Agile Tradespace Exploration for Space Rendezvous Mission Design via Transformers

2025-10-03

AI Summary

Trajectory optimization for orbital rendezvous involves challenging trade-offs between control effort and flight time, compounded by non-convex constraints that hinder computational efficiency. Method: This paper introduces the first Transformer-based architecture tailored for multi-objective trajectory trade-off analysis. Given boundary conditions and orbital parameters, it performs a single forward pass to generate a near-Pareto-optimal trajectory set in parallel, balancing accuracy and speed. The framework accommodates variable flight times, perturbed dynamics, and complex constraints—including opportunity constraints and passive safety—while incorporating verification-driven refinement to enhance feasibility. Results: Experiments across multiple rendezvous scenarios demonstrate that the method efficiently approximates the Pareto front, achieving inference speed comparable to convex relaxation approaches. Moreover, it delivers high-quality initial guesses that significantly reduce the number of iterations required by downstream numerical optimizers, thereby improving mission design agility and generalization capability.

Technology Category

• Constraint Satisfaction and Optimization: Constraint Optimization
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Spacecraft rendezvous enables on-orbit servicing, debris removal, and crewed docking, forming the foundation for a scalable space economy. Designing such missions requires rapid exploration of the tradespace between control cost and flight time across multiple candidate targets. However, multi-objective optimization in this setting is challenging, as the underlying constraints are often highly nonconvex, and mission designers must balance accuracy (e.g., solving the full problem) with efficiency (e.g., convex relaxations), slowing iteration and limiting design agility. To address these challenges, this paper proposes an AI-powered framework that enables agile mission design for a wide range of Earth orbit rendezvous scenarios. Given the orbital information of the target spacecraft, boundary conditions, and a range of flight times, this work proposes a Transformer-based architecture that generates, in a single parallelized inference step, a set of near-Pareto optimal trajectories across varying flight times, thereby enabling rapid mission trade studies. The model is further extended to accommodate variable flight times and perturbed orbital dynamics, supporting realistic multi-objective trade-offs. Validation on chance-constrained rendezvous problems with passive safety constraints demonstrates that the model generalizes across both flight times and dynamics, consistently providing high-quality initial guesses that converge to superior solutions in fewer iterations. Moreover, the framework efficiently approximates the Pareto front, achieving runtimes comparable to convex relaxation by exploiting parallelized inference. Together, these results position the proposed framework as a practical surrogate for nonconvex trajectory generation and mark an important step toward AI-driven trajectory design for accelerating preliminary mission planning in real-world rendezvous applications.

Problem

• Optimizing spacecraft rendezvous trajectories across flight time and control cost
• Addressing nonconvex constraints in multi-objective mission design trade-offs
• Enabling rapid Pareto front approximation for agile space mission planning

Innovation

• Transformer-based architecture generates near-Pareto optimal trajectories
• Model accommodates variable flight times and perturbed orbital dynamics
• Parallelized inference enables rapid mission trade studies efficiently