Compositional Diffusion Models for Powered Descent Trajectory Generation with Flexible Constraints

• URL: http://arxiv.org/abs/2410.04261v1
• Date: Sat, 5 Oct 2024 18:47:50 GMT
• Title: Compositional Diffusion Models for Powered Descent Trajectory Generation with Flexible Constraints
• Authors: Julia Briden, Yilun Du, Enrico M. Zucchelli, Richard Linares,
• Abstract summary: TrajDiffuser is a compositional diffusion-based flexible and concurrent trajectory generator. It learns the multi-modal distributions of a dataset of simulated optimal trajectories. During inference, the trajectory is generated simultaneously over time, providing stable long-horizon planning.
• Score: 24.530914372991273
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work introduces TrajDiffuser, a compositional diffusion-based flexible and concurrent trajectory generator for 6 degrees of freedom powered descent guidance. TrajDiffuser is a statistical model that learns the multi-modal distributions of a dataset of simulated optimal trajectories, each subject to only one or few constraints that may vary for different trajectories. During inference, the trajectory is generated simultaneously over time, providing stable long-horizon planning, and constraints can be composed together, increasing the model's generalizability and decreasing the training data required. The generated trajectory is then used to initialize an optimizer, increasing its robustness and speed.

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