still rely on extensive expert input (e.g., waypoints, constraints, mission timelines, etc.), which limits operational scalability in complex missions such as rendezvous and proximity operations. This paper introduces SAGES (Semantic Autonomous Guidance Engine for Space), a trajectory-generation framework that translates natural-language commands into spacecraft trajectories that reflect high-level intent while respecting nonconvex constraints. Experiments in two settings (fault-tolerant proximity operations with continuous-time constraint enforcement and a free-flying robotic platform) demonstrate that SAGES reliably produces trajectories aligned with human commands, achieving over 90% semantic-behavioral consistency across diverse behavior modes. Ultimately, this work marks an initial step toward language-conditioned, constraint-aware spacecraft trajectory generation, enabling operators to interactively guide both safety and behavior through intuitive natural-language commands with reduced expert burden. Project Website: https://semantic-guidance4space.github.io/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_09111 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Language-Conditioned Safe Trajectory Generation for Spacecraft Rendezvous Takubo, Yuji Dwivedi, Arpit Ramkumar, Sukeerth Pabon, Luis A. Gammelli, Daniele Pavone, Marco D'Amico, Simone Robotics Artificial Intelligence Optimization and Control Reliable real-time trajectory generation is essential for future autonomous spacecraft. While recent progress in nonconvex guidance and control is paving the way for onboard autonomous trajectory optimization, these methods still rely on extensive expert input (e.g., waypoints, constraints, mission timelines, etc.), which limits operational scalability in complex missions such as rendezvous and proximity operations. This paper introduces SAGES (Semantic Autonomous Guidance Engine for Space), a trajectory-generation framework that translates natural-language commands into spacecraft trajectories that reflect high-level intent while respecting nonconvex constraints. Experiments in two settings (fault-tolerant proximity operations with continuous-time constraint enforcement and a free-flying robotic platform) demonstrate that SAGES reliably produces trajectories aligned with human commands, achieving over 90% semantic-behavioral consistency across diverse behavior modes. Ultimately, this work marks an initial step toward language-conditioned, constraint-aware spacecraft trajectory generation, enabling operators to interactively guide both safety and behavior through intuitive natural-language commands with reduced expert burden. Project Website: https://semantic-guidance4space.github.io/ |
| title | Language-Conditioned Safe Trajectory Generation for Spacecraft Rendezvous |
| topic | Robotics Artificial Intelligence Optimization and Control |
| url | https://arxiv.org/abs/2512.09111 |