Spacecraft Rendezvous Trajectory Generation with Modular Constraints via Diffusion Model Composition

2026-10-05

AI Summary

This study addresses the challenge of flexibly accommodating multiple mission constraints in spacecraft rendezvous trajectory design by proposing a compositional generative framework based on energy-based diffusion models. The method decouples diverse mission constraints into independent energy models and achieves modular configuration through analytical energy field fusion and model composition techniques, enabling dynamic reconfiguration of novel constraint combinations without retraining. Experimental results demonstrate that the proposed framework attains constraint satisfaction rates in multi-constraint scenarios comparable to those of single-constraint specialized models, effectively validating the feasibility and efficiency of the modular design. This work provides a flexible, retraining-free solution for trajectory planning under complex constraints.

Abstract

Emerging mission classes such as on-orbit servicing, satellite inspection, and active debris removal require trajectory design methods that are adaptable to a variety of mission scenarios. We present a diffusion-based trajectory generation approach for rendezvous and proximity operations (RPO) that enables flexible configuration of mission constraints. First, individual energy-based diffusion models are trained to satisfy distinct constraints such as approach cone and sensor line-of-sight from a set of optimized trajectories. Then, at inference time, the learned energy models can be composed with one another, or with an analytically defined energy field, to enforce specific constraint combinations. We validate this framework with the composition of a learned approach cone model and a learned sensor line-of-sight model, as well as a learned approach cone model and synthetic obstacle avoidance model, both of which yield constraint satisfaction rates that are within 1 percentage point of the single-constraint models or higher. These results indicate that our compositional diffusion framework can provide a modular approach to RPO trajectory design and enable reconfiguration for new constraint combinations without requiring model retraining.

Problem

• Spacecraft Rendezvous
• Trajectory Generation
• Modular Constraints
• Rendezvous and Proximity Operations
• Constraint Composition

Innovation

• Diffusion Model Composition
• Trajectory Generation
• Modular Constraints
• Spacecraft Rendezvous
• Energy-based Models