Generalizable Spacecraft Trajectory Generation via Multimodal Learning with Transformers

• URL: http://arxiv.org/abs/2410.11723v1
• Date: Tue, 15 Oct 2024 15:55:42 GMT
• Title: Generalizable Spacecraft Trajectory Generation via Multimodal Learning with Transformers
• Authors: Davide Celestini, Amirhossein Afsharrad, Daniele Gammelli, Tommaso Guffanti, Gioele Zardini, Sanjay Lall, Elisa Capello, Simone D'Amico, Marco Pavone,
• Abstract summary: We present a novel trajectory generation framework that generalizes across diverse problem configurations. We leverage high-capacity transformer neural networks capable of learning from data sources. The framework is validated through simulations and experiments on a free-flyer platform.
• Score: 14.176630393074149
• License:
• Abstract: Effective trajectory generation is essential for reliable on-board spacecraft autonomy. Among other approaches, learning-based warm-starting represents an appealing paradigm for solving the trajectory generation problem, effectively combining the benefits of optimization- and data-driven methods. Current approaches for learning-based trajectory generation often focus on fixed, single-scenario environments, where key scene characteristics, such as obstacle positions or final-time requirements, remain constant across problem instances. However, practical trajectory generation requires the scenario to be frequently reconfigured, making the single-scenario approach a potentially impractical solution. To address this challenge, we present a novel trajectory generation framework that generalizes across diverse problem configurations, by leveraging high-capacity transformer neural networks capable of learning from multimodal data sources. Specifically, our approach integrates transformer-based neural network models into the trajectory optimization process, encoding both scene-level information (e.g., obstacle locations, initial and goal states) and trajectory-level constraints (e.g., time bounds, fuel consumption targets) via multimodal representations. The transformer network then generates near-optimal initial guesses for non-convex optimization problems, significantly enhancing convergence speed and performance. The framework is validated through extensive simulations and real-world experiments on a free-flyer platform, achieving up to 30% cost improvement and 80% reduction in infeasible cases with respect to traditional approaches, and demonstrating robust generalization across diverse scenario variations.

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