Semantic Trajectory Generation for Goal-Oriented Spacecraft Rendezvous

2025-12-09

AI Summary

Trajectory optimization for autonomous spacecraft rendezvous relies heavily on expert-defined waypoints and timelines, limiting scalability and adaptability. Method: This paper proposes a natural language–driven end-to-end trajectory generation framework that tightly couples large language model–based semantic parsing with rigorous continuous-time nonconvex optimal control solvers (GPOPS-II/ACADO), enabling real-time mapping from high-level semantic intent to dynamically feasible and collision-free trajectories. It further introduces real-time constraint propagation and robust verification to ensure dynamical consistency and guaranteed collision avoidance. Results: Validated on proximity operations and free-flying robotic platforms, the framework achieves >90% semantic–behavioral consistency, supports millisecond-scale safe trajectory generation under multimodal natural language instructions, and significantly enhances on-orbit autonomy and intuitive human–machine collaboration.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: Generation
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Large language models for search
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

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 the operational scalability in real rendezvous missions.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.

Problem

• Generates spacecraft trajectories from natural-language commands.
• Reduces reliance on expert input for autonomous rendezvous missions.
• Ensures trajectories respect nonconvex constraints and high-level intent.

Innovation

• Uses natural-language commands to generate spacecraft trajectories
• Translates high-level intent while respecting nonconvex constraints
• Achieves over 90% semantic-behavioral consistency in experiments